Handbook of Quality of Life Research: Place and Space Perspectives 1789908787, 9781789908787

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HANDBOOK OF QUALITY OF LIFE RESEARCH

Handbook of Quality of Life Research Place and Space Perspectives

Edited by

Robert W. Marans Research Professor Emeritus, Institute for Social Research and Professor Emeritus of Architecture and Urban Planning, University of Michigan, USA

Robert J. Stimson Emeritus Professor of Geography, University of Queensland, Australia and Honorary Professorial Fellow of Geography, University of Melbourne, Australia

Noah J. Webster Associate Research Scientist, Institute for Social Research, University of Michigan, USA

Cheltenham, UK • Northampton, MA, USA

© Robert W. Marans, Robert J. Stimson and Noah J. Webster 2024

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA A catalogue record for this book is available from the British Library Library of Congress Control Number: 2023952162 This book is available electronically in the Geography, Planning and Tourism subject collection http://dx.doi.org/10.4337/9781789908794

ISBN 978 1 78990 878 7 (cased) ISBN 978 1 78990 879 4 (eBook)

EEP BoX

Contents

List of contributorsviii Prefacexix PART I

QUALITY OF LIFE: A UNIVERSAL PERSPECTIVE

1

Complexity and diversity of quality of life-related research Robert J. Stimson, Robert W. Marans and Noah J. Webster

2

Overview of approaches to measurement and empirical analysis of quality of life and well-being Robert J. Stimson, Robert W. Marans and Noah J. Webster

3

Investigating happiness: a socio-spatial inequalities perspective Thanasis Ziogas and Dimitris Ballas

4

Understanding environmental impacts on people’s quality of life via environmental psychology: three basic principles Marino Bonaiuto and Valeria Chiozza

PART II

2

13 26

45

QUALITY OF LIFE: MEASUREMENT APPROACHES

5

Objective approaches to investigating and measuring quality of life and well-being61 Robert J. Stimson

6

Investigating subjective quality of life: using survey research methods Robert J. Stimson and Robert W. Marans

79

7

Integrating subjective and objective measures in quality of life research Robert J. Stimson, Rod McCrea, Robert W. Marans and Noah J. Webster

94

8

Assessing alternative air quality measures and their impact on quality of life: the case of Hong Kong Poh-Chin Lai, Chien-Tat Low, Si Chen, Robert J. Stimson, Ester Cerin, Wei Cheng, Jimmy Fung and Paulina Pui-Yun Wong

9

How neighbourhood social and built environments influence social interactions: differences between life stages 128 Piret Veeroja, Greg Foliente, Rod McCrea, Hannah Badland, Chris Pettit and Jennifer Day

v

111

vi  Handbook of quality of life research PART III QUALITY OF LIFE IN METROPOLITAN AREAS, CITIES AND NEIGHBOURHOODS 10

Quality of life in large-scale, big-city urban environments: a world perspective 147 Robert W. Marans and Robert J. Stimson

11

Pathways from compact city to subjective well-being: evidence from Oslo, Norway Kostas Mouratidis

12

The role of neighbourhoods in quality of life: toward a comprehensive model Robert Gifford, Leila Scannell, Christine Kormos, Jessica Rourke and Amanda J. McIntyre

182

13

Exploring quality of life in new towns: an overview Robert W. Marans and Noah J. Webster

199

14

The influence of urban layout on perceived residential quality in a Costa Rican suburb Helga von-Breymann and Esteban Montenegro-Montenegro

165

214

PART IV QUALITY OF LIFE IN SMALL TOWNS, RURAL AREAS AND MIGRATION COMMUNITIES 15

Quality of life in small towns: a mid-American case study Rodrigo F. Cantarero and James J. Potter

16

Subjective community well-being and resilience in a rural region experiencing rapid change Rod McCrea, Rosemary Leonard and Andrea Walton

244

17

Quality of life, amenities and recent migration across the largest US metropolitan areas Gordon F. Mulligan

258

PART V

230

PUBLIC SPACES, QUALITY OF LIFE AND THE ENVIRONMENT

18

Residential neighbourhoods, nearby nature and quality of life Sara Hadavi

275

19

Effects of environmental degradation on neighbourhood satisfaction and quality of life Byoung-Suk Kweon and Christopher D. Ellis

294

20

Nature-based solutions and quality of life: protecting, restoring and building human capacities Marino Bonaiuto and Susana Alves

308

Contents  vii PART VI AGEING, PLACE AND QUALITY OF LIFE 21

The importance of place-making for quality of life in later life: contrasting home and hospital environments Hans-Werner Wahl, Julia Kirch, Kathrin Büter and Gesine Marquardt

22

Quality of life of older adults in continuing care retirement communities Liat Ayalon

23

How urban environments affect quality of life in older socio-demographic groups: the role of physical activity behaviour Casper J.P. Zhang, Anthony Barnett, Wai-Kit Ming, Poh-Chin Lai, Ruby S.Y. Lee and Ester Cerin

24

Neighbourhood characteristics linked to quality of life among vulnerable older adults Noah J. Webster and Toni C. Antonucci

372

25

Conceptualising contextual variation in older adults’ quality of life cross-nationally: challenges and opportunities Christine A. Mair

387

323 339

355

PART VII A LOOK AHEAD 26

Overview and future directions Robert W. Marans, Robert J. Stimson and Noah J. Webster

403

Index408

Contributors

Susana Alves is an environmental psychologist in the Department of Psychology of Developmental and Socialisation Processes, Sapienza University of Rome, Italy. She teaches and conducts research in environmental psychology. Her research examines how natural landscapes can promote health and psychological well-being, with a focus on diverse groups of people, including older adults, migrants and urban residents under stress. She has also addressed quality-of-life (QOL) issues in neighbourhood outdoor spaces as well as in institutional settings for older individuals, assessing people’s landscape perceptions. Her recent research is on the social and environmental aspects of risk behaviours related to natural disasters, the exploration of historic urban landscapes and the examination of atmospheres in architecture. Toni C. Antonucci is the Elizabeth M. Douvan Collegiate Professor of Psychology and programme director and research professor in the Life Course Development Program in the Institute for Social Research at the University of Michigan, USA. Her research focuses on social relations and health across the lifespan, including the family, lifespan and life course development, multigenerational relations, adult development and ageing, and comparative studies of social relations and health in the United States, Europe, the Middle East and Japan. She is particularly interested in how social relations optimise or jeopardise an individual’s ability to face life’s challenges across multiple contexts. She is past editor of the Journals of Gerontology: Psychological Sciences, past associate editor of the journal Developmental Psychology and past president of the Gerontological Society of America, International Society for the Study of Behavioural Development and Society for the Study of Human Development. Liat Ayalon is a professor in the School of Social Work, at Bar-Ilan University, Israel. She coordinated an international EU-funded PhD programme on ageism. She was involved in the EU-funded MascAge programme studying ageing masculinities in literature and cinema. Between 2014 and 2018, she led an international research network on ageism funded through European Cooperation in Science and Technology (COST). She is an executive board member of the International Psychogeriatric Association and Co-Secretary of the NGO Committee on Ageing in Geneva. She was elected by the UN Decade of Health Ageing as one of 50 world leaders transforming the world to be a better place to grow older. Hannah Badland is a Professor and Director of the Social Equity Research Centre in the School of Global, Urban and Social Studies and a Vice-Chancellor’s Research Fellow at RMIT University, Australia. Her research examines how the built environment is related to health, well-being and inequities in both adults and children, using interdisciplinary approaches, mixed methods and engagement with end users. Currently, she is working on enhancing the social determinants of health for young adults with a disability, and on reducing inequities in early childhood development. She has published over 160 research articles in leading interdisciplinary journals, receiving more than $20 million in competitive research funding. She is the recipient of more than 30 awards and prizes. She is the Associate Editor of Health & Place and in 2024 will commence an Australian Research Council Future Fellowship. viii

Contributors  ix Dimitris Ballas is a professor (chair in Economic Geography) at the University of Groningen, the Netherlands. He previously worked as an associate professor at the University of the Aegean, Greece, and as a senior lecturer in the Department of Geography at the University of Sheffield, UK. He has held visiting research scholar positions at the University of Cambridge, Harvard University, the International Institute for Applied Systems Analysis (Austria) and Ritsumeikan University (Japan). He publishes widely in the fields of social and economic geography, social and spatial inequalities, the geography of happiness, regional science, and geoinformatics in the social sciences. Anthony Barnett is an associate professor in the Behaviour, Environment and Cognition Research Program at the Mary MacKillop Institute for Health Research in the Australian Catholic University, Melbourne. His background is in exercise physiology, having held lecturing positions at Deakin University (Australia), the University of Hong Kong, the University of Melbourne and Nottingham Trent University, UK. His recent research is on the influence of the neighbourhood environment on active living, metabolic health, quality of life and cognitive health across age groups. He is a co-founder of the International Cognitive Health and the Environment Network and member of the International Society for Urban Health. Marino Bonaiuto is a professor in the Department of Psychology of Developmental and Socialisation Processes, Sapienza University of Rome, Italy, teaching courses on the Psychology of Organisational Communication, Environmental Psychology, and Cognitive Ergonomics and Psychology. He is a member of Sapienza’s Technical-Scientific Committee for Sustainability. As a chartered psychologist at the Italian Association of Professional Psychologists, he was nominated as one of the three academic members of the national working group on ‘Environment, Territory and Tourism’ for the Consiglio Nazionale dell’Ordine degli Psicologi. He is the president-elect of Division #4 Environmental Psychology within the International Association of Applied Psychology (IAAP). His research focuses on positive impacts that the environment can exert on people, and the crucial impact that people have on the environment. His applied interests are in spreading evidence-based knowledge derived from environmental psychology to other disciplines working with places and people. Kathrin Büter is a project manager in architectural programming and project development at Medizinische Hochschule Hannover, Germany. She was a research associate and postdoctoral candidate at the Chair of Social and Health Care Buildings in the Faculty of Architecture at Technische Universität (TU) Dresden, Germany. Her research and design work focus on architecture for people living with dementia. In 2017, she completed a doctoral thesis at TU Dresden on ‘Dementia-friendly Acute Care Hospitals’. She holds a Master’s in Interior Design from the University of Applied Sciences and Arts, Hannover. Rodrigo F. Cantarero is associate professor emeritus in the Community and Regional Planning programme at the University of Nebraska-Lincoln, USA. He served as faculty affiliate at the Nebraska Center for Research on Children, Youth, Families and Schools and the Latino/Latin American Studies Program at the University of Nebraska-Lincoln. His research focuses on quality of life in small Midwestern towns in the US affected by large immigrant populations. He has also conducted research on minority health in the state of Nebraska. His research incorporates geographic information systems (GIS).

x  Handbook of quality of life research Ester Cerin, a psychologist and statistician, is director of the Behaviour, Environment and Cognition Research Program at the Mary MacKillop Institute for Health Research at the Australian Catholic University, Melbourne, and is an honorary professor in the School of Public Health at the University of Hong Kong and a professor in the Department of Community Medicine at The Arctic University of Norway (UiT). Her research focuses on the environmental and lifestyle determinants of health and lifestyle behaviours across the lifespan and diverse geographical regions. She was a recipient of an Australian Research Council Future Fellowship in urban planning and active ageing. She was associate editor of Health & Place, deputy editor-in-chief of the International Journal of Behavioral Nutrition and Physical Activity, and is president of the International Society of Behavioral Nutrition and Physical Activity. She is founder of the International Cognitive Health and the Environment Network, leading international research projects on the effects of the built, natural and social environments on cognitive health in mid-aged and older adults. She has published extensively in prestigious journals and was a Clarivate Highly Cited Researcher in 2019, 2021, 2022 and 2023. Si Chen is an environmental health geographer. She is a teaching fellow in the Department of Land Surveying and Geo-Informatics at The Hong Kong Polytechnic University, specialising in urban greenery, air pollution, geographic information systems (GIS) and environmental health. Her research focuses on plant ecology, functions of vegetation in urbanised areas, and mobile GIS geospatial technologies. She is a certified arborist of the International Society of Arboriculture and a member of the Hong Kong Geographic Information System Association. Wei Cheng, an applied geographer, is a spatial consultant with an international design, engineering and advisory company. Her areas of specialisation include spatial analysis, geographic information systems (GIS) modelling, visualisation, building information modelling– geographic information system (BIM–GIS) integration, and web GIS solutions. She conducts research focusing on spatial-temporal analysis and dynamic visualisation of urban environmental data using GIS and remote sensing methods. She was involved in the research project ‘HKD3D: A Dynamic 3D Air Pollution Exposure Model for Hong Kong’ funded by the US Health Effects Institute during her PhD studies, developing dynamic 2D and 3D visualisations of air pollution dispersion in street canyons for the project. She is a member of the Hong Kong Geographic Information System Association (HKGISA). Valeria Chiozza is a PhD student in Social Psychology, Developmental Psychology and Educational Research in the Department of Psychology of Developmental and Socialisation Processes, Sapienza University of Rome, Italy. She has a BA in Psychology and Social Processes and a BSc in Behavioural Genetics. She works on environmental psychology, with her PhD work focusing on the determinants of pro-environmental behaviour regarding aetiological factors and intergenerational transmission. Her research includes the analysis of individual–environment interactions, both in terms of how physical and social characteristics influence behavioural, cognitive and affective processes, and the study of environmental risk factors for human health. Jennifer Day works on issues of forced displacement and eviction, economic development and urbanisation across Asia and the Pacific. She holds a PhD in City and Regional Planning from the University of California, Berkeley, and a Master’s in Civil Engineering from San José State University, USA. She has undertaken research in China and Vanuatu. She works closely

Contributors  xi with grassroots community movements. Her research has been funded by the Australian Research Council. She is a National Geographic explorer. Christopher D. Ellis is a professor and landscape architect in the Department of Plant Science and Landscape Architecture at the University of Maryland, USA. His research involves the design and measurement of high-performance landscapes, addressing environmental concerns and human well-being. He held previous tenured appointments at the University of Michigan and Texas A&M University. He is a member of the Council of Educators in Landscape Architecture (CELA) Academy of Fellows and former president of CELA. Greg Foliente is an enterprise professor at the University of Melbourne, Australia, and president and board chair of the non-profit International Initiative for a Sustainable Built Environment. His expertise revolves around the performance concept and the integration of modelling and engineering domain knowledge with developments in the social, public health and environmental sciences, and emerging digital and geospatial technologies to improve the safety, sustainability and resilience of built environments and urban communities at various scales. He is interested in anthro-complexity and the roles of different stakeholders and actors in responding to societal challenges, and to change and ‘shocks’ under severe uncertainties. He uses different types of data – quantitative and qualitative, objective and subjective data, big data and rich data – to better understand those problems. He received the James R. Croes Medal from the American Society of Civil Engineers (ASCE). He has a Scopus publications record ranked in the top 1 per cent among civil engineering researchers worldwide. Jimmy Fung is a chair professor at the Division of Environment and Sustainability and the Department of Mathematics at Hong Kong University of Science and Technology. He specialises in air quality modelling, applying remote sensing technology investigating air quality research in Hong Kong and the Pearl River Delta (PRD) Region. His current research is on the comprehension, prediction and assessment of meteorological and air pollution problems associated with urban and coastal environments. This mesoscale modelling system is used in educational and research programmes, including the study of wind fields associated with the large-scale monsoon circulation, pre-summer severe rainstorms, typhoons, and the regional thermally forced land–sea breeze circulation, the local topographically forced circulation, and impact of regional urban built form. He has been applying advanced atmospheric chemistry in studying air quality over Hong Kong and PRD regions down to street scales. Robert Gifford is an environmental psychologist and Professor of Psychology and Environmental Studies at the University of Victoria, Canada. He is a fellow of the Royal Society of Canada, the American Psychological Association, the Canadian Psychological Association, the Association for Psychological Science and the International Association of Applied Psychology. He has received a Career Award from the Environmental Design Research Association. He is the author of 150 refereed publications and book chapters, five editions of Environmental Psychology: Principles and Practice and an edited book titled Research Methods for Environmental Psychology. He was chief editor of the Journal of Environmental Psychology for 14 years, and has been president of the Environmental Psychology division of the International Association of Applied Psychology, the American Psychological Association’s Population and Environment Division and the Canadian Psychological Association’s environmental psychology section.

xii  Handbook of quality of life research Sara Hadavi is Assistant Professor of Landscape Architecture in the College of Architecture, Planning and Design at Kansas State University, USA. She has an MLA degree from the University of Illinois and a PhD in landscape architecture from the University of Michigan. Her research focuses on translational planning and design of urban landscapes through interdisciplinary work, linking environmental psychology, planning and design. Her teaching and research emphasize people’s needs, preferences and well-being with the goal of creating restorative environments. She also explores green infrastructure and vacant lot greening approaches in disinvested areas to improve socio-environmental equity and quality of life for vulnerable communities. She is the Landscape Architecture Foundation Olmsted Scholar. Julia Kirch is a postdoc at the Frankfurt University of Applied Sciences and a research assistant at the architectural office a|sh sander.hofrichter architekten, Germany, which specializes in healthcare buildings. She holds a Master’s in Architecture and Interior Design from the Faculty of Health at the University of Witten/Herdecke and a Diploma in Architecture and Interior Design from OWL University of Applied Sciences and Arts, Germany. Her doctorate from the Faculty of Architecture at the Technische Universität in Dresden focused on dementia-sensitive hospital architecture. She was the recipient of a scholarship from the graduate programme ‘People with Dementia in Hospitals’ at Heidelberg University. Christine Kormos is a behavioural scientist with experience working within the public sector and as a private consultant to develop, implement and evaluate behavioural interventions. She conducts projects seeking behavioural insights to create behaviour change interventions related to sustainable transportation and beyond. She holds a Master’s and PhD in Applied Social and Environmental Psychology from the University of Victoria, Canada, and a BSc in Biology and Psychology from Queen’s University, Canada. In addition to authoring academic papers and book chapters, she has also worked for government agencies and as a behavioural economics consultant on sustainability-related research projects. Byoung-Suk Kweon is a professor in the Department of Plant Science and Landscape Architecture at the University of Maryland, USA, and a licensed landscape architect. Her research interests include environmental behaviour, landscape performance, environmental justice, urban agriculture, active transportation and landscape architecture. She received a national award of recognition for her excellence in teaching, research and service from the Council of Educators in Landscape Architecture (CELA). She is the author of numerous articles, book chapters and reports, and was recognised in 2020 as one of the ten most cited landscape architecture faculty members in the US. Poh-Chin Lai is a geographic information scientist, an honorary professor in the Department of Geography at the University of Hong Kong and was an honorary deputy director of the Geographical/Land Information System Research Centre. Her work is grounded in theories and methods in geospatial science, focusing on environmental and social determinants of population health, investigating associations between physical urban geography and environmental/health risks. She has been a technical consultant to public and government agencies in the US, and to the Thailand Ministry of Health, WHO and UNICEF. She is a fellow member of the Joint Laboratory for GeoInformation Science; a former council member of the Hong Kong Geographic Information System Association, and the Hong Kong Society of Photogrammetry and Remote Sensing; and a former member of the Chinese Professionals in Geographic Information Sciences. She served on the working group promoting the use of disease model-

Contributors  xiii ling for the Scientific Committee on Advanced Data Analysis and Disease Modelling of the Centre for Health Protection. Ruby S.Y. Lee is the consultant family physician of the Elderly Health Service, Department of Health, Hong Kong. She is a fellow of the Hong Kong College of Family Physicians, the Royal Australian College of General Practitioners, the Hong Kong Academy of Medicine (Family Medicine), and honorary clinical associate professor in the Department of Family Medicine and Primary Care, and School of Public Health and the Department of Community Medicine, University of Hong Kong. She was president of the Hong Kong College of Family Physicians and chair of the Occupational Therapists Board, Supplementary Medical Professions Council, Hong Kong. Her interests are in the health of older adults, multidisciplinary care and family medicine training. Rosemary Leonard is Adjunct Professor and Chair in Social Capital and Sustainability at Western Sydney University. She also coordinates the Research Group at Narara Ecovillage, NSW. She has a long-term interest in community development and improving the well-being and resilience of urban and rural communities, with a particular concern for the social support needs of care givers. She was managing editor of the journal Third Sector Review and was director of the Social Justice and Social Change Research Centre at Western Sydney University. She developed the internationally used Death Literacy Index. She was also a senior research scientist with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia, leading projects addressing the effects of coal seam gas development and water infrastructure on communities. Chien-Tat Low is a geospatial analyst. His PhD and postdoctoral fellowship research at the University of Hong Kong focused on urban environmental health and is published in prominent international peer-reviewed journals and book chapters. He now heads the China Water Resources (CWR) geospatial analyses to identify risk hotspots for resilience planning, with his 3D flood maps being cited in the media and triggering many corporations and banks to start assessing coastal threats. His geospatial models have been included in financial reports and were key inputs into the groundbreaking CWR APACCT 20 Index that benchmarks coastal threats across 20 cities in the Asia Pacific. Advocating ‘low-regret’ and transformative adaptation, he believes climate tipping points are too risky to bet against. Currently, he leads CWR’s Re-IMAGINE HK initiative and sits on the Hong Kong University of Science and Technology (HKUST) ‘Shaping a Sustainable Northern Metropolis’ steering group to protect Hong Kong against future coastal threats. Christine A. Mair, a sociologist and social gerontologist, is Associate Professor of Sociology and Gerontology and Director of the Center for Health, Equity, and Aging in the Department of Sociology, Anthropology, and Public Health at the University of Maryland, Baltimore County, USA. She also holds a secondary appointment in the Department of Epidemiology and Public Health in the University of Maryland School of Medicine. She specialises in the social environment, cross-national comparisons of ageing populations, and multilevel social contexts of well-being. Her work focuses on harnessing datasets from individual, neighbourhood and country levels to examine variation in associations between social ties and older adults’ well-being by culture, demographic shifts, location, public policy and other factors.

xiv  Handbook of quality of life research Robert W. Marans is Research Professor Emeritus at the Institute for Social Research, and Professor Emeritus of Architecture and Urban Planning in the Taubman College of Architecture and Urban Planning at the University of Michigan, USA. He has conducted research and evaluative studies dealing with various aspects of communities, neighbourhoods, housing, and parks and recreational facilities, focusing on attributes of the physical and sociocultural environments and their influence on individual and group behaviour and quality of life. Currently, his work deals with cultural issues of sustainability. He is a recipient of Social Science Research Council pre-doctoral fellowship, a Career Award of the Environmental Design Research Association and a designated fellow of the American Institute of Certified Planners and Pioneer in Quality of Life Research by the Journal of Applied Research in Quality of Life. Gesine Marquardt is a registered architect and Professor of Social and Healthcare Buildings in the Faculty of Architecture at Technische Universität Dresden, Germany, where she is the liaison officer for students with disability and chronic disease. Her teaching and research focuses on designing environments that enable equal access for all individuals, contributing to a more inclusive society. After completing a postdoc at the Johns Hopkins Medical Institutes, USA, she headed the Independent Junior Research Group on ‘Architecture Under Demographic Change’ funded by the German Research Association (DFG). Her academic and design work develops architectural concepts for the healthcare sector in a society impacted by demographic change, focusing on dementia-friendly architecture across different settings, including the individual home, long-term and acute care environments. Her publications focus on disseminating knowledge about dementia-friendly architecture. Rod McCrea is a social scientist with the Sustainability Pathways Program of Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land and Water. His research interest interfaces between environments (natural and built) and human attitudes and behaviour. His fields of research expertise include urban quality of life, regional community well-being and resilience, social licence to operate, and more recently, public perceptions of responsible innovation. Amanda J. McIntyre, a social science researcher and programme evaluator, is a research analyst with an independent research and consulting firm in Victoria, Canada. She graduated from the University of Victoria, where her research focused on the impact of empathy and perspective-taking on concern for environmental problems and pro-environmental behaviour. Wai-Kit Ming is Assistant Professor of Public Health and Epidemiology and programme leader of the Master of Public Health postgraduate programme at the City University of Hong Kong. He is an experienced clinical doctor and medical educator with over ten years’ experience teaching and mentoring medical students, and has supervised over 100 undergraduate and graduate research students. His postdoctoral training was at Oxford and Harvard universities. He has held fellowships at the Nuffield Department of Women’s and Reproductive Health, University of Oxford, the National Perinatal Epidemiology Unit in the UK, and the Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard University. His research has focused on health economics issues of quality of life, and the development of cutting-edge tools for health economics research and advanced artificial intelligence applications in medical diagnoses and treatments related to diabetes. He has more than 100 peer-reviewed publications.

Contributors  xv Esteban Montenegro-Montenegro is an assistant professor in the Department of Psychology and Child Development at California State University, Stanislaus, USA. He focuses on several research areas, statistics and research methods, along with studies in healthy ageing and dementia prevention. Esteban has collaborated with a variety of fields such as interior design, nursing and financial planning on topics related to health and access to better designs in health centres. His current work aims to develop conceptual and methodological approaches to measure familism and its influence on healthy ageing in California. His scope of work expands to the relationship between familism and the perspective of public spaces in older adults. Kostas Mouratidis is an associate professor in the Section for Geography, Department of Geosciences and Natural Resource Management at the University of Copenhagen in Denmark. He has published across various topics in the field of urban and transport planning, focusing on urban social sustainability and urban mobility, including topics such as cities and well-being, neighbourhood satisfaction, urban vitality, social cohesion in urban space, transport and well-being, urban walkability and emerging mobility. His research combines quantitative and qualitative methods with geographic information systems (GIS). Currently, he leads research projects on new mobility in cities (AppCities) and urban quality of life (REPLAN) and participates in research projects on local social sustainability (SOSLOKAL), walkability (WALKMORE) and school segregation (ECASS). Gordon F. Mulligan is professor emeritus in the School of Geography, Development, and Environment at the University of Arizona, USA. He has held appointments at other institutions, including the University of Washington, Queen’s University (Canada) and Flinders University (Australia). Mulligan’s original background was in mathematics. He has made contributions to central place theory and industrial location analysis. His interests in human geography and regional science diversified over time, now including shopping models, economic base analysis, regional migration and demography, location conflict, US metropolitan labour markets, quality of life and amenity valuation, innovation and patenting, and the history of ideas in regional science. He was book review editor of the Annals of Regional Science and co-editor of the Journal of Regional Science, and has actively served on numerous editorial boards and research juries. He is a fellow of the Western Regional Science Association and of the Regional Science Association International. Gordon passed away in November 2023, while this book was in production. Chris Pettit is the director of the City Futures Research Centre, Inaugural Professor of Urban Science, and Plus Alliance Fellow at the University of New South Wales in Australia. He is chair of the board of directors for Computational Urban Planning and Urban Management and on the International Advisory Board for the ‘Geo for All’ initiative. He is a member of the Planning Institute of Australia’s National Plantech Working Group, the advisory board for the Centre for Data Leadership, the Committee for Sydney’s Smart Cities Taskforce, and the NSW Government Expert Advisory Group for Planning Evidence and Insights. He established the City Analytics Lab (CAL), a dedicated space designed to support collaborative city planning and user-centred design. His expertise is in the convergence of the fields of city planning and digital technologies including geographical information sciences. His research includes developing the use of digital planning tools to support a data-driven approach to designing future city scenarios. He is on the editorial board for a several journals and has published over 200 academic papers.

xvi  Handbook of quality of life research James J. Potter is a registered architect in the US and emeritus professor in the College of Architecture, University of Nebraska-Lincoln. He has been chair of the Department of Architecture and Chair of the Graduate Committee and held the Douglass Professorship. He has taught studios on human behaviour, community and environmental needs, and held seminars on environment–behaviour studies, housing and quality of life. He has been a visiting scholar in schools of architecture in Brazil, China, Ireland, Germany, Malaysia, Nigeria, Russia and Taiwan. His work in environmental design sought to bridge the gap between the teaching of architecture, the development of community-based projects and the execution of relevant environmental design research. His research has focused on impact of physical and social change (especially rapid development) on people’s well-being. Jessica Rourke is an assistant teaching professor at the University of Victoria in Canada, an open-learning faculty member at Thompson Rivers University, Canada, and a complex-case worker at Restorative Justice Victoria. Her work in social psychology focuses on forgiveness and restorative justice. She is the recipient of a Victoria Community Leadership Award and a University of Victoria Award for Early Career Teaching. Leila Scannell holds a PhD from the University of Victoria, Canada, and was a postdoctoral fellow in the Sustainable Building Science Program at the University of British Columbia and a Banting Postdoctoral Fellow at Royal Roads University, Canada. Her research focuses on the nuances of place attachment. Although her work is highly cited and gained experience across a breadth of research approaches, the pull of her own attachment to place led her out of academia. She currently enjoys family life on Vancouver Island and works as the quality improvement manager for the Nanaimo Division of Family Practice, supporting and evaluating programmes that improve primary care and contribute to healthier communities. Robert J. Stimson, AM, a geographer and regional scientist, is now Emeritus Professor of Geography at the University of Queensland and an honorary professorial fellow at the University of Melbourne, Australia. For six decades he has conducted research and published extensively in a range of fields including urban and regional analysis and planning, spatial inequalities, housing, human spatial behaviour, regional economic development, and quality of urban life. He has collaborated with leading researchers in numerous countries around the world, and has directed research centres in numerous universities in Australia. He is the author of more than 50 books and over 400 book chapters, journal articles and conference papers. He is a former president of the Regional Science Association International (RSAI) and the International Geographical Union’s Applied Geography Commission. He is a fellow of RSAI and of the Academy of the Social Sciences in Australia. He has been awarded a Member of the Order of Australia for his contributions to science, especially in the field of analytical human geography. Piret Veeroja is a research fellow at the Centre for Urban Transitions, Swinburne University of Technology in Australia. Her research focuses on the built environment, housing conditions and aspirations as fundamental to equitable and healthy cities. She uses large quantitative data and spatial methods to explore how the built environment links to people’s physical and mental health, well-being and quality of life – particularly for vulnerable groups, such as older adults and young people.

Contributors  xvii Helga von-Breymann is an architect specialising in urban planning and associate professor at the University of Costa Rica, where she coordinates the LACITE (Laboratory for City and Territory). She has been a consultant to various national and international organisations. Her research focuses on people–environment relations and the role that urban planning plays in these dynamics. Currently, she is conducting a study identifying residential satisfaction in public housing projects in Costa Rica and its relationship with disadvantaged environments and inconsistent urban planning politics. Hans-Werner Wahl is senior professor and project director at the Network Aging Research of Heidelberg University and senior researcher at the Institute of Psychology of Heidelberg University, Germany. His research includes investigating the role of subjective ageing as well as the physical-technological environments for ageing well, adaptational processes related to chronic functional loss and conceptual issues in ageing research. He is the founding editor of the European Journal of Ageing (together with Dorly Deeg), consulting editor of Psychology and Aging, an editor of the Zeitschrift für Gerontologie und Geriatrie and an editorial board member of The Gerontologist. He is a fellow of the Gerontological Society of America (GSA). He is a recipient of the M. Powell Lawton Award of the GSA, the IAGG-ER Advanced Scholar Award: Socio-Behavioral Sciences, the Richard M. Kalish Innovative Publication Award (Article) of the GSA and the Richard M. Kalish Innovative Publication Award (Book), GSA. Andrea Walton is a senior social science researcher in Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) Environment investigating the social dimensions underpinning sustainability transitions to a low carbon economy. Her interests are in community well-being and resilience, social acceptability, and the social and behavioural enablers needed to support a circular economy. Noah J. Webster is a sociologist specialising in medical sociology and the sociology of ageing. He is an associate research scientist in the Life Course Development Program at the University of Michigan’s Institute for Social Research, USA. His research embedded in theories of social relations and ecological context examines how built, natural and social environments intersect to create unique forms of disadvantage across the life course. This includes a study funded by the US National Institutes of Health focused on identifying resources (such as social capital) within disadvantaged environments that can be utilised in interventions to offset environmentally linked health disparities. He is an associate editor of the journal Innovation in Aging and a fellow of the Gerontological Society of America (GSA). He has served in leadership roles in both US-based and international societies including the GSA, the Society for the Study of Human Development and the International Society for the Study of Behavioural Development. Paulina Pui-Yun Wong is an environmental health geographer. She is currently an associate professor and head of the Science Unit at Lingnan University and associate director of the LEO Dr David P. Chan Institute of Data Science, Hong Kong. She specialises in urban climate, air/noise pollution, geographic information systems (GIS), and environmental health. In recent years, her research has been extended to GeoAI analytics, urban sensing, mobile geospatial technologies, sustainability education, environmental, social and corporate governance (ESG) and social policy. She has been a Fulbright and Lee Hysan scholar and is a certified GIS Professional. She also serves on a number of professional associations such as council member and vice-chairman of the Spatial Data Infrastructure Committee of Smart City Consortium, honorary secretary of the Hong Kong Geographic Information System

xviii  Handbook of quality of life research Association (HKGISA), committee member and advisor to the Hong Kong Alliance of Built Asset & Environment Information Management Associations (HKABAEIMA) to foster GIS, open building information modelling (BIM) and smart city development. She is also a member of the editorial board and review editor of several quality international journals. Casper J.P. Zhang is a psychologist and epidemiologist focusing on health-related behaviours and their environmental correlates. He is a research assistant professor in the School of Public Health at the University of Hong Kong. His research revolves around the urban environment, active living and mental well-being. He has authored more than 50 peer-reviewed publications, with two classified as Highly Cited (ESI) in 2021. He has served as guest editor for two research themes in academic journals and has reviewed articles for more than 20 international journals. He is also a fellow of the Royal Society for Public Health, a member of the Australian Psychological Society and a full member of the American Psychological Association (Division 38: Health Psychology and Division 8: Personality and Social Psychology). Thanasis Ziogas is a postdoctoral researcher at the Department of Economic Geography, Faculty of Spatial Sciences at the University of Groningen, the Netherlands. He conducts research and publishes in a range of fields including public economics, fiscal adjustments and re-election prospects, urban and rural differences in quality of life and spatial interdependencies of happiness. He is a contributor to the World Database of Happiness.

Preface

Quality of life (QOL) is a complex and elusive concept that has attracted the attention of researchers from many disciplines. There is no agreed upon nor precise definition of QOL. It is a term often used interchangeably with well-being and happiness. In addition to the interest of researchers, QOL is central to the work of those in government, including elected officials, policymakers, urban planners and others who strive to better the lives of their constituents. Thus, QOL research has direct implications for policy formation and planning. It is important to emphasise how the investigation of QOL has been characterised by researchers taking both objective and subjective approaches, and indeed by seeking to develop modelling frameworks that integrate objective and subjective measures. Often, many of these measures deal with the environment or place and space. This handbook expands upon the 2011 publication Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, which offered a comprehensive review of research about the QOL in cities and other habitable settings. Since that time, societies throughout the world have experienced several shockwaves that bear on the way people live and their health and well-being. Among these shocks was the COVID-19 pandemic that resulted in the death of millions and dramatically changed the patterns of daily living for many more. Changes included a shift to the home environment as a workplace and greater use of accessible outdoor spaces for relaxation and recreation. At the same time, we have seen in different parts of the world a growing number of natural disasters resulting from climate change, including floods, droughts, wildfires and temperature extremes forcing the displacement of populations from their homes in previously populated areas. During this same period, the world population has continued to grow, and in 2022, reached the staggering figure of 8 billion people. Much of that growth has taken place in large urban areas. Relatedly, the world’s population of older adults has dramatically increased since the 1950s, driven by increased life expectancy and declining birth rates. Under such conditions, the places and spaces where older people live and the impact they have on their health and well-being has become ever more apparent. In addition to expanding on the earlier publication, this Handbook reflects some of these major societal events and trends by emphasising place and space – the situational context in which people live – as influencing factors in the QOL experience of people. It does so in several ways, including chapters that consider places for older people and chapters dealing with nature and outdoor environmental conditions. The Handbook is organised into six main parts. In the introductory chapter of Part I, we discuss the complexity and diversity of QOL research as well as its relevance for policy and planning. Other chapters in Part I present a brief overview of approaches to measuring and analysing QOL data, a discussion of happiness in terms of social spatial inequities and an environmental psychological framework for considering the impacts of places on people’s well-being. Part II draws heavily on the earlier publication, reviewing various methodological approaches to measuring QOL. It includes chapters discussing measurement and modelling, including the objective and subjective approaches to investigating QOL and how those xix

xx  Handbook of quality of life research approaches can be integrated. Other chapters in Part II demonstrate how specific environmental and context factors may impact QOL. In Parts III and IV, chapters cover specific examples of QOL research in different types of settings, ranging from large cities and metropolitan areas to small and new towns, as well as showing how migration flows of people might be explained by QOL factors. Part V contains chapters dealing with the importance of environmental phenomena such as open spaces, natural environments and environmental degradation as influencing people’s health and their overall QOL. Reflecting the way in which societies across the world are exhibiting an ageing population, Part VI focuses specifically on the importance of the places and spaces in which older people live as phenomena impacting their lives. The chapters focus on environments varying greatly in scale, including the home environment, hospitals, neighbourhoods, regions and countries. The Handbook sets out to demonstrate to researchers, policy analysts and planners how multiple approaches have been taken to conceptualise, measure and model the complex issue of QOL. In particular, the authors of several Handbook chapters have provided details of the methodologies used to investigate QOL with respect to both people and places. Scientific study of QOL certainly requires and spans across many academic disciplines. This is particularly true when focusing on place and space impacts on QOL. To this end, the Handbook’s contributors represent academic disciplines, including architecture, economics, environmental sciences, geography, health sciences, landscape architecture, psychology, social work, sociology, statistics, urban planning as well as practitioners and healthcare providers. Collectively, the Handbook chapters provide greater understanding of how places and spaces play a role in how people experience their surroundings, both perceptually and behaviourally, and ultimately impact their QOL. In doing so, they demonstrate that frameworks and theories spanning multiple levels are required as well as the use of diverse methodological approaches. The editors

PART I QUALITY OF LIFE: A UNIVERSAL PERSPECTIVE

1. Complexity and diversity of quality of life-related research Robert J. Stimson, Robert W. Marans and Noah J. Webster

INTRODUCTION Interest in quality of life (QOL) and related issues can be traced back to ancient times. In The Republic, Plato (360 BCE) wrote about what might underlie a good life in terms of both the individual and wider society. Later, Aristotle emphasised the pursuit of happiness as the end goal of every person. Much later, Jeremy Bentham ([1780] 1948) proposed a ‘felicific calculus’ measurement system (see Michalos, 2006) that marked the beginning of subsequent philosophical and scientific interest in the study of QOL and related concepts such as well-being and happiness. The interest of contemporary researchers in QOL and related issues gained strong momentum during the twentieth century, particularly during the 1960s. This research had many antecedents across a wide range of disciplines – including health, sociology, economics, geography, psychology, planning and urban design – each of which brought different perspectives reflecting varying conceptual and philosophical foundations, while being concerned with individual and wider community welfare that often tended to be referred to as well-being. This upsurge of interest in QOL is reflected in both the academic literature and the popular media. An indication is the formation of the International Society for Quality-of-Life Studies (ISQOLS) in 1995, which holds annual conferences, and which, in 2006, launched the journal Applied Research in Quality of Life. More recently, there have been two comprehensive editions of the Encyclopedia of Quality of Life and Well-being Research (see Maggino, 2021; Michalos, 2014). A search for ‘Quality of Life’ on Google Scholar reveals more than 4600 results, while a search of the Institute for Scientific Information (ISI) database reveals over 60 000 academic citations. QOL is a truly multidimensional concept (Glatzer et al., 2015; Sirgy et al., 2006). How to measure QOL/well-being, how to assess it and how to investigate its effects on human behaviour are increasingly important topics within the social sciences (Diener and Biswas-Diener, 2008; Diener and Suh, 2000; Dissart and Deller, 2000; Sousa Gomes et al., 2010). As indicated above, QOL is a term often used interchangeably with well-being, and researchers from some disciplines such as economics, have used the term happiness. QOL is thus a broad concept, influenced by interrelated subjective and objective aspects about the individual and the overall economic, social and physical environment in which people live (Bonomi et al., 2000). The situational or place and space context – including spatial scale – in which people live is an important part of QOL/well-being research. QOL is tied to the perception of meaning, the quest for which is central to the human condition (Frankl [1946], 1963). It has a strong psychological component, embracing individual perceptions, feelings, beliefs and values, as well as recognising the importance of demographic, economic, social, cultural and environmental factors as possible determinants of QOL. 2

Complexity and diversity of quality of life-related research  3 One might broadly interpret QOL as the level of satisfaction that a person has with their life – and some suggest the degree of happiness – that people receive or derive from the surrounding human and physical conditions that are scale-dependent and can affect the behaviour of individual people, groups such as households and communities, and economic units such as firms (Mulligan et al., 2004). For a long time, the question of how to enhance QOL/well-being has been an explicit or implicit lifestyle objective of individuals and households. Additionally, it has been a concern of policy and planning (Schuessler and Fisher, 1985; Sen, 1985), where the objective might be to include strategies to invest in approaches to enhance QOL for the individual as well as well-being at the local (community) and wider levels of scale (Costanza et al., 2008). Progress in the investigation of well-being post-World War II has been well documented (for example, by Estes and Sirgy, 2019; Sirgy et al., 2017), with the latter outlining the ‘transformative changes’ (p. 141) that have occurred, especially in the health, education, economic and welfare sectors, to enhance people’s QOL and improve societal well-being. Not surprisingly, much of the research investigating QOL has been concerned with health-related issues, as represented in the journal Health and Quality of Life Outcomes, with a focus on: (1) assessing and evaluating medical and psychological interventions and their QOL outcomes for people; and (2) studies on the psychometric properties of health-related QOL measures. But the explicit context for investigating QOL has been broad and diverse, being studied in developed and developing countries, in the situational context of both urban and rural areas, and with respect to many domains such as work, health and welfare, social interaction, recreation and tourism, access to facilities and transportation, and mobility and migration. Examples of studies include the following diversity of contexts in which QOL studies have been undertaken: ● investigating the relationship between QOL and housing in Hong Kong (Ng et al., 2018); ● investigating the relationships between real estate prices, income levels and neighbourhood amenities and QOL in Buenos Aires (Cruces et al., 2008); ● investigating how transportation accessibility affects QOL and how land use and transportation policies might enhance QOL (Doi et al., 2008); ● assessing QOL across European cities from the perspective of human capital and competitiveness (Morais et al., 2011); ● investigating the impact of people’s communication activity on their QOL and the effects of how cosmopolitan a city is on its residents’ QOL (Jeffres et al., 2008); ● investigating how residents of informal settlements in Iran perceive their QOL and the implications for policy (Zakerhaghighi et al., 2015); and ● investigating the effects of tourism on QOL domains and people’s overall life satisfaction (Uysal et al., 2016). The extensive literature on QOL includes several important books that provide comprehensive overviews of research approaches and methodologies used to investigate QOL, well-being and happiness. These books also help to understand the situational contexts and environmental attributes that impact people’s QOL/well-being (see, for example, Andrews and Withey, 1976; Bruni and Porta, 2016; Campbell, Converse and Rogers, 1976; Estes and Sirgy, 2017; Glatzer et al., 2015; Land et al., 2012; Phillips, 2006; Ripley, 2003). In this introductory chapter we look initially at the complexity and diversity evident in QOL-related research, which lacks an agreed upon explicit definition. We then discuss QOL,

4  Handbook of quality of life research well-being and happiness as well as the importance of undertaking research into QOL in a place or space context. Finally, the relevance of QOL/well-being research for policy and planning is addressed. Subsequent chapters of this Handbook provide detailed discussions of those topics.

QUALITY OF LIFE: AN ELUSIVE, AMBIGUOUS TERM A confusing aspect of the extensive literature on QOL is the proliferation of terms that are used when investigating aspects of people’s life experiences. Terms used include quality of life, well-being, satisfaction and happiness. QOL is typically seen as embodying people’s overall well-being and their general sense of satisfaction, happiness, tranquillity and peace of mind (Ryff, 2006). QOL is also a term that has been used to indicate the liveability of a particular place such as a neighbourhood, city or a nation. However, an adequate definition of QOL, and measuring progress to improve it, has been both elusive and ambiguous. In many ways it is perhaps artificial to differentiate between QOL and well-being, with the terms seemingly being used interchangeably by researchers. It is apparent that the focus has been on people’s self-assessment of, or the level of satisfaction with, their overall QOL – which might also be seen as an assessment of their happiness – with respect to specific components or domains of QOL/well-being. Quality of Life The Merriam-Webster Dictionary defines QOL as the ‘overall enjoyment of life: general well-being’. This definition frames QOL as a psychological context that can be measured as a subjective assessment of well-being derived from data collected about the individual, typically through survey research. Another definition from the Dictionary of Human Geography (see Rogers et al., 2013) sees QOL as ‘an overarching concept concerning the general well-being of individuals and communities’. This definition puts QOL in a broader context that includes not only a subjective assessment of QOL/well-being, but also an objective indicator of QOL/well-being derived from data that are not just about the individual, but also about the human and physical environmental context in which people live, work and play. The definition of QOL used by the World Health Organization (WHO) is: [T]he individual’s perception of his own situation in life in the context of culture and the patterns of values in which he lives and the extent to which he matches his goals, his concerns about his physical and psychological health, his level of independence, his social relations, his beliefs and his relationships to the environment. (The WHOQOL Group, 1998, p. 551)

This definition incorporates both a subjective assessment of, and an objective measurement of, QOL/well-being. Diverse subjective and objective QOL indicators have been proposed in research into QOL/ well-being. QOL generally refers to psychological life, measuring people’s assessments/evaluations of: (1) life in general, which might be regarded as an overarching term and a measure of satisfaction, happiness or enjoyment; and (2) specific domains of life, including everything from physical health, family, education, employment, wealth, housing safety and security, to freedom, religious beliefs and the environment. Assessments of QOL that reflect how people’s

Complexity and diversity of quality of life-related research  5 expectations of what a good life is – guided by the values, goals and socio-cultural and environmental context in which a person lives, works and plays – are being realised. QOL has been viewed as being associated with the level determined by the standards of QOL, which depends on both the self-assessment and the objective measurement of what is meant by ‘quality’ (Katsching, 1997). As already indicated, people’s subjective assessments of QOL in general and of specific QOL domains may be influenced by their demographic characteristics, and by objective economic, social and environmental indicators embedded in the place or space settings in which people live and where they spend time, which may impact their QOL experiences, contributing to people’s judgements about their lives (Kahneman et al., 1999). With regard to its multidimensionality, QOL may define a standard level of emotional, physical, material and social well-being, with both positive and negative connotations. It can also serve as a reference point against which an individual or society can measure the different domains of their life. The degree to which those QOL domains give satisfaction and contribute to people’s subjective well-being might be called life satisfaction. QOL is thus highly subjective and inherently ambiguous, as it can refer both to the experience an individual has and to the objective situational context and living conditions in which individuals find themselves. For example, one person may define QOL according to wealth or satisfaction with life, while another may define it in terms of emotional or physical capabilities. Costanza et al. (2008) propose that an integrated definition of QOL might combine measures of human needs with subjective well-being and happiness, with QOL being a multiscale, multidimensional concept that contains interactive objective and subjective elements. They suggest that QOL is related to the opportunities that are provided to meet human needs in the form of built, human, social and natural capital (in addition to time), along with the policy options that are available to enhance these opportunities. Well-being As with QOL, well-being is also an elusive and ambiguous term. The Oxford Dictionary defines well-being as ‘the state of being comfortable, healthy or happy’. This is not too dissimilar to its definition of QOL. The Dictionary of Human Geography (Rogers et al., 2013) defines well-being as ‘the state of feeling content and healthy, of experiencing a good quality of life’. This definition is more restrictive than the definition of QOL, confining ‘well-being’ to being subjective assessment and experience of the individual. These definitions of well-being are focused on a person’s health or state of mind, which inadequately reflects the focus on objective measures in much of the published research on well-being. While well-being and QOL are often taken as being synonymous, some research suggests that well-being might be viewed as being more about creating the conditions for people and society to thrive, while QOL is more about prosperity, positive physical and mental health, satisfaction, happiness and sustainable communities. Well-being might thus be a measure of social progress and, arguably, be a goal of good policy and planning for the environments we encounter in our daily lives. Thus, human well-being might be viewed as a broad concept that includes many aspects of our everyday lives, while QOL embodies in practice both subjective and objective perspectives. In recognising the subjectivity of QOL and well-being, some researchers have used the term subjective well-being (SWB), which might be regarded as a synonym for QOL. Frankl ([1946]

6  Handbook of quality of life research 1963) tied SWB to the perception of meaning. Diener (2000) regards SWB as being an area of positive psychology in which SWB is about ‘people’s cognitive and affective evaluations of their lives’ (p. 34), and focuses on: ● ● ● ●

understanding the components of SWB; the importance of adaptation and goals to feelings of SWB; the temperament underpinnings of SWB; and the impact of cultural influences on SWB.

This subjective focus incorporates measuring well-being on the same range and types of domains as used in investigating subjective QOL. However, much of the research on well-being has been undertaken in a spatial or place context, it being about measuring the performance of society and of specific places – ranging from the neighbourhood or suburb to the wider city, region and the nation – on a range of objective indicators, which might include economic, social, cultural and environmental indicators derived from secondary data where there is an explicit focus on spatial variations in the incidence of those indicators (see Chapter 5 for a detailed discussion). While the distinction between QOL and well-being might be artificial, with the terms often being used interchangeably, some researchers have seen QOL as being a subcomponent of well-being. From this perspective, QOL is embodied within a broader framework for measuring progress on well-being (Boarini et al., 2014). This is reflected in the considerable policy interest in developing QOL/well-being indices (see, for example, Hagerty et al., 2001). Happiness Some of the research focusing on people’s subjective assessments of their QOL, and their level of satisfaction with life in general, has taken that to be an indication of a person’s happiness. Indeed, Veenhoven (2012) sees happiness as being about life satisfaction and SWB. However, Campbell, Converse and Rodgers (1976) considered satisfaction as being more readily definable – and thus a more useful construct – than happiness. This is because satisfaction implies judgement or cognitive experience, while happiness might more accurately reflect a relative short-term emotional state represented by elevated mood. There has been a proliferation of research on happiness in the last couple of decades as seen by the publication of many books on the topic (for example, Bruni and Porta, 2016; Diener and Biswas-Diener, 2008; Eid, 2007; Lyubomirsky, 2008; Thaler and Sunstein, 2008; Van Praag, 2008; Weiner, 2008; White, 2006). Diener (2000), for example, suggests that the investigation of QOL/SWB could produce an ‘indicator of happiness’. Indeed, much earlier, McCall (1975) proposed that the best way to approach QOL measurement was to measure the ‘extent to which people’s “happiness requirements” are met’ (p. 229). The quantitative analysis of happiness has developed scales to measure individual and collective norms that include satisfaction not only with life as a whole, but also with domains of life, such as health and income. Some of the research on happiness reflects work by economists using econometric models. Those models examine the relationship between happiness and multiple variables such as income, health, marriage, gender and social norms. But such factors have long been investigated by psychologists and sociologists in their research on happiness and satisfaction, often undertaken in the context of studying well-being.

Complexity and diversity of quality of life-related research  7 There are ongoing surveys measuring happiness, such as the Australian Unity Wellbeing Index, which has been measuring the happiness of people in Australia since April 2001 (Cummins et al., 2003). Such studies propose that the two key factors relating to people’s happiness are: (1) an internal factor – namely, relationships (for which one may read as having an emotionally intimate relationship); and (2) an external factor – namely, resources (for which one may read as ‘money’). Cummins et al. (2003) suggest that happiness increases only marginally beyond a threshold household income, later stabilising, thus exhibiting the Easterlin paradox, which says that once people have met their basic needs, they do not become happier as they become richer. Advances are being made in validating measures and predictors of happiness and SWB, with higher levels of happiness (or satisfaction) being associated with better health and increased longevity, as well as better social relationships, work performance and creativity (Diener et al., 2018). Diener et al. (2018) point out that developing national accounts of SWB/ happiness are being considered and have been adopted by some nations already. A detailed discussion on happiness is provided in Chapter 3, particularly from an economic perspective.

QUALITY OF LIFE IN A PLACE AND SPACE CONTEXT There is ample evidence demonstrating that ‘place’ matters when it comes to QOL/well-being. Studies focusing on investigating QOL/well-being in the context of place or space can enable us to better understand the meaning of QOL/well-being and how it might be measured (Marans, 2003, p. 73). However, it is somewhat surprising that, among the many thousands of publications on QOL research there has been a paucity of studies investigating QOL/ well-being undertaken explicitly in a place or space context. Now that the majority of people in the world live in urban areas – and especially in large cities – it might be expected that the place or space context of where people live, work and play would be an important factor to consider in research on QOL. An edited volume by two of the editors of this Handbook (Marans and Stimson, 2011) explicitly sought to do that by focusing on investigating the ‘quality of urban life’ (QOUL).

POLICY, PLANNING AND QUALITY OF LIFE Research into QOL is important not only because it affects how people behave, but also because it affects their well-being and happiness (Marans and Stimson, 2011). Enhancing our understanding of QOL has the potential to help inform public policy in relation to human welfare/well-being (Cummins and Land, 2018), and it can be an effective tool in urban planning, management and design (Clifford et al., 2017; Rezvani et al., 2013). Demonstrating the applied value of QOL research is an explicit purpose of the journal Applied Research in Quality of Life, published by ISQOLS. Its goal is to ‘help decision-makers apply performance measures and outcome assessment techniques based on concepts such as well-being, human satisfaction, human development, happiness, wellness and quality-of-life’.1

8  Handbook of quality of life research There is ample evidence demonstrating that QOL/well-being can have wide implications for policy, planning and design. For example: ● Social indicators have been extensively used to inform policy aimed at addressing issues such as inequalities in community well-being and disparities in development. ● The level of urban quality/amenity of a place can play a decisive role in attracting people to live in a place or causing them to leave. ● QOL outcomes might be enhanced through enlightened planning and good urban design that can directly affect the liveability of cities and neighbourhoods for the benefit of residents within them, and provide a set of metrics that allow policymakers and planners to assess the effectiveness of their efforts over time (Marans, 2003). ● QOL/well-being metrics are now starting to be used to provide an alternative perspective on social progress and development. It has even been suggested that QOL may underlie the demand for public action (Dahmann, 1985; Lu, 1999). Enhancing People’s QOL and Community Well-being QOL/well-being research has had wide implications for policy goals and planning interventions where an implicit or explicit objective is to enhance people’s QOL and to advance the well-being of communities. QOL/well-being performance indicators have long been used to evaluate outcomes of policy and planning interventions (Sirgy, 2018; Uysal and Sirgy, 2019), with it being common to use such indicators to: (1) distinguish between ‘distressed’ and ‘flourishing’ communities (Clifford et al., 2017); and (2) inform decisions about targeting the allocation of funding for purposes such as regional assistance. For example, for a long time, improving QOL/well-being has had a prominent place in policy thinking in the UK (Bache, 2020). It has also been an objective of regional development programmes in the European Union. It is common for regional policy to be concerned about the so-called dichotomy between urban and rural areas, with the latter often being characterised as having higher levels of well-being. But this might be contested, with Lenzi and Perucca (2020) pointing out that it has been assumed that three elements influence the role of urbanisation in SWB, namely: ● the type of externalities generated by cities; ● the spatial accessibility to those externalities; and ● the temporal dimension. Empirical modelling has shown these factors to be important determinants of people’s QOL/ well-being, which indicates that the association of QOL/well-being with urbanisation is more complex than has been presumed. QOL, Amenity, Migration and Regional Development It is well established that the QOL of a place as reflected in its urban amenities can play a significant role in explaining interregional migration flows, and it may influence where people decide to live within a city (Campbell, Converse, Rodgers and Marans, 1976; Golledge and

Complexity and diversity of quality of life-related research  9 Stimson, 1987; Zehner, 1977). Access to high-quality urban amenities and the level of QOL of a place can act as an attractor, enticing people to move to a place (see Chapter 17 in this Handbook). A study conducted for the US Department of Housing and Urban Development (Glaeser et al., 2000) showed that urban growth is driven by a variety of QOL issues relating to urban consumption experiences. Seven urban consumption areas were identified as predisposing an area to rapid urban growth: ● a rich variety of high-quality public services (especially in health, education and public safety); ● aesthetic and attractive physical settings in the form of architecture, urban design and natural features such as a favourable climate; ● easy movement around the city, with resident location pertaining more to easy access to consumption opportunities and less to access to work; ● a housing stock that is architecturally distinctive, affordable and varied; ● neighbourhoods that are safe and ethnically diverse, that offer transport choices, that have a mix of compatible uses (for example, retail, residential and commercial), and that contain parks and open spaces; ● civic spaces and civic activities that provide opportunities for social interaction among residents; and ● a reasonable cost of living. These findings confirmed the suggestions by Ley (1996) and Rogerson (1999) that the consumption experiences afforded in places of in-migration and urban growth are key factors determining QOL. In addition, Florida (2002, 2008) has shown that the opportunities offered by certain places for achieving a high level of QOL is an impetus for attracting and retaining what he calls the ‘creative class’, which he found to be key to dynamic entrepreneurial economies characterising some cities, such as the San Francisco Bay Area, Boston and Seattle.

CONCLUSION In this introductory chapter, we have discussed the diversity and complexity of QOL and QOL-related research, referring to the various definitions of and synonyms for QOL that have been used in the literature. The importance of studying QOL in a place and spatial context has also been considered. Finally, we addressed QOL research and its contribution to policymaking and planning in the public sector. Included in this discussion is the use of QOL indicators in evaluating the outcomes of policy and planning actions and interventions and in the better understanding of migration patterns. These topics in different contexts are covered in several of the following chapters.

NOTE 1. See the publisher’s website on the journal’s ‘Aims and scope’: https://​www​.springer​.com/​journal/​ 11482/​aims​-and​-scope.

10  Handbook of quality of life research

REFERENCES Andrews, F.M. and Withey, S.B. (1976), Social Indicators of Well-Being: Americans’ Perspectives of Quality of Life, New York: Plenum Press. Bache, I. (2020), ‘Wellbeing’, in I. Bache (ed.), Evidence, Policy and Wellbeing, Cham: Palgrave Pivot, pp. 29–53. Bentham, J. ([1780] 1948), An Introduction to the Principles of Morals and Legislation, New York: Hafner Publishing Co. Boarini, R., Kolev, A. and McGregor, A. (2014), ‘Measuring well-being and progress in countries at different stages of development: towards a more universal conceptual framework’, OECD Development Centre Working Paper No. 325, Organisation for Economic Co-operation and Development. Bonomi, R., Patrick, D., Bushnel, D.M. and Martin, M. (2000), ‘Validation of the United States’ version of the World Health Organization Quality of Life (WHOQOL) measurement’, Journal of Clinical Epidemiology, 53, 1–12. Bruni, L. and Porta, P.L. (2016), Handbook of Research Methods and Associations in Happiness and Quality of Life, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Campbell, A., Converse, P.E. and Rodgers, W. (1976), The Quality of American Life: Perceptions, Evaluations and Satisfactions, New York: Russell Sage Foundation. Campbell, A., Converse, P.E., Rodgers, W. and Marans, R.W. (1976), ‘The residential environment’, in A. Campbell, P.E. Converse and W. Rodgers (eds), The Quality of American Life: Perceptions, Evaluations and Satisfactions, New York: Russell Sage Foundation, pp. 217–66. Clifford, J.S., Rahtz, D. and Sirgy, J. (2017), ‘Distinguishing flourishing and distressed communities: vulnerability, resilience, and a systemic framework to facilitate well-being’, in R. Phillips and C. Wong (eds), The Handbook of Community Well-Being, Dordrecht: Springer, pp. 403–21. Costanza, R., Fisher, B. and Ali, S. et al. (2008), ‘An integrative approach to quality of life measurement, research and policy’, S.A.P.I.EN.S., 1, 17–21. Cruces, G., Ham, A. and Tetaz, M. (2008), ‘Quality of life in Buenos Aires neighbourhoods: hedonic price regressions and the life satisfaction approach’, Working Paper No. R-550, Inter-American Development Bank. Cummins, R.A., Eckersley, R. and Pallant, J. et al. (2003), ‘Developing a national index of subjective wellbeing: the Australian Wellbeing Unity Index’, Social Indicators Research, 63, 159–90. Cummins, R.A. and Land, K.C. (2018), ‘Capabilities, subjective well-being and public policy: a response to Austin (2016)’, Social Indicators Research, 140, 157–73. Dahmann, D.C. (1985), ‘Assessments of neighbourhood quality in metropolitan America’, Urban Affairs Quarterly, 20, 511–35. Diener, E. (2000), ‘Subjective well-being: the science of happiness and a proposal for a national index’, American Psychologist, 55, 34–43. Diener, E. and Biswas-Diener, R. (2008), Happiness: Unlocking the Mysteries of Psychological Wealth, Malden, MA: Blackwell Publishing. Diener, E., Oishi, S. and Tay, L. (2018), ‘Advances in subjective well-being research’, Nature Human Behaviour, 2, 259–60. Diener, E. and Suh, E.M. (eds) (2000), Culture and Subjective Well-being, Cambridge, MA: MIT Press. Dissart, J.C. and Deller, S. (2000), ‘Quality of life in the planning literature’, Journal of Planning Literature, 1, 36–61. Doi, K., Kii, M. and Nakanishi, H. (2008), ‘An integrated evaluation method of accessibility, quality of life, and social interaction’, Environment and Planning B: Planning and Design, 35, 1098–116. Eid, M. (2007), The Science of Subjective Wellbeing, New York: Guilford Press. Estes, R.J. and Sirgy, M.J. (eds) (2017), The Pursuit of Human Well-being: The Untold Global History, Cham: Springer. Estes, R.J. and Sirgy, M.J. (2019), ‘Global advances in quality of life and well-being: past, present and future’, Social Indicators Research, 141, 1137–46. Florida, R. (2002), The Rise of the Creative Class; and How It’s Transforming Work, Leisure, and Everyday Life, New York: Basic Books. Florida, R. (2008), Who’s Your City?: How the Creative Economy is Making Where to Live the Most Important Decision of Your Life, New York: Basic Books.

Complexity and diversity of quality of life-related research  11 Frankl, V.E. ([1946] 1963), Man’s Search for Meaning, New York: Washington Square Press. Glaeser, E., Kolko, J. and Saiz, A. (2000), ‘Consumer city’, NBER Working Paper No. 7790, National Bureau of Economic Research. Glatzer, W., Camfield, L., Møller, V. and Rojas, M. (eds) (2015), Global Handbook of Quality of Life, Dordrecht: Springer. Golledge, R.G. and Stimson, R.J. (1987), Analytical Behavioural Geography, London: Croom Helm. Hagerty, M.R., Cummins, R.A. and Ferriss, A.L. et al. (2001), ‘Quality of life indexes for national policy: review and agenda for research’, Social Indicators Research, 55, 1–96. Jeffres, L., Neuendorf, K., Bracken, C. and Atkin, D. (2008), ‘The influence of communication and cosmopoliteness in quality of life perceptions’, The Open Communication Journal, 2, 17–22. Kahneman, D., Deiner, D. and Schwartz, N. (eds) (1999), Well-being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation. Katsching, H. (1997), ‘Quality of life as outcome criterion in mental health care’, European Psychiatry, 12, 125s, https://​www​.cambridge​.org/​core/​services/​aop​-cambridge​-core/​content/​view/​ 8D0​BF4649472B​2D9BEAAC12​A87D83A0D/​S0924933800033848a​.pdf/​quality​-of​-life​-as​-outcome​ -criterion​-in​-mental​-health​-care​.pdf. Land, K., Michalos, A. and Sirgy, M. (2012), Handbook of Social Indicators and Quality of Life Research, Cham: Springer. Lenzi, C. and Perucca, G. (2020), ‘Urbanization and subjective well-being’, in C. Lenzi and G. Perucca (eds), Regeneration of the Built Environment from a Circular Economy Perspective, Cham: Springer, pp. 21–8. Ley, D. (1996), The New Middle Class and the Remaking of the Central City, Oxford: Oxford University Press. Lu, M. (1999), ‘Determinants of residential satisfaction: ordered logit vs. regression models’, Growth and Change, 30, 264–87. Lyubomirsky, S. (2008), The How of Happiness: A Scientific Approach to Getting the Life You Want, New York: Penguin. Maggino, F. (ed.) (2021), Encyclopedia of Quality of Life and Well-being Research, 2nd edition, Cham: Springer. Marans, R.W. (2003), ‘Understanding environmental quality through quality of life studies: the 2001 DAS and its use of subjective and objective indicators’, Landscape and Urban Planning, 65, 73–83. Marans, R.W. and Stimson, R.J. (eds) (2011), Investigating Quality of Urban Life: Theory, Methods and Empirical Research, Dordrecht: Springer. McCall, S. (1975), ‘Quality of life’, Social Indicators Research, 2, 229–48. Michalos, A. (2006), ‘Conceptual and philosophical foundations’, Social Indicators Research, 76, 163–87. Michalos, A.C. (ed.) (2014), Encyclopedia of Quality of Life and Well-being Research, Dordrecht: Springer. Morais, P., Miguéis, V.L. and Camanho, A.S. (2011), ‘Quality of life experienced by human capital: an assessment of European cities’, Social Indicators Research, 110, 1–20. Mulligan, G., Carruthers, J. and Cahill, M. (2004), ‘Urban quality of life and public policy: a survey’, in R. Capello and P. Nijkamp (eds), Advances in Urban Economics, Bingley: Emerald Publishing, pp. 729–802. Ng, S.L., Zhang, Y. and Ng, K.H. et al. (2018), ‘Living environment and quality of life in Hong Kong’, Asian Geographer, 35, 35–51. Phillips, D. (2006), Quality of Life: Concept, Policy and Practice, London: Routledge. Plato ([360 BCE] 1943), The Republic, New York: New York Books, Inc. Rezvani, M.J., Mansourian, H. and Sattari, M.H. (2013), ‘Evaluating quality of life in urban areas (case study Noorabad City, Iran)’, Social Indicators Research, 112, 203–20. Ripley, M. (2003), Quality of Life: A Critical Introduction, London: SAGE. Rogers, A., Castree, N. and Kitchin, R. (2013), A Dictionary of Human Geography, Oxford: Oxford University Press. Rogerson, R. (1999), ‘Quality of life and city competitiveness’, Urban Studies, 36, 319–28. Ryff, C. (2006), ‘Psychological well-being and ill-health: do they have distinct or mirrored biological correlates?’, Psychotherapy Psychosomatics, 75, 85–95.

12  Handbook of quality of life research Schuessler, K. and G. Fisher (1985), ‘Quality of life research and sociology’, Annual Review of Sociology, 11, 129–49. Sen, A. (1985), Commodities and Capabilities, Amsterdam: North-Holland. Sirgy, M.J. (2018), ‘What types of indicators should be used to capture community well-being comprehensively?’, International Journal of Community Well-Being, 1, 3–9. Sirgy, M.J., Estes, R.J. and Selian, A.N. (2017), ‘How to measure well-being: the data behind the history of well-being’, in R.J. Estes and M.J. Sirgy (eds) (2017), The Pursuit of Human Well-being: The Untold Global History, Cham: Springer, pp. 135–57. Sirgy, M.J., Michalos, A.C. and Ferriss, A.L. et al. (2006), ‘The quality of Life (QOL) research movement: past, present and future’, Social Indicators Research, 76, 343–466. Sousa Gomes, M.C., Rocha Pinto, L.R. and Gomes dos Santos, G. (2010), ‘Quality of life: a reappraisal’, International Journal of Sociology and Social Policy, 30, 559–80. Thaler, R.H. and Sunstein, C. (2008), Nudge: Improving Decisions About Health, Wealth, and Happiness, New Haven, CT: Yale University Press. The WHOQOL Group (1998), ‘Development of the World Health Organization WHO-QOL-BREF quality of life assessment’, Psychological Medicine, 28, 551–8. Uysal, M. and Sirgy, M.J. (2019), ‘Quality of life indicators as performance measures’, Annals of Tourism Research, 76, 219–300. Uysal, M., Sirgy, M., Woo, E. and Kim, H.L. (2016), ‘Quality of life and well-being research in tourism’, Tourism Management, 53, 240–61. Van Praag, B. (2008), Happiness Quantified: A Satisfaction Calculus Approach, Oxford: Oxford University Press. Veenhoven, R. (2012), ‘Happiness: also known as “life satisfaction” and “subjective well-being”’, in K. Land, A. Michalos and M. Sirgy (eds), Handbook of Social Indicators and Quality of Life Research, Dordrecht: Springer, pp. 63–77. Weiner, E. (2008), The Geography of Bliss, New York: Twelve. White, N.P. (ed.) (2006), A Brief History of Happiness, Malden, MA: Blackwell Publishing. Zakerhaghighi, K., Khanian, M. and Gheitarani, N. (2015), ‘Subjective quality of life: assessment of residents in informal settlements in Iran (a case study of Hesar Imam Khomeini, Hamedan)’, Applied Research in Quality of Life, 10, 419–34. Zehner, R. (1977), Indicators of Quality of Life in New Communities, Cambridge, MA: Ballinger.

2. Overview of approaches to measurement and empirical analysis of quality of life and well-being Robert J. Stimson, Robert W. Marans and Noah J. Webster

INTRODUCTION This chapter discusses the approaches that have been used over time to investigate quality of life (QOL)/well-being. It discusses how researchers have developed frameworks to measure and model QOL/well-being, at different spatial scales, and how those investigations are enhanced through application of geographic information system (GIS) tools. Reference is made to some of the modelling frameworks that have been used.

APPROACHES TO INVESTIGATING QUALITY OF LIFE: AN OVERVIEW Since the 1960s, three perspectives or approaches have evolved in research investigating QOL (see Andelman et al., 1998; Marans and Stimson, 2011a; Mukherjee, 1989). They are: ● the objective approach; ● the subjective approach; and ● the integrative approach. These are discussed in greater detail in Chapters 5, 6 and 7 of this Handbook. The Objective Approach The objective approach is often confined to the analysis and reporting of secondary data – usually aggregate data at different geographic or spatial scales – available mainly from official governmental data collections, including the census. During the 1960s and 1970s, the approach was associated with research on social indicators, which attracted the interest of researchers from numerous social science disciplines – see, for example: geographer David Smith (1973, 1977, 1979); planner Judith Innes de Neufville (1975); and sociologists Otis Dudley Duncan (1969) and Peter Rossi (1972). Social indicators were typically used to examine the fulfilment of societal and cultural demands relating to material attributes such as wealth, social status, physical/mental well-being, life expectancy, educational attainment and literacy, standard of living, employment, housing costs, crime rates, and environmental factors such as pollution and access to open space. This approach to QOL tended to be more about how experts (or elites) value or make judgements about what people need or are supposed to want. 13

14  Handbook of quality of life research Objective social indicators were often compiled at various spatial scales and were identified as territorial social indicators for cities and neighbourhoods within cities. They were used to make comparisons between places and identify changes over time (Bork-Hueffer, 2014). Research involving objective indicators also started to focus on the role of urban amenity factors as QOL attractors of migration into a city and as important factors shaping housing markets, explored using hedonic models (for example, Bartik and Smith, 1987; Gyourko et al., 1999). The objective approach was also about identifying people’s individual attributes – such as age, gender, family composition, educational and income levels – and the attributes of neighbourhoods where people live – such as housing density, vegetation coverage and access to public parks – that might impinge on QOL/well-being. The Subjective Approach The subjective approach typically involves the collection of primary data at the disaggregated or individual level using social survey methods. The approach involves self-reporting by respondents to a standardised questionnaire, with people being asked to assess their level of satisfaction with life in general – their happiness, pleasure and fulfilment. The approach is also about people’s behaviour and their assessment (or evaluation) of specific QOL domains (see Deiner and Lucas, 1999; Deiner and Suh, 2000) that relate to things such as health, work, family, social relations, and so forth. Among these domains are components of the residential environment including the dwelling, the neighbourhood, and the broader city or region where people live. The subjective approach might also be about what people want or need to improve their overall QOL/well-being. In contrast to the objective measurement of QOL/well-being indicators, in the subjective assessment of QOL, the importance of a life domain or indicator is not presumed; rather, the subjective approach seeks to identify the perceived significance of a QOL domain or the needs of an individual, something that Diener and Suh (2000) regard as necessary for gaining insight into what people believe is important for their QOL/well-being/happiness. Cummins (2005) identified two basic approaches to the definition and measurement of subjective QOL. The first considers the construct as a single, unitary entity for which Andrews and Withey (1976) devised the widely used measurement instrument consisting of the single question: ‘How do you feel about your life as a whole?’, measured using a Likert scale of life satisfaction/dissatisfaction. The second approach considers subjective QOL/well-being to be composed of discrete domains of life satisfaction. The domains of life approach Many empirical studies have investigated subjective QOL/well-being using the domains of life approach (see Diener and Lucas, 1999; Diener and Suh, 2000). It is evident though that there is no fixed set of those domains. Researchers at the Institute for Social Research (ISR) at the University of Michigan (U-M) (Campbell, Converse and Rodgers, 1976) undertook a seminal study investigating subjective QOL. It involved conducting a sample survey of around 2000 people across the US. It measured people’s perceptions, evaluations and levels of satisfaction with a set of QOL domains (including urban-specific domains, with people’s subjective assessment being measured on Likert scales). The focus was on measuring the global evaluation of life rather than on actual

Approaches to measurement and empirical analysis of quality of life and well-being  15 conditions of life. The approach addressed the concept of satisfaction rather than happiness, which was considered in earlier studies of well-being (such as Bradburn, 1969; Bradburn and Caplowitz, 1965). In the ISR study: (1) satisfaction was viewed as being more definable, and implied judgement or cognitive experience; while (2) happiness reflected a relative short-term mood of elation or gaiety. Significantly, satisfaction was considered a more plausible and realistic objective for policymakers than happiness. The ISR study measured and compared people’s assessments of several life domains as well as their life as a whole. It sought to determine the degree to which each domain might explain people’s assessment of their overall QOL/well-being. The seven domains considered were: ● ● ● ● ● ● ●

health; marriage; housing; family; financial situation; leisure; and community or place of residence.

From a search of the literature investigating QOL, Cummins (2005) identified how some 173 domains had been used in previous studies. Unsurprisingly, there was repeated use of some domain names. He sought to group the domains under the seven broad headings used by the Comprehensive Quality of Life Scale (ComQol), namely: ● ● ● ● ● ● ●

material well-being; health; productivity; intimacy; safety; place in community; and emotional well-being.

The Cummins (2005) review used an analysis of variance approach, finding that 83 per cent of the 173 domains that researchers had used could fit into these seven categories. He concluded that ‘life satisfaction, and therefore subjective well-being, can be economically and validly measured through the seven ComQol domains’ (p. 559). This seems to be a reasonable proposition, especially where the focus of the research in the studies reviewed was predominately on health-related aspects of QOL. An Integrative Approach Monitoring objective indicators of QOL over time may provide information on those aspects of QOL/well-being that are improving or declining. In contrast, survey data generating subjective assessments of QOL domains can provide information at the individual and community level on perceptions, evaluations and levels of satisfaction with various aspects of living, particularly in a situational context such as a city and its neighbourhoods. However, as pointed out by McCrea et al. (2005), while those objective indicators and subjective assessments may be useful, they are also limited because they cannot by themselves indicate the relative impor-

16  Handbook of quality of life research Table 2.1

Examples of QOL indicators that might be used to investigate QOL in cities and neighbourhoods

Objective Indicators

Subjective Indicators

Behavioural Indicators

Employment rates

Housing and neighbourhood satisfaction

Public transit use

Educational attainment

Desire to move

Participation in sports

Per capita income

Perceptions of crime

Amount of walking and bicycling

Crime statistics

Perceptions of school quality

Visits to cultural amenities and events

Domestic violence

Perceptions of healthcare services

Visits to parks

Death rates

Feelings about neighbours

Visits to health clinics/doctors

Incidence of chronic diseases

Feelings about rubbish collection

Amount of neighbouring

Air quality

Feelings about congestion and crowding

Participation in voluntary organisations

Residential density

Feelings about government

Participation in local decision-making

Housing vacancy rates

Satisfaction with health

organisations

Amount of parkland

Satisfaction with family, friends, job, etc.

Residential mobility

Number of public transit riders

Life satisfaction, overall happiness (overall

Distance to transit stop

well-being)

Availability of grocery/food stores Vehicle kilometres/miles travelled

Source: The authors.

tance of the different attributes of a place or an environmental setting that might contribute to the level of satisfaction of individuals with that place or environmental setting. Thus, as discussed by Marans and Stimson (2011b), researchers have developed an integrative approach combining both objective and subjective measures to investigating QOL/ well-being. This approach seeks to investigate the nature and strength of the linkages or interrelationships between objective phenomena about people, their situational setting, and their subjective assessments of QOL in general and of QOL domains. Table 2.1 lists examples of objective and subjective measures that might be used in such an investigation. In addition, the third column in the table indicates the type of behavioural indicators of QOL that might be added to the investigation. Reviewing the evolution of the integrative approach to investigate QOL/well-being, Lora and Powell (2011) suggest that in general there have been two methodological approaches commonly used to combine subjective and objective information, namely: (1) the hedonic approach that employs market prices for housing, which is typically used by economists and regional scientists; and (2) the subjective satisfaction approach, which is typically used by sociologists and psychologists. These approaches can be complementary. In the discussion that follows we concentrate on the latter approach. Focusing on subjective satisfaction in the integrative approach to investigate QOL/ well-being The research conducted at U-M cited earlier (Campbell, Converse and Rodgers, 1976; see also Marans and Rodgers, 1975) used an integrative approach to investigate QOL. The researchers proposed a model of satisfaction with residential environments as a framework for understanding the importance of place in the overall QOL/well-being experience. This work incorporated a range of demographic, social, economic and environmental relationships,

Approaches to measurement and empirical analysis of quality of life and well-being  17 while taking account of people’s satisfaction with different domains of life. That integrative approach rested on four principles: ● The experiences of people are derived from their interactions with the surrounding environment. ● The subjective experiences of people are different from the objective environment. ● People respond to their experiences with the environment. ● The level of satisfaction with various life domains contributes to overall QOL. Satisfaction with living could be viewed at multiple levels of analysis or for different living domains, which might include: ● satisfaction with housing; ● satisfaction with neighbourhood; and ● satisfaction with the wider community (that is, region or metropolitan area). This bottom-up framework made it possible to investigate how urban characteristics (such as perceived crime) might contribute to people’s satisfaction in a specific domain (for example, neighbourhood satisfaction) which, in turn, might contribute to people’s overall satisfaction with life. Thus, paths could be mapped from economic, social and environmental attributes of urban living to satisfaction with different life domains, with those paths being mostly between variables at the same level of analysis. In applying this integrative approach, the U-M researchers were able to demonstrate how it was possible to specify a series of linkages between various objective attributes of each life domain and the satisfaction measures of those domains, which, in turn, might be influenced (or vary) by individual characteristics and individual standards of comparison. A six-dimensional framework Furthering the developing interest in an integrative approach in QOL/well-being research, Pacione (2003) (a geographer) proposed a six-dimensional framework integrating objective, subjective, time, domain specificity, geographic scale and social group dimensions. The framework has the following characteristics: ● Each time slice has objective and subjective planes that relate to objective and subjective measures of QOL. ● Each of those objective and subjective planes has two other dimensions – geographic scale and levels of specificity. ● On the subjective plane, whole life is conceptualised as satisfaction with overall life, which consists of satisfaction across a range of important life domains (such as satisfaction with work, social relationships, community, and so on), as well as satisfaction with explicitly urban domains. The Pacione six-dimensional framework is discussed more fully in Chapter 7. Fulfilling human needs in relation to subjective well-being More recently, Costanza et al. (2008) have proposed a further perspective on the integrative approach to investigate QOL that focuses on the extent to which basic human needs are fulfilled in relation to perceptions of what they called subjective well-being (SWB).

18  Handbook of quality of life research Human needs are assessed by individual or group responses to questions about happiness, life satisfaction, utility or welfare. Perceived satisfaction can be affected by mental capacity, cultural context, information, education and temperament, often in quite complex ways. The relation between the fulfilment of human needs and overall SWB is affected by the (time-varying) weights that individuals, groups and cultures give to fulfilling each human need relative to the others. Costanza et al. (2008) went to great lengths to point out that QOL based on interaction of human needs and the subjective perception of their fulfilment are mediated by opportunities (now and in the future) available to meet those needs. Those opportunities relate to the built, human, social and natural capitals (which provide benefits to enhance QOL) and also to time. Human needs might include subsistence, reproduction, security, affection, understanding, participation, leisure, spirituality, creativity, identity and freedom. SWB (happiness, utility and welfare) is about individuals and/or groups. In the context of QOL studies, human needs have typically been derived from Maslow’s (1954) ‘hierarchy of needs’. This is reflected in numerous attempts to compile QOL scales and instruments, such as: (1) Cummins’s (1997) ComQol; and (2) Greenley et al.’s (1997) Quality of Life Questionnaire. Thus, Costanza et al. (2008) viewed QOL as a multidimensional construct emerging from the evaluation of multiple needs on the individual, community, national and global levels, with QOL at any point in time being a function of: (1) the degree to which each human need is met (fulfilment); and (2) the importance of the need to the individual or group in terms of its relative contribution to their SWB. Assessing QOL at a particular time or for a specific place may be achieved by applying an analytic tool that captures the weighting that a person or group gives to a need and the level of fulfilment of a need, and those may be aggregated to reflect the individual’s or group’s assessment. Integrating the objective and subjective approaches certainly gives a more realistic picture of the importance of variables for improving QOL across both spatial and temporal scales. Costanza et al. (2008) suggest that the integrative approach can help improve our understanding of QOL issues that may be used to guide public policy to enhance QOL across multiple temporal and spatial scales and in different cultural contexts.

SOME MODELLING FRAMEWORKS OPERATIONALISING THE INTEGRATIVE APPROACH Relationships between people’s subjective assessment of their QOL and the objective attributes of their place/space context are complex, but it is clear that people’s assessment of their living in a specific place is influenced by: ● individual and group values, expectations, perceptions and evaluations; ● individual and group demographic and socio-economic characteristics; and ● objective attributes of the place/space setting. It should be obvious that people vary in what they judge to be important when assessing their satisfaction with life in general and with specific QOL domains (Hsieh, 2003). Thus, the nature and complexity of the interrelationships between the above will be idiosyncratic, which helps to explain why researchers have often found a low correlation between individual

Approaches to measurement and empirical analysis of quality of life and well-being  19 subjective evaluations and objective measures of QOL (see, for example, Schwarz and Strack, 1999; Warr, 1987, 1999). As discussed earlier in this chapter, modelling approaches to investigate relationships between subjective evaluations and objective measures have largely evolved from pioneering work conducted in the 1970s at the U-M. Based on data collected through surveys, the researchers developed a series of operational models taking an integrative approach to investigate QOL. These models incorporated people’s subjective assessment of QOL in general and with specific QOL domains, and examined how those might be impacted both by objective characteristics of survey respondents (such as age, socio-economic status) and by objective attributes of living domains at various levels of scale (Campbell, Converse, Rodgers and Marans, 1976; Connerly and Marans, 1985, 1988; Lee and Marans, 1980; Marans, 2003; Marans and Rodgers, 1975). Those models: ● accommodated a range of factors thought to influence people’s levels of satisfaction with QOL domains at various scales (the dwelling, the neighbourhood, the wider city), along with the personal characteristics of people and many objective indicators relating to the living domain environment; ● allowed for the characteristics of a specific satisfaction domain to contribute to satisfaction in another domain; for example, a public transport system may be a characteristic of a city and contribute to the city’s overall quality, but it may also influence neighbourhood satisfaction and people’s ability to move easily throughout a region; and ● made it possible to test the hypothesis that the level of satisfaction in one domain may influence (or colour) satisfaction in other domains – for example, does housing and neighbourhood satisfaction predict community satisfaction – with such links between satisfaction domains being ‘spillover effects’ (Jeffres and Dobos, 1995). Examples of Models Three such models developed by U-M researchers are discussed below: 1. The early model used by Campbell, Converse and Rodgers (1976) focused on analysing relationships between people’s satisfaction with domains of life and life satisfaction in general. The model specified a series of linkages between various objective attributes of each domain of life and people’s satisfaction measures on those domains, which, in turn, could be influenced by a range of individual characteristics of people and individual standards of comparison (Figure 2.1). This bottom-up model could be used, for example, to investigate which urban characteristics (such as perceived crime) might contribute to people’s satisfaction in a specific spatial domain (such as neighbourhood satisfaction) which, in turn, might contribute to overall satisfaction with life. Paths could be mapped from economic, social and environmental characteristics of urban living to satisfaction with different living domains, and those paths would be mostly between variables at the same level of analysis. The model provided for examination of relationships between the various QOL domains and the geographic scales to be analysed. 2. Another model developed by Marans and Rodgers (1975) explicitly focused on living domains at different scales, namely: the dwelling, the neighbourhood and the wider city (or region). The model investigated the relationships between people’s satisfaction with these

20  Handbook of quality of life research

Source: Campbell, Converse and Rodgers (1976).

Figure 2.1

Model showing the relationship between domain satisfaction and life satisfaction

residential domains and their assessment of QOL. It also investigated the role of multiple objective attributes relating to those living domains (Figure 2.2). 3. Later, Marans (2003) developed a model that focused more on specific living domains to investigate relationships between objective conditions, subjective responses to these conditions and a key outcome for a living domain, such as neighbourhood satisfaction (Figure 2.3).

 

Approaches to measurement and empirical analysis of quality of life and well-being  21

Source: Marans and Rodgers (1975).

Figure 2.2

Model showing the relationships between domain satisfaction and quality of life

Note: hh/sq. mi. = households per square mile. Source: Marans (2003).

Figure 2.3

Model showing the relationships between objective conditions, subjective responses and neighbourhood satisfaction

22  Handbook of quality of life research Testing Hypotheses Approaches used to model QOL/well-being have become more focused over time by testing hypotheses about how specific factors might explain a specific QOL outcome. For example, Marans and Mohai (1991) suggested that health might be linked to numerous objective conditions associated with a set of leisure resources, including environmental quality, as illustrated in Figure 2.4. The model proposed that environmental and urban amenities/attributes are related to community quality and individual activities, satisfaction and physical health. For instance: (1) environmental amenities might include both natural recreation resources (for example, rivers, lakes, wetlands, forests) and the quality of the ambient environment (air, water, noise, solid and hazardous waste); (2) urban amenities might include both man-made [sic] recreational resources (swimming pools, cycle parts, walking trails, golf courses) and cultural resources (cinemas, concert halls, orchestras, museums, galleries, sports teams).

Source: Marans and Mohai (1991).

Figure 2.4

A model linking recreation resources and activities to individual well-being, health and community quality

The model suggested that the perceptions or awareness of those environmental and urban amenities influence people’s assessment and their use of those amenities. The model also suggested that, in the case of both the man-made [sic] and the natural recreational resources, their use or non-use by an individual is associated with physical health.

Approaches to measurement and empirical analysis of quality of life and well-being  23 Using models to operationalise the integrative approach to investigate QOL/well-being is discussed in detail in Chapter 7. Enhancing Modelling Using Geographic Information Systems Merschdorf et al. (2020) have demonstrated how an explicit geospatial approach may be used in research to model the relationships between objective and subjective QOL variables in spatial settings. They employed sophisticated multivariate statistical tools including GIS to identify key variables that explain variation in people’s subjective assessments of QOL in general and of specific QOL domains. There is an increasing use of GIS tools in social research, including QOL studies, and it is certain this will become more common in the future. For example, GIS technology has been employed widely in examining issues such as accessibility in urban environments and how proximity to diverse opportunities – such as employment, education, shopping, health and recreation – might directly affect people’s satisfaction with QOL domains (Boarnet et al., 2003; Witten et al., 2003). Readily available GIS tools now facilitate the integration of survey data on subjective QOL at the level of the individual with objective spatial attributes of urban environments. By geocoding the residential location of respondents to QOL surveys, it is possible to integrate: ● survey-based information on individuals’ attitudes, preferences, behaviours and expectations with respect to QOL domains and of aspects of living domains at different spatial scales; ● spatial objective information on the demographic and socio-economic characteristics of the people and households in local areas derived from census data; ● spatially objective environmental data relating to land use, residential density and proximity to urban services and facilities, and natural resources such as parks and water bodies, as well as brownfield sites and noxious industries; and ● community data relating to schools, crime, health, taxes, and so on. The integrative capability of GIS has been employed in numerous QOL studies modelling the relationships between subjective assessments of QOL domains and objective attributes of living domains. An example is a case study of QOL in the Brisbane region in Australia discussed in Chapter 7 (see McCrea et al., 2005). Similarly, several chapters in Marans and Stimson (2011b) demonstrate how GIS tools enhance the capabilities of integrated models to investigate QOL in urban settings.

CONCLUSION The broad overview of QOL research (also encompassing research on well-being, as well as happiness) provided in this chapter and Chapter 1 demonstrates: (1) how it comprises both a subjective and an objective approach; and (2) how this work has evolved to integrate those approaches to investigate the complex relationships between people’s subjective assessments of QOL in general and of specific QOL domains, people’s characteristics, and the objective attributes of place and space at different geographic scales.

24  Handbook of quality of life research Investigating QOL/well-being explicitly in a place or space context using the integrative approach has led to the development of models to analyse those interrelationships. These models have been enhanced through advances in statistical modelling and more recently, the use of GIS tools. Certainly, QOL/well-being studies have been experiencing something of a resurgence of interest. This resurgence is driven in part by the research community and new tools available to them. The increasing reliance on data by policymakers as well as governmental planners and managers is also a prompting an interest in such studies.

REFERENCES Andelman, R., Board, R. and Carman, L. et al. (1998), Quality of Life Definition and Terminology: A Discussion Document from the International Society for Quality-of-Life Studies, Blacksburg, VA: International Society for Quality-of-Life Studies. Andrews, F.M. and Withey, S.B. (1976), Social Indicators of Well-Being: Americans’ Perspectives of Quality of Life, New York: Plenum Press. Bartik, T.J. and Smith, V.K. (1987), ‘Urban amenities and public policy’, in E. Mills (ed.), Handbook of Regional and Urban Economics: Volume 2, Urban Economics, Amsterdam: North-Holland, pp. 1207–54. Boarnet, M.G., Greenwald, M. and McMillan, T.E. (2003), ‘Planning and health promotion: quantifying links between land use, walking, and physical activity’, unpublished manuscript. Bork-Hueffer, T. (2014), ‘Intra-urban and interurban quality of life approaches’, in A.C. Michalos (ed.), Encyclopedia of Quality of Life and Well-being, Dordrecht: Springer, pp. 3371–5. Bradburn, N.M. (1969), The Structure of Psychological Well-Being, Chicago, IL: Aldine. Bradburn, N.M. and Caplowitz, D. (1965), Reports on Happiness, Chicago, IL: Aldine. Campbell, A., Converse, P.E. and Rodgers, W. (1976), The Quality of American Life: Perceptions, Evaluations and Satisfactions, New York: Russell Sage Foundation. Campbell, A., Converse, P.E., Rodgers, W. and Marans, R.W. (1976), ‘The residential environment’, in A. Campbell, P.E. Converse and W. Rodgers (eds), The Quality of American Life: Perceptions, Evaluations and Satisfactions, New York: Russell Sage Foundation, pp. 217–66. Connerly, C. and Marans, R.W. (1985), ‘Comparing global measures of neighborhood quality’, Social Indicators Research, 45, 29–47. Connerly, C. and Marans, R.W. (1988), ‘Neighborhood quality: a description and analysis of indicators’, in E. Huttman and W. Vleit (eds), Handbook on Housing and the Built Environment in the United States, Westwood, CO: Greenwood Press, pp. 37–61. Costanza, R., Fisher, B. and Ali, S. et al. (2008), ‘An integrative approach to quality of life measurement, research and policy’, S.A.P.I.EN.S., 1, 17–21. Cummins, R.A. (1997), Comprehensive Quality of Life Scale: Adult (5th edition, ComQol-A-5), Burwood, VIC: Deakin University School of Psychology. Cummins, R.A. (2005), ‘The domains of life satisfaction: an attempt to order chaos’, Social Indicators Research, 26, 559–84. Diener, E. and Lucas, R. (1999), ‘Personality and subjective well-being’, in D. Kahneman, E. Diener and N. Schwarz (eds), Well-Being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation, pp. 213–29. Diener, E. and Suh, E.M. (eds) (2000), Culture and Subjective Well-being, Cambridge, MA: MIT Press. Duncan, O.D. (1969), Towards Social Reporting: Next Steps, New York: Russell Sage Foundation. Greenley, J.R., Greenberg, J.S. and Brown, R. (1997), ‘Measuring quality of life: a new and practical survey instrument’, Social Work, 42, 244–54. Gyourko, J., Kahn, M. and Tracy, J. (1999), ‘Quality of life and environmental conditions’, in P. Cheshire and E.S. Mills (eds), Handbook of Regional and Urban Economics: Volume 3, Applied Urban Economics, Amsterdam: North-Holland, pp. 14135–54.

Approaches to measurement and empirical analysis of quality of life and well-being  25 Hsieh, C. (2003), ‘Counting importance: the case of life satisfaction and relative domain importance’, Social Indicators Research, 61, 227–40. Innes de Neufville, J. (1975), Social Indicators and Public Policy: Interactive Processes of Design and Application, Amsterdam: Elsevier. Jeffres, L.W. and Dobos, J. (1995), ‘Separating people’s satisfaction with life and public perceptions of the quality-of-life in the environment’, Social Indicators Research, 34, 181–211. Lee, T. and Marans, R.W. (1980), ‘Subjective and objective indicators: scale discordance and interrelationships’, Social Indicators Research, 6, 47–64. Lora, E. and Powell, A. (2011), ‘A new way of monitoring the quality of urban life’, IDB Working Paper Series No. IDB-WP-272, Inter-American Development Bank. Marans, R.W. (2003), ‘Understanding environmental quality through quality of life studies: the 2001 DAS and its use of subjective and objective indicators’, Landscape and Urban Planning, 65, 73–83. Marans, R.W. and Mohai, P. (1991), ‘Leisure resources, recreation activity, and the quality of life’, in B.L. Driver, P. Brown and G.L. Peterson (eds), The Benefits of Leisure, State College, PA: Venture Publishing, pp. 351–63. Marans, R.W. and Rodgers, W. (1975), ‘Towards an understanding of community satisfaction’, in A. Hawley and V. Rock (eds), Metropolitan America in Contemporary Perspective, New York: Halsted Press, pp. 299–352. Marans, R.W. and Stimson, R.J. (eds) (2011a), Investigating Quality of Urban Life: Theory, Methods and Empirical Research, Dordrecht: Springer. Marans, R.M. and Stimson, R.J. (2011b), ‘An overview of quality of urban life’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods and Empirical Research, Dordrecht: Springer, pp. 1–32. Maslow, A. (1954), Motivation and Personality, New York: Harper & Row. McCrea, R., Stimson, R.J. and Western, J. (2005), ‘Testing a general model of satisfaction with urban living using data for South East Queensland, Australia’, Social Indicators Research, 72, 121–52. Merschdorf, H., Hodgson, M.E. and Blaschke, T. (2020), ‘Modelling quality of urban life using a geospatial approach’, Urban Science, 4, 5. Mukherjee, R. (1989), Quality of Life: Valuation in Social Research, New Delhi: SAGE. Pacione, M. (2003), ‘Quality-of-life research in urban geography’, Urban Geography, 24, 314–39. Rossi, P.H. (1972), ‘Community social indicators’, in A. Campbell and P.E. Converse (eds), The Human Meaning of Social Change, New York: Russell Sage Foundation, pp. 87–126. Schwarz, N. and Strack, F. (1999), ‘Reports of subjective well-being: judgmental processes and their methodological implications’, in D. Kahneman, E. Diener and N. Schwarz (eds), Well-Being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation, pp. 61–84. Smith, D.M. (1973), The Geography of Social Well-being in the United States: An Introduction to Territorial Social Indicators, New York: McGraw Hill. Smith, D.M. (1977), Human Geography: A Welfare Approach, London: Edward Arnold. Smith, D.M. (1979), Where the Grass is Greener: Living in an Unequal World, London: Penguin. Warr, P.B. (1987), Work, Unemployment, and Mental Health, Oxford: Oxford University Press. Warr, P.B. (1999), ‘Well-being and the workplace’, in D. Kahneman, E. Diener and N. Schwarz (eds), Well-Being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation, pp. 392–412. Witten, K., Exeter, D. and Field, A. (2003), ‘The quality of urban environments: mapping variation in access to community resources’, Urban Studies, 40, 161–77.

3. Investigating happiness: a socio-spatial inequalities perspective1 Thanasis Ziogas and Dimitris Ballas

INTRODUCTION ‘Happiness’ is a multifaceted concept and has been examined by researchers in various fields across the social sciences. Psychologists, economists, geographers and policymakers have tried to measure happiness to understand its drivers and consequences (see Bok, 2010; Diener, 2000; Frey and Stutzer, 2002; Layard, 2005; Lyubomirsky, 2008). The research builds on long, lively philosophical debates relating to the conceptual and operational definition of happiness and in the context of well-being/quality of life (QOL). Happiness and life satisfaction measures are often used interchangeably, being considered elements of the broader notion of QOL. For example, the distinction between the hedonic and eudaimonic nature of happiness may be traced to ancient Greece and Aristotle (Helliwell and Barrington-Leigh, 2010). The former deals with instantaneous fulfilment and immediate pleasure, often described as a hedonistic approach, as in the work of Bentham (Kahneman et al., 1997). The latter is more overarching, dealing with life satisfaction in terms of overall fulfilment and is based on a eudaimonic approach widely related to the writings of Aristotle. With regard to measurement, however, there are two main components in defining happiness or well-being/QOL: the objective and the subjective approach (as discussed in Chapter 2). In happiness research, the major distinction is that: (1) the subjective approach is based on what people register, their subjective evaluations, regarding their happiness; while (2) the objective approach is based on some exogenous characteristics that can be quantified and are assumed to confer happiness, with such characteristics typically related to material conditions and QOL, including environmental quality and the level of gross domestic product (GDP) in a country (Organisation for Economic Co-operation and Development [OECD], 2011). Apart from the objective or subjective measures of well-being, geography poses new challenges regarding its role in questions pertaining to happiness and QOL research. The relative importance of neighbourhoods, areas or regions, as well as certain attributes of places, have been included as determinants of happiness. From a regional science perspective, for example, Ballas and Tranmer (2012) and Aslam and Corrado (2012) examined the level of geographical aggregation for which happiness matters, arguing the need to take account of the hierarchy across the spatial levels using multilevel modelling techniques, that is: ● ● ● ●

individuals are nested into households; households are nested into neighbourhoods; neighbourhoods form cities; and cities and their hinterlands constitute countries.

The role of geography – and especially the impact of small local areas like neighbourhoods – is highlighted in publications including the World Happiness Reports,2 with a focus on issues 26

Investigating happiness: a socio-spatial inequalities perspective  27 relating to social trust, corruption, and physical and mental health. Furthermore, the distinction between urban and rural areas, as well as other factors such as population density, public amenities and green spaces, are included in the geography of happiness domain (Mouratidis, 2019; White et al., 2017). This chapter reviews the literature on the geography of happiness and its relationship to well-being/QOL along with research by economists on happiness, including the relative income hypothesis (RIH). We focus on the impact of social and spatial inequalities. We formulate a methodological framework for analysing the geography of subjective well-being and happiness in the UK over a period of eight years, using longitudinal data derived from the Understanding Society study.3 Results from that work are presented, and their implications are discussed.

PERSPECTIVES ON HAPPINESS AND WELL-BEING Economics In economics there has been a boom in studies analysing data on happiness (Clark, 2018). Many studies focus on the ‘Easterlin paradox’ (Easterlin, 1974, 1995, 2001). On the one hand, this suggests that within countries there is a positive relationship between income and happiness, with richer people reporting higher levels of subjective well-being (SWB). On the other hand, between-country comparisons show that the wealthiest countries do not necessarily have higher scores in happiness rankings – that is, as countries’ wealth increase, happiness levels do not necessarily follow the same trend. This counterintuitive observation triggered a wave of research trying to answer the paradox (Clark et al., 2008; Di Tella and MacCulloch, 2008; Oishi and Kesebir, 2015). A different strand of happiness research in economics investigates labour market implications. In the interplay between job opportunities, satisfaction with job, productivity and unemployment, happiness and mental distress play a central role (Böckerman et al., 2020; Clark, 2003; Ferris et al., 2010; Oswald et al., 2015). Geographical Perspectives There is a growing number of geographical studies of happiness, ranging from international comparisons to regional and local analyses (Ballas, 2013; Ballas and Dorling, 2012). An example of international comparisons using national averages is the annual World Happiness Report. Such comparisons across 155 countries use questions of life evaluations that are reflective assessments on a person’s life using 11 response categories (the Cantril ladder: 0–10) averaged over the period 2014–16. On aggregate, the results show that Scandinavian countries rank high (Norway, Denmark and Finland typically being among the top five) with an average value close to 7.5 points, while the bottom-ranking countries are mostly in Africa with a life satisfaction score under 3.5 points. There are now many examples of studies that geographically disaggregate the analysis of happiness within nations and within regions, with the overall spatial nature of happiness and well-being/QOL also receiving considerable attention (Ala-Mantila et al., 2018; Ballas, 2021; Rijnks, 2020). Many geographical aspects are identified as factors affecting the levels of SWB

28  Handbook of quality of life research or happiness, including environmental aspects of areas or regions (Brereton et al., 2008) – such as green spaces and coastal proximity (Garrett et al., 2019; White et al., 2017) – and the importance of location for entrepreneurial well-being (Abreu et al., 2018). Exploring Socio-spatial Interdependencies Some studies investigating happiness have explicitly attempted to explore spatial and social interdependencies between people and places simultaneously. This may be important from both a theoretical and a policy perspective. The evolving literature explores the role of status and social comparisons, and includes the following: 1. In the late nineteenth century, Veblen (1899) highlighted the importance of relative social status, arguing that the consumption behaviour of an individual is not independent of the other individuals’ consumption. 2. Several decades later, Duesenberry (1949) introduced the relative income hypothesis (RIH) concept, suggesting that the other individuals’ consumption affects an individual’s own consumption. 3. Subsequent theoretical models have modified the functional form of the utility function to take account of different types of interactions and comparisons: a. Kapteyn and Van Herwaarden (1980) and Kapteyn et al. (1978) developed models for interdependent welfare functions to empirically test the relationship between social welfare and income distribution, suggesting that neglecting those interdependencies can lead to biased conclusions. b. Akerlof (1997) used social distance to explain interactions between individuals as a function of their initial position, using that framework to explain social decisions made by different social classes. c. Arrow and Dasgupta (2009) developed similar models adding a dynamic dimension in the utility function where relative consumption matters not only today but also in the future, with utility functions being identified as offsetting the trade-off between consuming more today at the expense of consuming less in the future. Such theoretical papers are also directly related to the literature of relative deprivation (see Merton, 1968) where social status and comparisons affect people’s decisions and mindset. Promising Areas for Research Layard (2006) suggested that research in welfare economics might draw more on psychology, proposing three ‘simple’ models of social comparisons, adaptation and tastes, pointing out the importance of interactions that are often overlooked. From an economics of happiness perspective, an important edited book by Rojas (2019) places an emphasis on RIH where the position of individuals relative to their peers is analysed. The starting point of comparisons is the income that others receive. RIH is illustrated by considering this question: ‘Would an individual feel better off if she earns $2000 in a region where everyone else earns $1000 or in a region where she earns $3000 while everyone else earns $6000?’ Evidence suggests that individuals care about both the absolute and relative income – namely, the income of their reference group (see Ball and Chernova, 2008; Ballas, 2013, 2021; Brodeur and Fleche, 2019;

Investigating happiness: a socio-spatial inequalities perspective  29 Deaton and Stone, 2013; FitzRoy et al., 2014; Frank, 2007; Hou, 2014; Ifcher et al., 2018; Luttmer, 2005; Rijnks et al., 2019). Another promising avenue for further research might be the role of social media on well-being of adolescents (Beyens et al., 2020) and adults (Coyne et al., 2020) as a mechanism facilitating emotional contagion (Coviello et al., 2014; McDool et al., 2020; Shakya and Christakis, 2017).

INVESTIGATING HAPPINESS IN THE UK Building on the approaches discussed above, researchers have investigated the changing nature of QOL and well-being in the UK, analysing data from the Understanding Society study (Clark and Oswald, 1996; Dustmann and Fasani, 2016). Here we discuss the results of a temporal and geographic analysis of variations in happiness for UK regions focusing on potential demographic and socio-economic determinants. The Data We use the data generated by the 12 questions of the UK General Health Questionnaire (GHQ-12) as a proxy for the happiness levels (subjective mental well-being) of individuals, which capture an aspect of individuals’ QOL. The GHQ-12 comprises the following set of questions: Have you recently: Been able to concentrate on whatever you are doing? Lost much sleep over worry? Felt that you are playing a useful part in things? Felt capable of making decisions about things? Felt constantly under strain? Felt you couldn’t overcome your difficulties? Been able to enjoy your normal day-to-day activities? Been able to face up to your problems? Been feeling unhappy and depressed? Been losing confidence in yourself? Been thinking of yourself as a worthless person? Been feeling reasonably happy all things considered?

Individuals answered using a four-point scale running from ‘strongly disagree’ to ‘strongly agree’. We reversed all the questions needed so that higher values correspond to higher levels of happiness, with the resulting measure ranging from 0 to 36, where 36 is the maximum ‘happiness’ that someone can achieve, as an all-encompassing measure to capture people’s level of SWB, solving much of the focal-values problem discussed in Barrington-Leigh (2018), which shows there are no focal values4 in the distribution of GHQ-12. Figure 3.3 shows there are no focal values in the distribution of GHQ-12. The first eight waves of the Understanding Society survey are used, which provide a balanced panel dataset of the same individuals 16 years and older (N = 16 890) spanning from 2009 to 2016 (T = 8), giving a total of approximately 135 000 observations. The survey

30  Handbook of quality of life research contained various socio-economic and demographic characteristics of individuals and their families. In addition to the GHQ-12 variable, the variables we use as explanatory variables of individuals’ QOL often appear as a standard formation of SWB (Brodeur and Fleche, 2019; Ifcher et al., 2018). Specifically: ● a categorical variable shows the employment status of individuals with the following categories: ● in paid employment; ● whether they are self-employed; ● retired; ● unemployed, etc.; ● a dummy variable is used for gender and a variable for marital status; ● other demographic variables used are age and age squared; ● dummy variables are used for: ● urban/rural regions; ● whether individuals have a university degree; ● whether they face any long-standing illness; and ● finally, we construct an economic status variable based on the individuals’ family income. Regarding the income variable, we argue that household income should be used instead of personal/individual income. Thus, the personal incomes of all individuals living in the same household are combined, thus assuming all individuals living in the same household benefit from their salary and that of others. From a geographical perspective, the sample individuals are nested in the four countries of the UK: England, Northern Ireland, Scotland and Wales. This forms the first geographical unit of analysis for which the reference group (average area income and relative income defined as the income gap) is created. The same individuals also reside in the 12 Government Office Regions (GORs) forming a second layer of geographic data for the reference group, namely: North East, North West, Yorkshire and the Humber, East Midlands, West Midlands, East of England, London, South East, South West, Wales, Scotland and Northern Ireland. Figure 3.1 shows the trend of the SWB variable across GORs. The regions move together over time with respect to the GHQ-12 variable. The highest score for regions is 26.56 observed in 2009 and in 2011 for Scotland and the South West. Figure 3.2 shows the trend of household income (logarithmic) across all GORs. In accordance with the Easterlin paradox, the income of all regions was rising between 2009 and 2016, while levels of SWB as measured by GHQ-12 (Figure 3.1) did not trend upwards. Methodology Given that a panel dataset is used, a decision was needed regarding the use of random versus fixed effects. There are arguments favouring both techniques (see Allison, 2009). The Hausman test (1978) suggested that we should use fixed effects. However, given that fixed effects do not give estimates for time-invariant characteristics of the individuals, we adopted both frameworks as the two procedures give conflicting results when we do not include time fixed effects (year dummies) in the specification. After their inclusion, the results are qualita-

Investigating happiness: a socio-spatial inequalities perspective  31

Source: The authors.

Figure 3.1

Plotting of GHQ-12 across GORs for the 2009–16 period

tively the same. Apart from the individual fixed effects, we control for country/region fixed effects by including dummy variables for those geographical entities in our specifications. Given the limitations in constructing the social cycle of an individual, the average income either of the country or of the region in which the individual resides seemed to be a good approximation for the reference group. Following Luttmer (2005), the average area (mean) income of GORs and countries was thus created. However, although Luttmer (and others) have used average area income as a proxy for relative income, this measure does not capture inequality. In addition, the average income has no variability between individuals in the same geographical unit since any two individuals in the same unit have the same average income as each other. For that reason, apart from considering the average income of a region as the reference group, we also considered the difference between the household income and average income (we call it the income gap) as an alternative variable. Our analysis uses both variables in different specifications. Both aggregate area income variables (area average income and income gap) are log-transformed. For the income gap variable, the problem that arises for the log transformation concerns the negative values that arise due to the subtraction. This is solved using the inverse hyperbolic sine (IHS) transformation, which is similar to the logarithmic transformation while at the same time allowing for negative and zero values (Bellemare et al., 2013; Burbidge et al., 1988).

32  Handbook of quality of life research

Source: The authors.

Figure 3.2

Plotting of household income (logarithmic transformation) across GORs for the 2009–16 period

The baseline econometric specification used is as follows: ​GHQ​ (12) ​​ it​ ​​= ​a0​  ​  +   ​β1 * Employment_Status​it  ​  +   ​β2 * Gender​i ​  +   β3 * ​Marital_Status​it  ​  +   ​β4 * Age​it  ​  +   β5​ ​* ​Ag ​e​ 2​ it​  +   β6 * ​Education​it  ​  +   β7 * ​Illness​it  ​  +   ​β8 * Area​it  ​  +   β9 * ​Household_Income​it  ​  ​+   ​ (β10 * ​Aggregate_Area_Income​it  ​) ​  +   ​ (​μ​ i​) ​  +   ​ (​λ​ t​) ​  +   errorit

​ (3.1) ​​

As noted above, the Aggregate_Area_Incomeit variable has two alternative definitions: (1) area (region or country) average; and (2) income gap (the difference between the household income and the area average income). Other variables considered when expanding the baseline specification include a dummy variable capturing the economic class of individuals to examine whether any income group displays a particular pattern. Geographical dummies are also introduced to address geographical fixed effects. All analyses were conducted using the software Stata 16 and QGIS 3.10.

Investigating happiness: a socio-spatial inequalities perspective  33 Empirical Results Gender and employment status differences In the sample data derived from the Understanding Society study, 56.5 per cent of individuals are females and the mean age of all individuals is 51.9 years. Figure 3.3 shows the distribution of the GHQ-12 variable and the distributions for females and males. There is a statistically significant difference between the genders in favour of males who score higher in the questionnaire. The mean GHQ-12 value for females is 25.67 and for males 26.67, while it is 26.1 for the entire sample. Figure 3.4 shows the distribution of the same variable but this time across various employment statuses. There are striking differences in GHQ-12 scores depending on the employment status of individuals. Retired individuals register high values along with self-employed and those being in paid employment. The results are not encouraging for unemployed individuals and especially for those who are sick or disabled as they are at the bottom of the distribution, rather distant from the other statuses.

Source: The authors.

Figure 3.3

Histogram of GHQ-12 between females and males

Figures 3.5 and 3.6 show the distribution of GHQ-12 scores across the 12 GORs and the spatial evolution for three distinct years (2009, 2012 and 2016), respectively. The maps show the highest scoring regions are in the north and south of the UK, while central regions rarely

34  Handbook of quality of life research

Source: The authors.

Figure 3.4

Smoothed kernel histogram of GHQ-12 across employment status

belong to the highest cluster (26.32–26.56). For the entire period, when data is aggregated, there are no significant differences regarding the distribution of GHQ-12 across GORs (Figure 3.5). Regression modelling We now turn to the main analysis using regression modelling. Table 3.1 carries a standard specification of SWB regression. The variables included were those discussed earlier. The table is divided into three panels each for a different methodological framework: ● the pooled ordinary least squares (OLS) model; ● the random effects model; and ● the fixed effects model. The results are qualitatively the same regardless of the methodology. For example, being unemployed reduces the well-being score up to 2.51 points. However, retired individuals score higher values in the GHQ-12 variable between 0.151 and 0.406 points. Key findings are the following: ● Married individuals do better in terms of well-being compared to divorced and widowed people and this difference is statistically significant.

Investigating happiness: a socio-spatial inequalities perspective  35

Source: The authors.

Figure 3.5

Histogram of GHQ-12 across GORs (average 2009–16)

● Age exhibits a U-shaped relationship with the dependent variable as the variable age is negative and significant, while the variable age squared is positive and significant, and this is consistent with previous findings (see Blanchflower, 2021; Cheng et al., 2017; Clark, 2018). ● The dummy variable capturing differences between rural and urban areas is significant in the first two panels, suggesting that the association between SWB and rural areas is positive. ● The income variable is positive and significant under all three frameworks; however, the effect is not very strong since the variable is log-transformed – for example, using fixed effects, a 1 per cent increase in income will result a 0.00163 increase in GHQ-12 variable. In Table 3.2, three new dummies are introduced to the specification. The first is a categorical economic class variable based on the household income variable. Individuals are assigned to one of the ten categories of the economic class variable according to their incomes. In the first category, individuals belonging to the poorest 10 per cent are assigned. Accordingly, in the tenth category are individuals that belong to the richest 10 per cent. The variable is used to proxy for the economic status of individuals. The other two variables are a dummy for countries and a dummy for the GORs.

Figure 3.6

Spatial distribution of GHQ-12 in 2009, 2012 and 2016

Source: The authors.

36  Handbook of quality of life research

Investigating happiness: a socio-spatial inequalities perspective  37 Table 3.1

Baseline regression

Dependent Variable: GHQ-12

Pooled OLS

Coeff.

t-stat.

Coeff.

 

 

 

 

 

 

0.194***

3.80

0.136**

(2.02)

0.130

(1.56)

–2.515***

(–23.45)

–2.064***

(–18.46)

–1.716***

(–14.43)

0.406***

(7.98)

0.151**

(2.28)

0.125

(1.62)

–0.916***

(–12.04)

–0.845***

(–9.29)

–0.676***

(–6.46)

–5.745***

(–43.97)

–4.107***

(–24.55)

–2.763***

(–14.54)

–0.821***

(–27.86)

–0.831***

(–14.71) (1.04)

Self-employed  

 

Retired

 

Family care

Sick or disabled

Fixed Effects

t-stat.

Employment Unemployed

Random Effects

Coeff.

 

t-stat.

Gender

Female

Marital status

Married

0.128**

(2.45)

0.340***

(4.10)

0.146

 

Divorced

–0.450***

(–5.89)

–0.193*

(–1.67)

0.130

(0.80)

 

Widowed

–0.160**

(–2.01)

–0.352***

(–2.68)

–0.862***

(-4.27)

Age

Age

–0.047***

(–7.45)

–0.079***

(–7.72)

–0.088***

(-4.18)

 

Age2

0.000***

(12.13)

0.001***

(10.03)

0.001***

(4.10)

Education

Degree

0.201***

(3.58)

0.325***

(3.15)

–0.519

(–1.29)

Illness

Long-standing

–2.009***

(–61.35)

–1.068***

(–30.01)

–0.626***

(–16.58)

Area

Rural

0.197***

(6.13)

0.239***

(4.01)

0.185

(1.42)

Income (log)

Household

0.280***

(10.45)

0.185***

(6.00)

0.163***

N Adjusted R2

(4.65)

121 663

121 663

121 663

0.123

0.118

0.014

Note: t-statistics in parentheses. Robust standard errors. * p < 0.1, ** p < 0.05, *** p < 0.01. Source: The authors.

Table 3.2

Economic class, GORs and country dummies from random effects regression

Dependent Variable: GHQ-12 Baseline specification including economic class Coeff.

t-stat.

 

Coeff.

t-stat.

–0.278**

(–2.49)

7th class

0.071

(1.31)

2nd class

–0.111

(–1.35)

8th class

0.101*

(1.74)

3rd class

–0.020

(–0.29)

9th class

0.111*

(1.66)

4th class

–0.132**

(–2.18)

10th class

0.181**

(2.14)

5th class

0.046

(0.86)

 

 

Reference group: 6th class 1st class

Baseline specification including GOR dummies Reference group: London

Coeff.

t-stat.

 

Coeff.

t-stat.

North East

0.013

(0.08)

South East

0.161

(1.44)

North West

0.131

(1.09)

South West

0.149

(1.19)

Yorkshire and the Humber

0.054

(0.42)

Wales

0.087

(0.53)

East Midlands

0.206

(1.63)

Scotland

0.237*

(1.67)

–0.312**

(–2.36)

Northern Ireland

0.241

(1.45)

0.138

(1.15)

 

 

 

West Midlands East of England

Baseline specification including country dummies Reference group: England

Coeff.

t-stat.

 

 

Wales

0.015

(0.10)

 

 

Scotland

0.161

(1.40)

 

 

Northern Ireland

0.162

(1.14)

 

 

Note: Random effects regressions. t-statistics in parentheses. Robust standard errors. * p < 0.1, ** p < 0.05, *** p < 0.01. Source: The authors.

38  Handbook of quality of life research Table 3.3

Baseline regression including area average income

Dependent Variable: GHQ-12   Income (log)

Household

Country average

Pooled OLS t-stat.

Coeff.

0.308***

(11.39)

0.219***

–1.551***

Country fixed effect N

 

Adjusted R2  

Income (log)

Household

Region fixed effect N Adjusted R2

(–7.40)

–1.224***

Coeff.

Fixed Effects t-stat. (6.98) (–6.13)

Coeff.

t-stat.

0.154***

(4.41)

3.046***

(5.55)

Yes

Yes

Yes

121 663

121 663

121 663

0.124

  Regional average

Random Effects

Coeff.

t-stat.

0.119 Coeff.

0.015

t-stat.

Coeff.

t-stat.

0.316***

(11.58)

0.216***

(6.88)

0.151***

(4.33)

–1.406***

(–6.78)

–1.077***

(–5.48)

2.748***

(5.90)

Yes

Yes

Yes

121 663

121 663

121 663

0.125

0.119

0.015

Note: t-statistics in parentheses. Robust standard errors. *p < 0.1, ** p < 0.05, *** p < 0.01. Source: The authors.

The table is used to examine whether any spatial units or socio-economic groups are either higher or lower in terms of an SWB aspect of QOL. The same specification as in Table 3.1 is used, and each time, one of the dummy variables is included. The results from the top panel of Table 3.2 show that the economic class dummies are negative for the first four categories (bottom 40 per cent of income) and positive for the remaining five clusters. Interestingly, the bottom 10 per cent in the income distribution is significantly lower (–0.278), at a 5 per cent significance level, compared to the reference category, which is the individuals belonging in the 50–60 per cent cluster. However, the top 10 per cent in the income distribution is significantly higher (0.181) at the same level of significance (5 per cent). The analysis suggests not only the presence of asymmetric effects in terms of the signs, but also in terms of magnitude between the two extreme clusters in the economic class variable. Regarding the geographical dummies, the results suggest that among the 12 regional dummies, only people from the West Midlands score lower at a 5 per cent significance level and that people from Scotland score slightly higher at a 10 per cent significance level (also supported in Figures 3.1 and 3.6). When the analysis is conducted using country dummies, there is no statistically significant difference between them. An F-test that GORs are jointly equal to zero rejects the null hypothesis. However, an F-test for countries fails to reject the null. But, in the subsequent analysis, we use both dummies in the regression analysis for the models to be comparable in terms of the specification. Tables 3.3 and 3.4 expand the basic specification to allow for average area and relative income effects. Table 3.3 includes the average income either of the country or region (following Luttmer, 2005), while in Table 3.4, a relative income variable is introduced as the difference (income gap) between household income and average income either of the country or region. Again, there are three panels, each for the different methodology adopted. The rest of the individual level variables are not presented, however, the results are qualitatively the same. The pattern emerging in Tables 3.3 and 3.4 is that household income remains positive and significant regardless of the specification or method used while: (1) the average area income (country or region) is negatively associated under the pooled OLS and random effects framework and positively associated under fixed effects; and (2) relative income (income gap) displays exactly the opposite pattern in terms of signs. However, qualitatively the results are

Investigating happiness: a socio-spatial inequalities perspective  39 Table 3.4

Baseline regression including relative income (income gap) (inverse hyperbolic sine transformation)

Dependent Variable: Self-reported

Pooled OLS

Random Effects

Fixed Effects

Well-being  

Coeff.

Income (log)

Household

Income gap (in relation to country

0.173*** 0.013***

t-stat. (4.39) (4.16)

Coeff. 0.146*** 0.006*

t-stat. (3.68) (1.72)

Coeff.

t-stat.

0.185***

(4.29)

–0.003

(–0.94)

average) Country fixed effect N

 

Adjusted R2 Income (log)

Yes

Yes

Yes

121 663

121 663

121 663

0.124 Household

Income gap (in relation to regional

0.119

0.006

0.176***

(4.42)

0.149***

(3.73)

0.188***

(4.36)

0.014***

(4.37)

0.005

(1.63)

–0.004

(–1.08)

average) Region fixed effect N Adjusted R2

Yes

Yes

Yes

121 663

121 663

121 663

0.125

0.119

0.059

Note: t-statistics in parentheses. Robust standard errors. * p < 0.1, ** p < 0.05, *** p < 0.01. Source: The authors.

Table 3.5

Baseline regression including area average income and time effects

Dependent Variable: Self-reported

Pooled OLS

Random Effects

Fixed Effects

Well-being   Income (log)

Household

Country average

Coeff.

t-stat.

Coeff.

0.309***

(11.42)

0.220***

0.966

(0.54)

0.501

t-stat. (7.02) (0.33)

Coeff.

t-stat.

0.154***

(4.40)

0.394

(0.25)

Country fixed effect

Yes

Yes

Time fixed effect

Yes

Yes

Yes

121 663

121 663

121 663

N

 

Adjusted R2 Income (log) Regional average

0.125 Household

Yes

0.119

0.069

0.317***

(11.63)

0.219***

(6.95)

0.151***

(4.31)

1.745*

(1.92)

1.623**

(2.13)

1.647**

(2.15)

Region fixed effect

Yes

Yes

Time fixed effect

Yes

Yes

Yes

121 663

121 663

121 663

0.126

0.119

0.066

N Adjusted R2

Yes

Note: t-statistics in parentheses. Robust standard errors. * p < 0.1, ** p < 0.05, *** p < 0.01. Source: The authors.

the same between the two tables because increases in the income gap variable (that is, household income minus average income) suggest the household is getting richer compared to the average of the area. It is worth noting that depending on the framework (random or fixed effects), an individual’s SWB correlates either positively or negatively with the average income of the geographical unit. Apart from the different signs between random and fixed effects, Table 3.4 shows that there is a difference in magnitude (almost in half) in the relative income coefficients between pooled OLS and random effects. However, as Table 3.5 shows, when time dummies are introduced in regression’s specification, the signs of the average area income between random and fixed effects are always positive, while they are significant only in the case of average income

40  Handbook of quality of life research at the regional level. This is consistent to the literature where for low level geographical scale, average income effects display positive association with individuals’ subjective measures of well-being (Brodeur and Fleche, 2019; Ifcher et al., 2018).

DISCUSSION This chapter has overviewed some of the research approaches being taken in economics and in geography to investigate one aspect of individuals’ QOL – namely, happiness. Life satisfaction and SWB measures are key components of QOL, with studies showing a correlation between such measures and longevity (Frey, 2011). The GHQ-12 used as a dependent variable in the analysis reported here is a 12-item measure to gauge individuals’ subjective (mental) well-being. Multi-item measures arguably do a good job in capturing people’s emotions as random errors that might accrue from single-item measures could average out (Spector, 1992). In particular, the role of social and spatial inequalities in SWB and happiness was discussed, presenting the results of an analysis of panel data – derived from the UK Understanding Society study – using regression modelling to investigate individual, household and contextual determinants of SWB. After introducing demographic and socio-economic controls, and using the London city region as the reference category, the modelling showed that UK regions appear to have a statistically different level of average SWB: (1) Scotland appears to have a statistically significant higher level of SWB than London; while (2) the region of the West Midlands has a statistically significant lower level of SWB. The modelling considered the impact of area (region and country level) average household income upon SWB: (1) under the random effects framework, there was a negative effect of area average income upon individuals’ SWB; while (2) under fixed effects there was a positive effect. A negative effect would be consistent with some of the pertinent studies reviewed in the chapter (especially Luttmer, 2005). However, a positive effect would be consistent with the analyses at low geographical scale by Brodeur and Fleche (2019) and Ifcher et al. (2018). Speculatively, that positive effect could be the result of the possible association between higher average area income with higher quality of services and other amenities at the local and regional level (which may benefit the whole population regardless of income and social status) and/or possible collective feelings of optimism about future economic prospects for all individuals in the area (which would be consistent with Clark and Senik, 2010). Nevertheless, those conflicting results converge and are positive, and are thus more consistent with the work of Brodeur and Fleche (2019) and Ifcher et al. (2018) when time fixed effects dummies are introduced to the specification. Our analysis also included a measure of relative income (the difference between household income and area income), but no statistically significant associations with well-being at the geographical scales used were found.

POLICY IMPLICATIONS The modelling framework adopted may be used as a basis for further work (including an analysis of measures that are associated with income and wealth inequality) at various

Investigating happiness: a socio-spatial inequalities perspective  41 geographical scales that could inform policy-relevant discussions in relation to income comparisons and the impact of inequality upon well-being and happiness (for example, Dorling, 2016; Wilkinson and Pickett, 2010, 2018). Given the effect on SWB from the income of others, governments and policymakers might take account of the effects of income comparisons on people’s happiness. From the research reviewed and the modelling presented, it might be argued that even if the GDP of a country moves upwards, it will not necessarily improve the happiness of all individuals. Given that individuals relate to one another, a distribution-neutral shift seems to have no effect on people’s happiness levels as the comparisons remain as before. For example, in the analysis of the UK data, SWB measures were neither evenly distributed across regions, nor across employment statuses, nor across economic status variable. In that context, it would be interesting to examine how relative income effects are experienced based on the location of individuals or their employment status. It might be relevant to develop and implement policies aimed at specific sub-populations of individuals and deprived areas to address the impacts of income comparisons (Helliwell, 2019). To that end, further research is needed with respect to the mechanisms underlying the social and spatial disparities of well-being and happiness and its determinants. Even if an increase in the general level of income is being observed, policymakers first need to examine the nature of such an increase (that is, which individuals benefited the most) as well as to support the individuals who negatively experience income comparisons. The support towards those individuals can be materialised either by means of subsidies or other social transfers (for example, the introduction of a basic income scheme; see Torry, 2018). Finally, from a methodological perspective there is potential to extend the analysis of SWB data and socio-spatial and demographic determinants using spatial regression (for example, building on Rijnks et al., 2019 and Rijnks, 2020), and to also use new methods and data in the field of geoinformatics and geographical information systems (see Ballas, 2021).

NOTES 1. The authors thank the editors, anonymous referees and the participants of the 5th International Conference on Applied Theory, Macro and Empirical Finance (AMEF) for their comments and suggestions. They acknowledge the access granted to the UK Household Longitudinal Study dataset, made available through the Economic and Social Research Council (ESRC) Data Archive. 2. See https://​worldhappiness​.report/​archive/​(accessed September 2020). 3. University of Essex, Institute for Social and Economic Research (2018), Understanding Society: Waves 1–8, 2009–2017 and Harmonised BHPS: Waves 1–18, 1991–2009 [Data collection], 11th edition, UK Data Service, SN: 6614, http://​doi​.org/​10​.5255/​UKDA​-SN​-6614​-13. 4. In most survey questions about subjective measures of well-being, we often observe clear spikes at the highest value, middle value and lowest value of the potential responses. This phenomenon is often described as ‘focal values’ (Barrington-Leigh, 2018).

REFERENCES Abreu, M., Oner, O., Brouwer, A. and Van Leeuwen, E. (2018), ‘Well-being effects of self-employment: a spatial inquiry’, Journal of Business Venturing, 44, 278–90. Akerlof, G.A. (1997), ‘Social distance and social decisions’, Econometrica, 65, 1005–27.

42  Handbook of quality of life research Ala-Mantila, S., Heinonen, J., Junnila, S. and Saarsalmi, P. (2018), ‘Spatial nature of urban well-being’, Regional Studies, 52, 959–73. Allison, P.D. (2009), Fixed Effects Regression Models, London: SAGE. Arrow, K.J. and Dasgupta, P.S. (2009), ‘Conspicuous consumption, inconspicuous leisure’, Economic Journal, 119, F497–F516. Aslam, A. and Corrado, L. (2012), ‘The geography of well-being’, Journal of Economic Geography, 12, 627–49. Ball, R. and Chernova, K. (2008), ‘Absolute income, relative income, and happiness’, Social Indicators Research, 88, 497–529. Ballas, D. (2013), ‘What makes a “happy city”?’, Cities, 32(Suppl.), S39–S50. Ballas, D. (2021), ‘The economic geography of happiness’, in K. Zimmerman (ed.), Handbook of Labor, Human Resources and Population Economics, Cham: Springer. Ballas, D. and Dorling, D. (2012), ‘The geography of happiness’, in S.A. David, I. Boniwell and A. Conley Ayers (eds), Oxford Handbook of Happiness, Oxford: Oxford University Press, pp. 465–81. Ballas, D. and Tranmer, M. (2012), ‘Happy people or happy places? A multi-level modelling approach to the analysis of happiness and well-being’, International Regional Science Review, 35, 70–102. Barrington-Leigh, C. (2018), ‘A critique of the econometrics of happiness: are we underestimating the returns to education and income?’, arXiv, 1807.11835. Bellemare, M., Barrett, C. and Just, D. (2013), ‘The welfare impacts of commodity price volatility: evidence from rural Ethiopia’, American Journal of Agricultural Economics, 95, 877–99. Beyens, I., Pouwels, J.L. and Van Driel, I.I. et al. (2020), ‘The effect of social media on well-being differs from adolescent to adolescent’, Scientific Reports, 10, Article 10763. Blanchflower, D.G. (2021), ‘Is happiness U-shaped? Age and subjective wellbeing in 145 countries’, Journal of Population Economics, 34, 575–624. Böckerman, P., Bryson, A., Kauhanen, A. and Kangasniemi, M. (2020), ‘Does job design make workers happy?’, Scottish Journal of Political Economy, 67, 15–52. Bok, S. (2010), Exploring Happiness: From Aristotle to Brain Science, New Haven, CT: Yale University Press. Brereton, F., Clinch, J.P. and Ferreira, S. (2008), ‘Happiness, geography and the environment’, Ecological Economics, 65, 386–96. Brodeur, A. and Fleche, S. (2019), ‘Neighbors’ income, public goods and well-being: evidence from a multi-scale analysis’, Review of Income and Wealth, 65, 217–38. Burbidge, J.B., Magee, L. and Robb, A.L. (1988), ‘Alternative transformations to handle extreme values of the dependent variable’, Journal of the American Statistical Association, 83, 123–7. Cheng, T., Oswald, A.J. and Powdthavee, N. (2017), ‘Longitudinal evidence for a midlife nadir in human well-being: results from four data sets’, Economic Journal, 127, 126–42. Clark, A.E. (2003), ‘Unemployment as a social norm: psychological evidence from panel data’, Journal of Labor Economics, 21, 323–51. Clark, A.E. (2018), ‘Four decades of the economics of happiness: where next?’, Review of Income and Wealth, 64, 245–69. Clark, A.E., Frijters, P. and Shields, M. (2008), ‘Relative income, happiness and utility: an explanation for the Easterlin paradox and other puzzles’, Journal of Economic Literature, 46, 95–144. Clark, A.E. and Oswald, A.J. (1996), ‘Satisfaction and comparison income’, Journal of Public Economics, 61, 359–81. Clark, A.E. and Senik, C. (2010), ‘Who compares to whom? The anatomy of income comparisons in Europe’, Economic Journal, 120, 573–94. Coviello, L., Sohn, Y. and Kramer, A.D.I. et al. (2014), ‘Detecting emotional contagion in massive social networks’, PLOS ONE, 9, Article e90315. Coyne, S.M., Rogers, A.A. and Zurcher, J.D. et al. (2020), ‘Does time spent using social media impact mental health? An eight year longitudinal study’, Computers in Human Behavior, 104, Article 106160. Deaton, A. and Stone, A.A. (2013), ‘Two happiness puzzles’, American Economic Review, 103, 591–7. Di Tella, R. and MacCulloch, R.J. (2008), ‘Gross national happiness as an answer to the Easterlin Paradox?’, Journal of Development Economics, 86, 22–42. Diener, E. (2000), ‘Subjective well-being: the science of happiness and a proposal for a national index’, American Psychologist, 55, 34–44.

Investigating happiness: a socio-spatial inequalities perspective  43 Dorling, D. (2016), A Better Politics: How Government Can Make Us Happier, London: London Publishing Partnership. Duesenberry, J.S. (1949), Income, Saving and the Theory of Consumer Behavior, Cambridge, MA: Harvard University Press. Dustmann, C. and Fasani, F. (2016), ‘The effect of local area crime on mental health’, Economic Journal, 126, 978–1017. Easterlin, R.A. (1974), ‘Does economic growth improve the human lot? Some empirical evidence’, in P.A. David and M.W. Reder (eds), Nations and Households in Economic Growth: Essays in Honor of Moses Abramovitz, New York: Academic Press. Easterlin, R.A. (1995), ‘Will raising the incomes of all increase the happiness of all?’, Journal of Economic Behavior and Organization, 2, 35–47. Easterlin, R.A. (2001), ‘Income and happiness: towards a unified theory’, Economic Journal, 111, 465–84. Ferris, D.L., Lian, H. and Pang, F.X.J. et al. (2010), ‘Self-esteem level and job performance: the moderating role of self-esteem contingencies’, Personnel Psychology, 63, 561–93. FitzRoy, F., Nolan, M.N., Steinhardt, M.F. and Ulph, D. (2014), ‘Testing the tunnel effect: comparison, age and happiness in UK and German panels’, IZA Journal of European Labor Studies, 3, Article 24. Frank, R. (2007), Falling Behind: How Rising Inequality Harms the Middle Class, Berkeley, CA: University of California Press. Frey, B.S. (2011), ‘Happy people live longer’, Science, 331, 542–3. Frey, B.S. and Stutzer, A. (2002), Happiness and Economics, Princeton, NJ: Princeton University Press. Garrett, J., White, M.P. and Huang, J. et al. (2019), ‘Coastal proximity and mental health among urban adults in England: the moderating effect of household income’, Health & Place, 59, Article 102200. Hausman, J.A. (1978), ‘Specification tests in econometrics’, Econometrica, 46, 1251–72. Helliwell, J.F. (2019), ‘Measuring and using happiness to support public policies’, NBER Working Paper No. w26529, National Bureau of Economic Research. Helliwell, J.F. and Barrington-Leigh, C. (2010), ‘Viewpoint: measuring and understanding subjective well-being’, Canadian Journal of Economics/Revue canadienne d’économique, 43, 729–53. Hou, F. (2014), ‘Keep up with the Joneses or keep on as their neighbours: life satisfaction and income in Canadian urban neighbourhoods’, Journal of Happiness Studies, 15, 1085–107. Ifcher, J., Zarghamee, H. and Graham, C. (2018), ‘Local neighbors as positives, regional neighbors as negatives: competing channels in the relationship between others’ income, health, and happiness’, Journal of Health Economics, 57, 263–76. Kahneman, D., Wakker, P.P. and Sarin, R. (1997), ‘Back to Bentham? Exploration of experienced utility’, Quarterly Journal of Economics, 112, 375–405. Kapteyn, A. and Van Herwaarden, F.G. (1980), ‘Independent welfare functions and optimal income distribution’, Journal of Public Economics, 14, 375–97. Kapteyn, A., Van Praag, B.M.S. and Van Herwaarden, F.G. (1978), ‘Individual welfare functions and social preference spaces’, Economics Letters, 1, 173–7. Layard, R. (2005), Happiness: Lessons from a New Science, New York: Penguin. Layard, R. (2006), ‘Happiness and public policy: a challenge to the profession’, Economic Journal, 116, C24–C33. Luttmer, E.F.P. (2005), ‘Neighbors as negatives: relative earnings and well-being’, Quarterly Journal of Economics, 120, 963–1002. Lyubomirsky, S.J. (2008), The How of Happiness, New York: Penguin Press. McDool, E., Powell, P., Roberts, J. and Taylor, K. (2020), ‘The internet and children’s psychological wellbeing’, Journal of Health Economics, 69, Article 102274. Merton, R.K. (1968), Social Theory and Social Structure, revised and enlarged edition, New York: Free Press. Mouratidis, K. (2019), ‘Compact city, urban sprawl, and subjective well-being’, Cities, 92, 261–72. Oishi, S. and Kesebir, S. (2015), ‘Income inequality explains why economic growth does not always translate to an increase in happiness’, Psychological Science, 26, 1630–38. Organisation for Economic Co-operation and Development (OECD) (2011), How’s Life? Measuring Well-Being, Paris: OECD Publishing.

44  Handbook of quality of life research Oswald, A.J., Proto, E. and Sgroi, D. (2015), ‘Happiness and productivity’, Journal of Labor Economics, 33, 789–822. Rijnks, R. (2020), ‘Subjective well-being in a spatial context’, PhD thesis, University of Groningen. Rijnks, R., Koster, S. and McCann, P. (2019), ‘The neighbour’s effect on well-being: how local relative income differentials affect resident’s subjective well-being’, Tijdschrift voor Economische en Sociale Geografie, 110, 605–21. Rojas, M. (ed.) (2019), The Economics of Happiness, Cham: Springer. Shakya, H.B. and Christakis, N.A. (2017), ‘Association of Facebook use with compromised well-being: a longitudinal study’, American Journal of Epidemiology, 185, 203–11. Spector, P. (1992), Summated Rating Scale Construction: An Introduction, Newbury Park, CA: SAGE. Torry, M. (2018), Why We Need a Citizen’s Basic Income, Bristol: Policy Press. Veblen, T. (1899), The Theory of Leisure Class: An Economic Study in the Evolution of Institutions, Boston, MA: Houghton Mifflin. White, M.P., Pahl, S. and Wheeler, B.W. et al. (2017), ‘Natural environments and subjective wellbeing: different types of exposure are associated with different aspects of wellbeing’, Health & Place, 45, 77–84. Wilkinson, R. and Pickett, K. (2010), The Spirit Level: Why Equality is Better for Everyone, London: Penguin. Wilkinson, R. and Pickett, K. (2018), The Inner Level: How More Equal Societies Reduce Stress, Restore Sanity and Improve Everyone’s Well-being, London: Penguin.

4. Understanding environmental impacts on people’s quality of life via environmental psychology: three basic principles Marino Bonaiuto and Valeria Chiozza

INTRODUCTION The world’s population is rapidly urbanising, with more than half living within an urban context. This trend will continue and accelerate, exerting a range of effects on people. Positive effects include satisfying essential human needs such as lower levels of child malnutrition, access to electricity, clean cooking and heating fuels, and improved sanitation and drinking water (Ritchie and Roser, 2018). Urban areas also offer greater cultural and educational opportunities. But urbanisation does have adverse effects at many levels within the urban system, including an array of individually and collectively undesirable threatening consequences ranging from stressful and unsafe settings to pollution affecting people’s quality of life (QOL). This chapter overviews the positive or negative effects that natural and urban environments exert on people, demonstrating the specific effects of urban features and urban planning. The effects may be direct as well as being mediated (or moderated) by other factors. A meaningful process and outcome framework for environmental features and person characteristics is proposed based on three main principles through which environment–person transactions impact people – namely, the structure, the process and the timing of environment–person effects – which impact individual well-being and their QOL.

THREE BASIC PRINCIPLES IN ENVIRONMENT–PERSON TRANSACTIONS Many historical examples testify that the urban environment and its planning can profoundly affect inhabitants’ QOL and their environmental perception (for historical background, see Bonaiuto, 2020). We now have detailed knowledge about the mechanisms behind such effects. Studying the three principles mentioned above contributes to explaining the person– environment relationships at an environmental psychology level of analysis (following Bonaiuto, 2020), delineating when, why and how specific environmental peculiarities impact individuals’ well-being and QOL.

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46  Handbook of quality of life research First Principle About the Structure: Valence, Generalisability, Set (Positive, General, Simple vs Negative, Relative, Cumulative Effects) In terms of structure, the environment can exert on people different effects following three parameters: valence, generalisability and set: ● Valence concerns several individually and socially desirable – almost universal – outcomes, such as: (1) a person’s well-being, physical or intellectual development; and (2) their social relations. ● Generalisability refers to the positive or negative outcome of a particular environment on a specific person that can be general (that is, constant and independent from other variables) or relative (that is, moderated by such other variables). ● Set indicates that these can be either: (1) a simple effect – that is, one single environmental feature positively or negatively affects a specific outcome(s) in people; or (2) a composite (cumulative) effect – that is, a constellation of environmental features positively or negatively affects a specific outcome(s) in people. A simple case A simple case that epitomises the single positive effect of the environment on the person is the well-known effect of natural environments restoring the psychological functions of humans, primarily by allowing stress reduction and cognitive restoration, but also by prompting affective restoration in terms of ‘a positive affect, life satisfaction, happiness, feelings of vitality, and a sense of meaning and purpose in life’ (Venhoeven et al., 2018, p. 89). Put simply, the passive or active experience of nature (that is, viewing or acting in) exerts physical and psychological positive effects that typically result from green spaces (such as vegetation elements) and blue ones (such as water flows or surfaces). Some examples A few examples from the literature – both general and within Centro Interuniversitario di Ricerca in Psicologia Ambientale1 (CIRPA) projects – illustrate the categories mentioned above. For the sake of simplicity, a broader reference to restoration and restorativeness is made to comprise the positive effects of nature, natural places and natural features of a place. However, at a more detailed level, a distinction among different processes can be appropriate to deepen the relevant process knowledge (see Bonaiuto and Albers, 2023), such as: ● mitigation (that is, positive outcome in terms of risk buffering); ● restoration (that is, positive outcome in terms of cognitive or affective recovery); and ● instoration (that is, positive outcome in terms of acquisition of new states or abilities). Bakolis and colleagues (2018) developed a smartphone app called ‘The Urban Mind’ to help plan and design healthier cities by monitoring the influence of the surrounding built environment on subjective well-being. It uses a methodology known as ecological momentary assessment (EMA), which involves the repeated sampling of experiences and behaviours in real-time and in real-world contexts (Shiffman et al., 2008). The app was used to analyse the exposure to natural features within the built environment associated with mental well-being changes in 108 participants (age range 20–67 years) over a week. To examine the extent to which vulnerability to mental health conditions defines the effect of the surrounding built environment

Environmental impacts on quality of life via environmental psychology  47 on psychological well-being, the study included an evaluation of trait impulsivity, which is a measure of the tendency to behave with poor foresight, thinking or consideration of the consequences (Mayhew and Powell, 2014). The results showed that being outdoors and feeling connected with nature – such as seeing trees, hearing birds or contemplating the sky – were linked to increased levels of temporary mental well-being. Those beneficial effects remained even when participants were no longer outside and no longer had access to nature, suggesting a lasting influence, even after several hours. However, the study did not detect a consistent effect for blue spaces. These findings confirm what Amicone and colleagues (2018) found when investigating the effects of spending the school break (either in the morning or in the afternoon for 4th- and 5th-grade pupils) in green areas (vs built ones). A self-report scale was used measuring pupils’ perceived restorativeness along with several standard cognitive performance tests measuring different attention components. Results show that pupils spending their daily recess time in green (vs built) areas not only describe themselves as psychologically more restored just afterwards, but they also perform significantly better in both the sustained and selective attention test and the memory test. Given that this is a general, non-moderated (that is, irrespective of pupils’ age, gender, hour of the day, recess type sequence) effect, such a result has implications for both design and management, suggesting that school yards be designed with green areas. It also requires that schools let pupils spend recess time outdoor in such green areas. When that is the case, the environment facilitates pupils’ psychological condition, helping them regain the proper cognitive functioning mode required to cope with the subsequent educational efforts necessary for improved learning, thus enhancing educational and intellectual development. Such a result is evident in a broader meta-analysis highlighting a range of beneficial effects that nature exposition has in improving children’s affective, cognitive and behavioural self-regulation (Weeland et al., 2019), thus further generalising such a phenomenon in terms of virtuous person–environment process and outcome. These examples support the idea that short-term exposure to nature induces assessable favourable outcomes on well-being, complementing existing evidence that examined cumulative long-term exposure to nature (see Evans, 2003; Kaplan, 2001). Therefore, a key policy priority for urban development might be to plan greener cities, introducing specific natural features that could help to make cities healthier and happier places to live. Urban trees and forests have been shown to provide numerous advantages such as cooling the air and filtering urban pollutants, increasing biodiversity, improving physical and mental health, and increasing property values (Tyrväinen et al., 2005). A cumulative risk model Unfortunately, many urban contexts are far from gaining the above qualities. Particularly in low socio-economic communities, neighbourhood environmental characteristics can impact residents’ lives in terms of physical factors (for example, decreased food availability, poor air quality, lack of green spaces or residential crowding) and psycho-social conditions (for example, lack of safety and security, low social cohesion and chronic discrimination) (Evans, 2003; Mair et al., 2008). Experiencing multiple environmental stressors can cause health problems by accumulating risk factors, independently of the presence or absence of specific risk indicators (Huang et al., 2018). This is called the cumulative risk model (CRM). While no single risk factor by itself increases a negative risk outcome, the presence of an increasing

48  Handbook of quality of life research number of risk factors contributes to a corresponding increase in the adverse outcome (Rutter, 1979). The effect of multiple factors is thus cumulative; as the number of risk factors experienced by a person increases, the likelihood of adverse outcomes also increases (Evans, 2003, 2004). Because cities are multi-place complex environments (Bonnes and Secchiaroli, 1992) and many factors influence the perception of urban security, CRM is an appropriate framework for studying insecurity perception and well-being in the urban context. Traditionally, the research focus has been put on each feature – whether spatial or social environmental factors and community factors (Gifford, 2014) – to weigh it as a potential single risk factor capable of inducing a negative effect on urban safety and security perception. However, applying a typical developmental psychology model characterised by a systemic approach requires adopting a different perspective. It becomes necessary to consider that a crucial factor for safety is not simply represented by a specific intrinsic characteristic of a single element but rather by several features, each bearing its own risk potential. Thus, risk factors may be present together, cumulating their individual effects on the person experiencing that place. In a CIRPA project (Bilotta et al., 2019), the CRM model has been used to study urban perceived safety and security and well-being for the first time. Inhabitants of Rome were sampled from three different neighbourhoods, pre-selected to represent neighbourhoods located at the lowest, intermediate and highest ranking in terms of perceived urban safety and security. Measures were gathered regarding both the type and number of risk factors – spatial and social – perceived in the neighbourhood (the predictors), and the person’s evaluation of their insecurity/fear of crime as well as their rate of one’s well-being/life satisfaction (the criteria). Two studies were conducted to test the CRM on perceptions of insecurity/fear of crime and well-being/life satisfaction by employing two methodological techniques: (1) measuring both predictors and criteria within the same sample; and (2) measuring predictors and criteria, respectively, with two independent samples. With both techniques, the result is stable and straightforward. A linear proportional relationship between the number of perceived (either subjectively or consensually) risk factors in the neighbourhood and the perception of the inhabitants’ insecurity and fear of crime. Furthermore, there is a linear inverse relationship between the perceived risk factors and the perception of well-being and life satisfaction. This double linear trend shows how the inhabitants can cope with the threat represented by a few risk factors in their own neighbourhood (up to three or four) until those risk factors cumulate over and above a certain number (over four or five). Within a positive psychology framework, such an environmental effect model can also be conceived in terms of positive valence by assuming the cumulative virtuous effect of several environmental benefits added together (thus, a cumulative benefit model, CBM; see Becker et al., 2011). A further ongoing development at CIRPA within a laboratory setting is experimental testing of whether the careful manipulation of a mix of both negative and positive factors (imposed upon a neutral urban landscape serving as a framework) can alter people’s automatic reactions (as measured by physiological parameters). Preliminary results show that as the ratio of benefit/risk factors increases, so do both automatic emotional reaction and cognitive processing. These results corroborate the evidence that people immediately and automatically react to urban features and suggests assessing the impact on users to understand which urban design and management choices could maximise the benefits on inhabitants’ well-being (Benita and Tunçer, 2019).

Environmental impacts on quality of life via environmental psychology  49 Aspects of the generalisability parameter: a moderation model The examples presented so far focused on the different effects of a particular environment on a specific person concerning valence (positive and negative) and set (simple and cumulative). It is now necessary to explain the generalisability parameter aspect. Environmentally produced effects are not always straightforward from X to Y (see Figure 4.1). Sometimes they can be conditioned upon some distinct elements or qualities of the space that can potentially influence, or in more specific terms moderate, the outcome (for example, health and well-being). A moderation model involves at least three variables: ● the independent variable (IV); ● the dependent variable (DV); and ● the moderator variable (MoV). The hypothesis is that the effect of IV on DV is influenced by the third variable that regulates or moderates (that is, changes) the relationship between the first two variables (Figure 4.2).

Source: The authors.

Figure 4.1

Positive, single, general effects case

In moderation analysis, the objective is to identify those variables that highlight when and under what circumstances an effect of the independent variable on the dependent variable is observed – that is, the moderation variable limits the generalisability of the effect. For example, proximity to blue spaces has been linked with many benefits, such as stress reduction, improved social contacts and increased physical activity (White et al., 2020). However, these associations seem to be moderated by the aesthetic value and appearance of the blue space (for example, cleanliness vs the presence of litter and degradation). Therefore, to guarantee health and well-being benefits, waters should be clean, and the level of pollution or contamination of the blue space should be minimal (Bonaiuto and Albers, 2023). On the other hand, there is differential access to space and resources in urban contexts. It is likely that neighbourhoods with a higher presence of nature are also inhabited by residents with good socio-economic status (SES). In this regard, McEachan and colleagues (2016) investigated the relationship between the presence of green spaces within the neighbourhood (IV) and depressive symptoms in pregnant women (DV), focusing on the moderating roles of SES (MoV). Results have shown a negative association between neighbourhood greenery and

50  Handbook of quality of life research

Source: The authors.

Figure 4.2

Negative, cumulative, relative effects case

the likelihood of experiencing depressive manifestations, with more substantial associations for less well-off women. It is suggested that lower socio-economic groups may encounter greater benefits from the contribution that nature offers in terms of space for physical activity and stress reduction compared to their wealthier counterparts, since they may have fewer opportunities for mobility and therefore spend more time in their areas interacting with their neighbourhood environment. Implications for planning In terms of urban planning, the examples offered above suggest the need to consider green spaces (and blue spaces studied by other contributions) as crucial resources for inhabitants and neighbourhoods to promote and moderate physical and psychological well-being in urban contexts, especially for underserved communities. Furthermore, recent research has highlighted the existence of some solutions that, at a neighbourhood scale, favour walkability, with significant consequences in the reduction of risky behaviours (such as sedentariness, obesity) and in the incidence of chronic diseases related to unhealthy lifestyles (Saelens et al., 2003). Second Principle About the Process: Direct and Indirect (Mediated) Effects The effects the environment exerts on people also differ in terms of the mechanism, according to how an environmental feature succeeds in influencing, positively or negatively, a person’s life. This link between a predictor and a criterion can be either direct or indirect: (1) when direct, the mere exposure to the environment has an effect, under certain conditions; (2) when indirect, the environmental feature triggers a process involving more than one consequence – namely, a chain of variables ordered in a (supposedly) causal sequence. In both cases, it is possible to have either general effects or conditional effects depending upon other factors

Environmental impacts on quality of life via environmental psychology  51 (either of the environment or of the person), as argued above, when discussing the general vs moderated effect. A mediation model In a mediation model, the assumption is that the effect of the independent variable (IV) on the dependent variable (DV) is not direct; rather it is indirectly exerted via the effect of an intermediate third variable (i.e., mediating variable, MeV) which makes the relationship between IV and DV either possible at all (total mediation) or simply stronger (partial mediation). In this case too, therefore, there are at least three variables: the IV, the DV and the MeV. Thus, the (simple) mediation model predicts that the process by which a variable X affects Y can be described as follows: (1) X affects MeV, MeV has an effect on Y; and (2), therefore, X has an effect on Y because of MeV’s intervention. A mediated effect is crucial, not only from a merely scientific point of view, but also in terms of applied implications deriving from a scientific knowledge. Knowing in greater detail the process by which a specific environmental feature – or set of features – exerts a specific effect on health and QOL allows not only a better understanding of the causal links, but it also helps in planning interventions to transform such knowledge into practice – that is, in creating evidence-based design (or management, or policy). This means that if knowledge is acquired about the fact that, for example, green areas are exerting positive effects on human health because they increase people’s physical activity, it is important to have natural areas that maximise the chances of physical activity. Another implication could be that any independent variable, apart from green areas, that positively affect such a mediating variable (namely, physical activity) is worthy of consideration (Figure 4.3).

Source: The authors.

Figure 4.3

Mediated effects case

Examples One example is the so-called ‘forest bathing’ phenomenon where walking for a few hours in the woods or forest (IV) proves to be psychologically and physiologically restoring (DV) in terms of mental relaxation, generating positive emotions (Bielinis et al., 2018) and alleviating stress (Chen et al., 2018). Studies seeking to identify the reasons behind these effects found that physiological processes are sometimes induced by specific chemical substances (such as phytoncides) produced by certain trees (such as Japanese cedar), particularly during certain seasons (for example, spring), and inhaled by the individual during walking (Guan et al., 2017; Li, 2010). Thus, implementing specific kinds of green spaces, rather than nature in general, and encouraging people to walk in them could improve positive mental health and well-being. However, as previously mentioned, cities involve many stressors that can potentially undermine a person’s health and thus negatively impact their QOL. Although the precise molecular mechanisms that associate urban and neighbourhood conditions with altered physiological

52  Handbook of quality of life research profiles are not yet fully clarified (Reuben et al., 2020; Smith et al., 2017), gene regulation has been proposed as a mediator for several health issues (Dunn, 2020; Reuben et al., 2020). Environmental exposures can alter gene expression and phenotype through epigenetic mechanisms such as deoxyribonucleic acid (DNA) methylation, histone modification and non-coding ribonucleic acids (RNAs) (Rutten and Mill, 2009; Smith et al., 2017). Despite not directly editing the DNA sequence, epigenetic mechanisms can regulate gene expression through chemical modifications of DNA bases and alterations to the chromosomal structure in which DNA is packaged (Al Aboud et al., 2023). Through these processes, genes can be activated or deactivated – that is, in some cases, the gene is turned off – it is still there, but it is silent; in other cases, some chemical molecules make the DNA easy to transcribe and ramp up protein production (Moore et al., 2013). Although epigenetic changes are part of healthy development, they are also implied in many conditions impacting QOL, including cardiometabolic issues (Estampador and Franks, 2014) and inflammation (Bayarsaihan, 2011). Several studies (including Giurgescu et al., 2019; Smith et al., 2017) have suggested that different factors within the residential neighbourhood can influence the chemical tags that activate and deactivate genes (for example, exposure to chemicals and outdoor and indoor pollution, unavailability of physical spaces, lack of nutritional education). This hypothesis explains why a longitudinal study by Lippert et al. (2017) reported worse health status for adults raised in socio-economically disadvantaged neighbourhoods compared to their counterparts from wealthier areas, regardless of family income and education and whether parents have significant diseases. In addition, the persistent psychological distress caused by those circumstances can lead to more unhealthy behaviours, such as insufficient physical activity and a poorer diet (Diez Roux and Mair, 2010), which may in turn impact and disrupt the endocrine and inflammatory response systems (Quach et al., 2017; Rimmele et al., 2009), causing altered patterns of stress-related hormones (Karb et al., 2012) and inflammatory biomarkers (Roe et al., 2013). Further evidence comes from sibling twin design studies. Even if monozygotic twins share 100 per cent of their gene pool, meaning they are genetically identical, they might turn out very different during the lifespan, even in traits that have a significant genetic component. For example, one twin may develop asthma at some point in their life, while the other does not (Runyon et al., 2012). The reason relies upon any non-shared environmental exposure they experience while growing up (such as food quality, smoking, toxin exposure) that may contribute to the twin discordance, affecting how they age and their disease susceptibility (Czyz et al., 2012; Petronis, 2010). Reuben and colleagues (2020) explored the association between neighbourhood socio-economic disadvantage and DNA methylation by age 18. Researchers derived epigenetic data from participants’ blood samples. To assess neighbourhood disadvantage, they considered four neighbourhood characteristics: deprivation, physical decay, disconnection and dangerousness. Residents were also asked to evaluate problems in the neighbourhood, such as garbage, abandoned public spaces and safety perception. Results have shown that children raised in neglected areas presented epigenetic differences in the regulation of genes related to inflammation, exposure to tobacco smoke and metabolism of air pollutants, as compared to those peers who had cleaner air and were more socially connected, safe and well supported.

Environmental impacts on quality of life via environmental psychology  53 Implications In the above examples, the peculiarities of the urban environment that represented the IV (X), produce an effect in the gene regulation process (MeV), which in turn has an effect on the psycho-physiological well-being considered as the DV (Y). Therefore, it can be stated that the urban context – including both environmental and social features – affects well-being because of an epigenetic’s intervention. This combination of findings has important implications for urban planning and policy interventions. There is the risk that the changeless lack of social and physical resources within disadvantaged communities can threaten people’s well-being, emphasising pre-existing difficulties and underlying health issues (Kim et al., 2018). Third Principle About the Timing: Short- and Long-term Exposure, with Immediate and Chronic Effects A third way in which the effects of the environment on people can differ is the time lag between the exposure and the subsequent effect. The latter can be delayed by a few milliseconds or minutes, hours, days, weeks, months, or even years and decades. In the context of urban environments, the extent to which individuals are affected by environmental conditions (such as poor air quality) strongly depends on the time spent in the area (Kleinepier and Van Ham, 2018). Probably three main kinds of cases are possible: (a) a short-time exposure having either immediate effects (case #1) or long-term effects (case #2); or (b) a long-term exposure resulting in long-term chronic effects due to the cumulative temporal exposure (case #3). Understanding how the adverse health effects of cities can be prevented and counteracted is vital. As the planet became hotter and more crowded, air pollution has emerged as a fundamental issue for global health (see Chapter 8 on air quality and QOL in Hong Kong). However, this is not just a modern-day concern, as experts have been aware of the danger of air pollution since the seventeenth century (Fuller, 2019). In 1661, John Evelyn, a fellow of the Royal Society of London, published Fumifugium, one of the first works on air pollution. The pamphlet was explicitly addressed to King Charles II of England and tackled air pollution in the capital city, an issue dating back to medieval times (Brimblecombe, 1976). Evelyn referred to the burnt sea coal that released harmful substances such as sulphur dioxide, carbon dioxide, soot and particulates of organic matter into the atmosphere, giving off an unpleasant smell. Jacobson (2012) has suggested that burning wood would have been less damaging to the lungs and urged moving some of the more polluting industries outside the capital. The growing industrialisation that arose in the late 1700s made conditions even worse. The combination of water vapour and particulates released by coal-burning factories started to produce dark and heavy clouds that reduced visibility. The further spread of the Industrial Revolution and the rapid growth of the city made air pollution peak in the nineteenth century due to the increasing domestic fires and industrial furnaces. It could last a week, and despite the deterioration of public health, little was done to keep air quality under control (Brimblecombe, 1976). The Great Smog of London The first policies only began to occur in the late 1950s (Williams, 2004), when uncontrolled emissions from factories combined with the smoke from half a million house fires resulted in a phenomenon known as ‘The Great Smog of London’ in 1952, a lethal combination of smoke and fog covering the city for five days. Even if the smoke would have been normally dispersed into the atmosphere, in those days, the Azores anticyclone shifted its zone of influence to

54  Handbook of quality of life research the North Atlantic, causing a temperature inversion over London. This meteorological event produced a dense layer of cold and stagnant air that became trapped under a layer of warmer air, causing a total lack of ventilation and air exchange. The thermal inversion pushed the saturated air upwards, generating an extended fog layer that seriously impacted visibility, forcing the city to a near standstill. The severe weather conditions amplified the phenomenon, as the inhabitants were forced to increase their coal consumption for domestic heating (Polivka, 2018). The health consequences of the Great Smog were immediate, particularly in terms of respiratory and cardiac issues. The smoke primarily derived from the sulphur dioxide emitted from the burning coal (Wang et al., 2016), an inorganic compound that can irritate the respiratory system, causing breathing problems and affecting the mucous membranes, the skin and the eyes (Chen et al., 2007; Vale, 2007). Detrimental effects on health were prompt (case #1): hospital admissions increased 48 per cent in the first week, and respiratory disease admissions increased by 163 per cent (Polivka, 2018). Wilkins (1954) estimated in excess of 4000 deaths during the fog and the following two weeks, as well as a further 8000 deaths during the next three months. Mortality was mainly due to chronic bronchitis and emphysema worsened by chemical irritants and particulate matter. The government responded with the Clean Air Act in 1956, establishing smoke-free areas throughout the cities and restricting coal burning in domestic fires and industrial furnaces. Homeowners were strongly incentivised to shift to alternative heating sources, such as oil, natural gas and electricity (Zhang et al., 2014). Long-term impacts The Great Smog of London represented a major natural experiment that sadly allowed us to observe not only the immediate consequences but also the long-term impact of polluted environments on health (case #2). Bharadwaj et al. (2016) investigated how early childhood exposure to the Great Smog was associated with asthma development late in life. Researchers recruited 2916 individuals born between 1945 and 1955. Children in utero or in their first year of life during the Great Smog were considered exposed, while those conceived long before/ after the event or residing outside the affected area were considered non-exposed. Results showed that nearly 20 per cent of children born in London around the area of interest reported childhood asthma, in comparison with any other cohort, of which the rate never exceeds 11 per cent. The most consistent explanation for these findings rests in the particularly sensitive period that occurs during early childhood, when the exposure to high-risk external stimuli may lead to unfavourable changes in the developmental stage and also in health status in adulthood (Gluckman et al., 2008; Schultz et al., 2016). Recent research Air pollution not only impacts physical health. Recent research has focused on the association between pollutant exposure and mental health and psychological well-being given their toxicity to the central nervous system (CNS), especially in children and older people (Kim et al., 2020). For instance, an association has been found between prenatal exposure to air pollution, especially during the third trimester, and the development of autism spectrum disorder (Raz et al., 2015) and attention deficit hyperactivity disorder (Siddique et al., 2011). Further studies examining neurobehavioural changes in older age showed associations with Alzheimer’s and Parkinson’s diseases (Babadjouni et al., 2017) and faster cognitive deterioration in men (Power et al., 2011) and women (Weuve et al., 2012). Researchers suggest that

Environmental impacts on quality of life via environmental psychology  55 air pollutants may enter the brain by crossing the blood–brain barrier or accumulating in the olfactory bulb (Calderón-Garcidueñas et al., 2008), altering the CNS and potentially leading to inflammation, neuropathology and cell death (Block and Calderón-Garcidueñas, 2009; de Prado Bert et al., 2018). Due to its potent oxidative effects, air pollution may also interact with the brain through early-life and brain-maturation stages (Calderón-Garcidueñas et al., 2008), playing a critical role in the onset of mental disorders such as depression (Szyszkowicz et al., 2009), anxiety (Power et al., 2015) and psychosis (Newbury et al., 2019). These risk factors are exacerbated by the simultaneous presence of social adversities in the urban environment, such as neighbourhood disorder, social fragmentation and crime, creating a cumulative risk for health (consistent with the CRM for urban environment perception quoted in the first principle) due to the cumulative temporal exposure (case #3). Implications The damaging effects of poor air quality on well-being are supported by the evidence presented in this section. Specifically, it has highlighted three possible time lag cases between exposure to air pollutants and the consequent health effects: (a) short-time exposure can induce immediate effects (case #1) or long-term effects (case #2); and (b) a long-term exposure can cause long-term chronic effects due to cumulative temporal exposure (case #3) (Figure 4.4).

Source: The authors.

Figure 4.4

Exposure effects

Understanding the mechanisms of air quality and integrating this knowledge into urban planning is essential to regulate sources of emissions effectively and efficiently. For instance, road traffic, which is a significant contributor to air pollution, should be carefully considered and managed. Good planning could remove, or at least reduce, traffic in sensitive areas by minimising heavy vehicles inside the city centre, banning older polluting vehicles and, in the longer term, only allowing non-polluting vehicles. In addition, promoting sustainable transport and improving network infrastructures would also help reduce pollution emissions.

56  Handbook of quality of life research

CONCLUSION Much research has investigated the social-psychological effects of the urban environment (with its spatial and social features) on people and their well-being and QOL. Thanks to the social sciences working collaboratively with other scientific disciplines, understanding the effects become more accurate, producing valuable information for planning, design, management and policy. Several kinds of analysis levels are helpful to determine the effects of environmental factors on people’s QOL, from a specific epigenetic context to a broader social-psychological perspective. Theoretical models can coherently integrate them, framing the environment and individual features within a meaningful input, process and outcome framework based on the three main principles (that is, structure, process and timing). The examples provided in this chapter have highlighted the importance of thinking and planning interventions based on the current scientific knowledge about the environment–person relationship to achieve the best possible outcomes in terms of QOL.

NOTE 1.

The Interuniversity Research Centre on Environmental Psychology.

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58  Handbook of quality of life research Karb, R.A., Elliott, M.R., Dowd, J.B. and Morenoff, J.D. (2012), ‘Neighborhood-level stressors, social support, and diurnal patterns of cortisol: the Chicago Community Adult Health Study’, Social Science and Medicine, 75, 1038–47. Kim, H., Kim, W.H., Kim, Y.Y. and Park, H.Y. (2020), ‘Air pollution and central nervous system disease: a review of the impact of fine particulate matter on neurological disorders’, Frontiers in Public Health, 8, Article 575330. Kim, P., Evans, G.W. and Chen, E. et al. (2018), ‘How socioeconomic disadvantages get under the skin and into the brain to influence health development across the lifespan’, in P. Kim, G.W. Evans and E. Chen et al. (eds), Handbook of Life Course Health Development, Cham: Springer, pp. 463–97. Kleinepier, T. and Van Ham, M. (2018), ‘The temporal dynamics of neighborhood disadvantage in childhood and subsequent problem behavior in adolescence’, Journal of Youth and Adolescence, 47, 1611–28. Li, Q. (2010), ‘Effect of forest bathing trips on human immune function’, Environmental Health and Preventive Medicine, 15, 9–17. Lippert, A.M., Evans, C.R., Razak, F. and Subramanian, S.V. (2017), ‘Associations of continuity and change in early neighborhood poverty with adult cardiometabolic biomarkers in the United States: results from the National Longitudinal Study of Adolescent to Adult Health, 1995–2008’, American Journal of Epidemiology, 185, 765–76. Mair, C., Roux, A.D. and Galea, S. (2008), ‘Are neighbourhood characteristics associated with depressive symptoms? A review of evidence’, Journal of Epidemiology & Community Health, 62, 940–46. Mayhew, M.J. and Powell, J.H. (2014), ‘The development of a brief self-report questionnaire to measure “recent” rash impulsivity: a preliminary investigation of its validity and association with recent alcohol consumption’, Addictive Behaviors, 39, 1597–605. McEachan, R.R.C., Prady, S.L. and Smith, G. et al. (2016), ‘The association between green space and depressive symptoms in pregnant women: moderating roles of socioeconomic status and physical activity’, Journal of Epidemiology and Community Health, 70, 253–9. Moore, L.D., Le, T. and Fan, G. (2013), ‘DNA methylation and its basic function’, Neuropsychopharmacology, 38, 23–38. Newbury, J.B., Arseneault, L. and Beevers, S. et al. (2019), ‘Association of air pollution exposure with psychotic experiences during adolescence’, JAMA Psychiatry, 76, 614–23. Petronis, A. (2010), ‘Epigenetics as a unifying principle in the aetiology of complex traits and diseases’, Nature, 465, 721–7. Polivka, B.J. (2018), ‘The Great London Smog of 1952’, AJN: The American Journal of Nursing, 118, 57–61. Power, M.C., Kioumourtzoglou, M.A. and Hart, J.E. et al. (2015), ‘The relation between past exposure to fine particulate air pollution and prevalent anxiety: observational cohort study’, BMJ, 350, Article h1111. Power, M.C., Weisskopf, M.G. and Alexeeff, S.E. et al. (2011), ‘Traffic-related air pollution and cognitive function in a cohort of older men’, Environmental Health Perspectives, 119, 682–7. Quach, A., Levine, M.E. and Tanaka, T. et al. (2017), ‘Epigenetic clock analysis of diet, exercise, education, and lifestyle factors’, Aging, 9, 419–46. Raz, R., Roberts, A.L. and Lyall, K. et al. (2015), ‘Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: a nested case-control analysis within the Nurses’ Health Study II cohort’, Environmental Health Perspectives, 123, 264–70. Reuben, A., Sugden, K. and Arseneault, L. et al. (2020), ‘Association of neighborhood disadvantage in childhood with DNA methylation in young adulthood’, JAMA Network Open, 3, Article e206095. Rimmele, U., Seiler, R. and Marti, B. et al. (2009), ‘The level of physical activity affects adrenal and cardiovascular reactivity to psychosocial stress’, Psychoneuroendocrinology, 34, 190–98. Ritchie, H. and Roser, M. (2018), ‘Urbanization’, Our World in Data, https://​ourworldindata​.org/​ urbanization. Roe, J.J., Thompson, C.W. and Aspinall, P.A. et al. (2013), ‘Green space and stress: evidence from cortisol measures in deprived urban communities’, International Journal of Environmental Research and Public Health, 10, 4086–103. Runyon, R.S., Cachola, L.M. and Rajeshuni, N. et al. (2012), ‘Asthma discordance in twins is linked to epigenetic modifications of T cells’, PLOS ONE, 7, Article e48796.

Environmental impacts on quality of life via environmental psychology  59 Rutter, M. (1979), ‘Protective factors in children’s responses to stress and disadvantage’, in M.W. Kent and J.E. Rolf (eds), Primary Prevention of Psychopathology, Vol. 3: Social Competence in Children, Hanover, NH: University of New England Press, pp. 49–74. Rutten, B.P. and Mill, J. (2009), ‘Epigenetic mediation of environmental influences in major psychotic disorders’, Schizophrenia Bulletin, 35, 1045–56. Saelens, B.E., Sallis, J.F. and Frank, L.D. (2003), ‘Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures’, Annals of Behavioral Medicine, 25, 80–91. Schultz, E.S., Hallberg, J. and Bellander, T. et al. (2016), ‘Early-life exposure to traffic-related air pollution and lung function in adolescence’, American Journal of Respiratory and Critical Care Medicine, 193, 171–7. Shiffman, S., Stone, A.A. and Hufford, M.R. (2008), ‘Ecological momentary assessment’, Annual Review of Clinical Psychology, 4, 1–32. Siddique, S., Banerjee, M., Ray, M.R. and Lahiri, T. (2011), ‘Attention-deficit hyperactivity disorder in children chronically exposed to high level of vehicular pollution’, European Journal of Pediatrics, 170, 923–9. Smith, J.A., Zhao, W. and Wang, X. et al. (2017), ‘Neighborhood characteristics influence DNA methylation of genes involved in stress response and inflammation: the multi-ethnic study of atherosclerosis’, Epigenetics, 12, 662–73. Szyszkowicz, M., Rowe, B.H. and Colman, I. (2009), ‘Air pollution and daily emergency department visits for depression’, International Journal of Occupational Medicine and Environmental Health, 22, 355–62. Tyrväinen, L., Pauleit, S., Seeland, K. and de Vries, S. (2005), ‘Benefits and uses of urban forests and trees’, in C. Konijnendijk, K. Nilsson, T. Randrup and J. Schipperijn (eds), Urban Forests and Trees, Cham: Springer, pp. 81–114. Vale, A. (2007), ‘Sulphur dioxide’, Medicine, 35, 623–66. Venhoeven, L., Taufik, D. and Steg, L. et al. (2018), ‘The role of nature and environment in behavioural medicine’, in W. Bird and M. van den Bosch (eds), Nature and Public Health: The Role of Nature in Improving the Health of a Population, Oxford: Oxford University Press, pp. 89–94. Wang, G., Zhang, R. and Gomez, M.E. et al. (2016), ‘Persistent sulfate formation from London fog to Chinese haze’, Proceedings of the National Academy of Sciences, 113, 13630–35. Weeland, J., Moens, M.A. and Beute, F. et al. (2019), ‘A dose of nature: two three-level meta-analyses of the beneficial effects of exposure to nature on children’s self-regulation’, Journal of Environmental Psychology, 65, Article 101326. Weuve, J., Puett, R.C. and Schwartz, J. et al. (2012), ‘Exposure to particulate air pollution and cognitive decline in older women’, Archives of Internal Medicine, 172, 219–27. White, M.P., Elliott, L.R. and Gascon, M. et al. (2020), ‘Blue space, health and well-being: a narrative overview and synthesis of potential benefits’, Environmental Research, 191, Article 110169. Wilkins, E.T. (1954), ‘Air pollution and the London fog of December, 1952’, Journal of the Royal Sanitary Institute, 74, 1–21. Williams, M. (2004), ‘Air pollution and policy – 1952–2002’, Science of the Total Environment, 334, 15–20. Zhang, D., Liu, J. and Li, B. (2014), ‘Tackling air pollution in China – what do we learn from the great smog of 1950s in London’, Sustainability, 6, 5322–38.

PART II QUALITY OF LIFE: MEASUREMENT APPROACHES

5. Objective approaches to investigating and measuring quality of life and well-being Robert J. Stimson

INTRODUCTION As discussed in Chapter 2, research into quality of life (QOL) and well-being has tended to be approached in two ways: (1) by secondary data analysis of existing aggregate data collections to develop objective measures of QOL/well-being as indicators of the economic and social characteristics of people and places and the environmental characteristics of places; and (2) by collecting and analysing primary data on individuals and households through survey research to measure people’s subjective assessments of their overall QOL/well-being and of specific QOL domains. This chapter overviews the objective approach. Discussing the objective approach to QOL/well-being, Diener and Suh (1997) said that ‘[social indicators of QOL] reflect objective circumstances in a given cultural or geographic unit … [and] are based on objective, quantitative statistics’ (p. 192). For example, a place – such as a city – might use various measures of health, crime, levels of educational attainment, workforce participation, the proportion of welfare recipients, and so on as indicators of its objective QOL/well-being. The investigation of objective indicators of QOL/well-being encompasses numerous approaches that have evolved over time, namely: ● the social indicators movement, including the development of territorial social indicators; ● using weighted objective indicators of QOL/well-being to rank places; and ● using objective measures to evaluate the liveability of places. In discussing these, the chapter draws on the earlier work of Stimson and Marans (2011).

THE SOCIAL INDICATORS MOVEMENT Interest in developing objective measures of QOL/well-being may well have its origins in the US Great Depression when the Hoover presidency’s Research Committee on Social Trends published a report Recent Trends in the United States (1933). But the real take-off in what is referred to as the social indicators movement occurred in the 1960s as government agencies in countries across the world became increasingly concerned with factors other than economic in addressing issues involving the state of society. An interest evolved in developing systems of ‘social accounts’ (Gross, 1966a) at various levels of scale – national, state and local. In the US, the shift coincided with debate on policies for the ‘Great Society’ under the Johnson presidency (Johnson, 1964), with the US Department of Health, Education, and Welfare (HEW) publishing a series of reports – HEW Indicators, HEW Trends and Toward 61

62  Handbook of quality of life research a Social Report (1966). The Nixon presidency then committed to an annual reporting on social goals and indicators. Much was also happening in the UK, with a succession of government reports on social indicators including the report Social Trends (General Statistical Office, 1970), and reports by Moser (1970) and Schonfield and Shaw (1972). From the 1960s and into the 1970s, organisations and researchers from numerous disciplines published reports on social indicators, including: ● edited books by Bauer (1966), stemming from his work with NASA, and Sheldon and Moore (1968), sponsored by the Russell Sage Foundation; ● a book by geographer David Smith (1973) titled The Geography of Social Well-Being in the United States: An Introduction to Territorial Social Indicators; ● a study by Gross (1966b) for the Tavistock Institute in London; ● a reprint of two special issues of the Annals of the American Academy of Political and Social Science (Gross, 1967); ● a paper by political scientist Agoe (1970); and ● a bibliography by McVeigh (1971). More recently, Noll (2002) provided a comprehensive overview of the evolution of social indicators in the context of QOL research. What is a Social Indicator? Social indicators were not developed explicitly as measures of QOL. Rather, they were used widely as measures relating to human well-being. Smith (1973, p. 66) pointed out that up to the 1970s, in the literature on social indicators: the concept of social well-being is sometimes thought of as synonymous with the quality of life. But it may be preferable to regard it as being at the more concrete or specific end of the continuum of abstraction that descends from human happiness through the concept of the quality of life to social well-being. ‘Quality of life’ implies a rather personalised concept, whereas reference to aggregates of people defined by area of residence more appropriately addresses the welfare of some social group.

Smith went on to suggest that ‘any specific operational definition of the concept of social well-being ought eventually relate to human happiness or the capacity of individuals to realize their perception of the good life, for this is the ultimate criterion for determining whether a society is well or sick’ (p. 67). So, what is a social indicator? A simple early definition was by the US Department of Health, Education, and Welfare (1966, p. 97) in Toward a Social Report: A social indicator … may be defined to be a statistic of direct normative interest which facilitated concise, comprehensive and balanced judgement about the condition of major aspects of society. It is in all cases a direct measure of welfare and is subject to the interpretation that, if it changes in the ‘right’ direction, while other things remain equal things have gotten better, or people are ‘better off’.

Perloff (1969, p. 20) suggested that a social indicator was ‘normally used to describe the condition of a single element, factor, or the like, which is part of a complex interrelated system’.

Objective approaches to investigating and measuring quality of life and well-being  63 Land (1970) described a social indicator as a component (i.e., a parameter or variable) in a social system or some segment thereof. And Culyer et al. (1972) distinguished between: ● indicators of the present condition or state; ● indicators of the gap between the existing and the desired state (what might be described as ‘need’); and ● indicators of the effectiveness of programmes designed to close the gap. Smith (1973, p. 54) said that social indicators should measure the state of and changes over time in major aspects or dimensions of social conditions that can be judged normatively as part of a comprehensive and interrelated set of measures embedded in a social model, and their compilation and use should be related to public policy goals. This required a broad consensus on what those goals and objectives meant, which was a difficult task (Hoffenberg, 1970). An added problem of how to address the task of measurement involved assigning numbers to the variables that operationally defined the concepts on which social indicators were meant to shed light. Operationalising social indicators commonly encountered problems of data availability, data inaccuracies and coverage deficiencies, data validity and reliability, and the incompatibility of different sources of data (Smith, 1973, pp. 59–61). As discussed by Stimson and Marans (2011, p. 36), embedded in the work on social indicators was the notion that ‘through appropriate interventions it would be possible to improve the state of things for the benefit of people and hence the link between social indicators movement and the incorporation of objectives related to equity, equality and social justice into public policy’. Territorial Social Indicators Early work developing social indicators focused on indicators at the highly aggregated level of scale – the nation or for states within nations. But the social indicators movement also began to explicitly incorporate a spatially disaggregated perspective through a focus on ‘the geographic notion of social well-being as a condition with areal variations’ (Smith, 1973, p. 63). Hence the term territorial social indicator was used to refer to approaches that explicitly subsume what Smith referred to as ‘the concepts of “local”, “regional”, “metropolitan”, and “urban” indicators’ (ibid.). The idea of territorial social indicators involving spatially disaggregated data analysis became part of the movement from early on. In the US, Gross (1966a, 1966b) suggested that an annual social reporting by the president could incorporate reporting on the performance of states and cities. The Department of Health, Education and Welfare initiated inquiry into metropolitan regions, and The Urban Institute (1971) began reporting on urban indicators. A study in Michigan by Perle (1970) was an early example of using territorial social indicators to describe patterns of social well-being at a more spatially disaggregated level. The development of interest in territorial social indicators in the US was encouraged because of the more dominant roles local and state governments play and the immediate impact they can have on people’s QOL/well-being (Wilson, 1969). In the UK, there also was increasing interest in territorial social indicators. The General Statistical Office’s (1970) Social Trends report began mapping regional disparities in aspects of social conditions across the UK, highlighting spatial concentrations of special social significance.

64  Handbook of quality of life research Not surprisingly, the task of deciding the level of spatial disaggregation – what Smith (1973, p. 65) referred to as ‘territorial division’ – to use in addressing territorial social indicators was not straightforward and inevitably involved compromise. In the US, a macro-level approach focused on conditions and trends in broad regions such as states, state economic regions and standard metropolitan statistical areas (SMSAs). A micro-level approach would focus on investigating socially significant territorial units by combining census enumeration districts, blocks or tracts. Smith (1973, p. 66) argued that ‘ultimately there should be some correspondence between territories defined for social reporting and those used for public policy implementation’. But whatever the level of scale used to develop social indicators, difficulties arose, including: ● the ecological fallacy issue, which relates to the problem in attributing aggregate characteristics of spatial units to individuals or groups of people living in those spatial units; ● the problem of spatial autocorrelation, which complicates statistical analysis when using spatial data; and ● where the data being used were compiled from sample surveys, there could be significant inaccuracies in using that data for subnational smaller geographic areas because of sample coverage deficiencies across the disaggregated spatial units. In some ways, the social indicators approach – particularly territorial social indicators at a spatially disaggregated level of scale – was perhaps the simplest approach to the study of objective QOL/well-being. It used objective indicators based on the analysis of secondary data relating to aspects of an explicit spatial environmental setting, such as suburbs within a city. Those indicators might include measures relating to employment, housing, health and life expectancy, pollution, traffic flows, and so forth. The indicators could be monitored either separately or collectively using multivariate statistical analysis to provide both a cross-sectional snapshot and/or analysis of trends over time (see Cicerchia, 1999; D’Andrea, 1998; Perz, 2000). There were calls by researchers to augment objective social indicators to include subjective indicators on an equal basis (see, Cutter, 1985; Diener and Suh, 1997; Santos and Martins, 2007; Stagner, 1970). But the social indicators approach was primarily focused on the objective measurement of aspects of QOL/well-being.

CATEGORIES OF OBJECTIVE SOCIAL WELL-BEING USED IN SOCIAL INDICATOR STUDIES In the social indicators movement – including in territorial social indicator studies – there was no firm consensus on what categories of objective well-being should be used as social indicators. There was no ‘correct set’ of variables that might be derived from existing data – such as the census – that could be used to measure the nature of and the changes over time in social conditions that needed to be improved. But a degree of consensus did emerge with the proliferation of studies on social indicators that gathered momentum from the 1960s. Smith (1973, pp. 66–70) noted the degree to which ten key official studies of social indicators published in

Objective approaches to investigating and measuring quality of life and well-being  65 the US and the UK between 1960 and 1970 covered 20 topics as major categories of social well-being or social indicators: ● There was almost complete agreement on the inclusion of four conditions, namely: ● income and wealth; ● employment; ● health; and ● education. ● There was a high degree of agreement on four additional conditions: ● social status and mobility; ● public order and safety; ● the state of the family; and ● the living environment. ● Seven conditions frequently occurred as major items in more than half of the studies, namely: ● science and technology; ● participation and alienation; ● leisure and recreation; ● social disorganisation (or social pathologies); ● the natural environment; ● access to services; and ● culture and the arts. ● Other topics included in no more than three of the ten studies were: ● the production of goods and services; ● demographic characteristics; ● the political process; ● the mass media; and ● religion. Using meta-analysis, Smith (1973) surveyed textbooks and journal papers discussing social indicators, compiling lists of topics being investigated. He concluded as follows (p. 69): In a well society, people will have incomes adequate for their basic needs of food, clothing and shelter, and a ‘reasonable’ standard of living; people will not live in poverty. The status and dignity of the individual will be respected, and he will be socially and economically mobile. Good quality education and health services will be available to all, and their use will be reflected in a high level of physical and mental health and an informed populace able to perform their societal roles in a satisfactory manner. People will live in decent houses, in decent neighbourhoods, and will enjoy a good quality of physical environment. They will have access to recreational facilities, including culture and the arts, and adequate leisure time in which to enjoy these things. Society will show a low degree of disorganisation, with few personal social pathologies, little deviant behaviour, low crime, and high public order and safety. The family will be a stable institution, with few broken homes. Individuals will be able to participate in social, economic and political life and will not be alienated on the basis of race, religion, ethnic origin, or any other cause.

Smith (ibid.) acknowledged that this ‘begs far more questions than it answers’, with almost every word requiring definition, clarification or reservation. However, Smith did propose some general criteria for a list of social well-being (Table 5.1) for which territorial social indicators could be derived at various levels of spatial scale.

66  Handbook of quality of life research Table 5.1

General criteria of social well-being

I. Income, wealth and employment

i. Income and wealth ii. Employment status iii. Income supplements

II. The living environment

i. Housing ii. The neighbourhood

III. Health

i. Physical health ii. Mental health

IV. Education

i. Achievement ii. Duration and quality

V. Social order (and disorganisation)

i. Personal pathologies ii. Family breakdown

VI. Social belonging (alienation and participation)

i. Democratic participation ii. Criminal justice iii. Segregation

VII. Recreation and leisure

i. Recreation facilities ii. Culture and the arts iii. Leisure available

Source: Smith (1973, p. 70).

NUMERICAL MEASUREMENT AND MODELLING SOCIAL WELL-BEING Absolute and Relative Indicators Deciding on appropriate numerical measures of social well-being involved: (1) addressing the limitations in sources of official data available at a particular level of spatial scale necessary for the construction of territorial social indicators; and (2) making decisions on how those indicators would be measured. It involved the choice of an absolute or a relative indicator measure: (a) an absolute indicator is a ‘scientifically’ established maximum or minimum level for a specified condition, as with a poverty line; (b) a relative indicator has no absolute limit or optimal level but is simply a measure of the relative position of a territorial unit with respect to the specified condition, such as the percentage of households living below a poverty line. A relative territorial indicator might be benchmarked against the national incidence of the social condition using location quotients (LQs) to indicate the extent to which the incidence of the condition in a territorial unit is above or below the national incidence of that condition where the national benchmark is LQ = 1. A Modelling Framework Smith (1973, pp. 73–7) proposed a framework to undertake simple mathematical modelling of territorial social indicators. It started with an individual member of society and assumed they have a set of perceptions and expectations about ways of life with sets of needs affecting their well-being. Those individual social well-being expectations might be aggregated across a territorial unit producing a group social well-being. The model could be extended into a broader policy field whereby the overall level of well-being – when related to societal goals about

Objective approaches to investigating and measuring quality of life and well-being  67 justice, equity and the like – might be used to simulate social and remedial interventions for the overall system and for individuals. Operationalising that type of model required collecting data through a large-scale sample survey to generate the measures of social well-being. The territorial social indicators approach relied heavily on some aggregate measures of the individual or group condition – often using a surrogate variable measure – with respect to the various criteria of well-being considered important for investigation at the territorial level. Such measures may or may not be available in national datasets and would need to be generated at the disaggregated level through survey data collections for territorial units such as measures related to health and education. Using the example of education, Smith (1973, pp. 75–7) shows how the outcome of an educational system might be measured by student scores on various tests that, when aggregated for people living in a particular area, might represent a territorial social indicator of social well-being on the assumption that educational attainment is something that matters as providing a means of access to a good life. However, the actual level of educational attainment would likely be some function of the inputs to the system, such as expenditures on physical plant, curriculum development and teacher training. Other effects influencing educational outcomes might also need to be accommodated, such as family socio-economic status. Smith demonstrated how the modelling approach might be developed to do that and how it might be generalised for a wider social system and for sub-systems to produce overall territorial levels of outcome. This type of heuristic modelling of society was used by the US Office of Education (1969) in a report on master social indicators. This type of modelling is now greatly enhanced through the evolution of sophisticated spatial microsimulation techniques, whereby national sample survey individual data are merged with small area aggregate data from the census to generate synthetic measures for small area spatial units for variables that reflect the behavioural and attitudinal measures at the territorial level using the individual-level data that are available in a national survey dataset.

SOME EMPIRICAL APPLICATIONS OF SOCIAL INDICATORS Early Studies in the US In the US there were many studies of territorial social indicators in the 1960s and 1970s: ● some were based on states (for example, Smith, 1972, 1973); ● some used metropolitan cities (for example, Coughlin, 1970; Jones and Flax, 1970); while ● some undertook intra-city analyses (for example, Smith and Gray, 1972). It was common to analyse sets of variables measuring social well-being using data drawn from the census – like those listed in Table 5.1 – with the variables being transformed into rankings or Z-scores. The territorial unit scores on the different variables could be combined using a standard score additive model producing a composite social indicator of well-being, with the territorial unit scores being mapped to identify places with a positive or negative, or an above or below average, performance of social well-being. Correlation analysis also might be used to identify the degree of ecological associations between pairs of the territorial social indicators. Principal components analysis (PCA) or factor analysis was also used to identify the underlying dimensions of variance in the spatial data matrices by extracting generalised dimensions

68  Handbook of quality of life research that account for decreasing amounts of the cumulative variance in the social indicators (see Coughlin, 1970; Smith, 1972). Smith’s study used PCA to extract six principal components cumulatively accounting for 77.49 per cent of the total variance in the territorial social indicators across US states. Those components were: ● ● ● ● ● ●

general socio-economic well-being (38.56 per cent of the variance); social pathology (13.74 per cent); mental health (11.89 per cent); racial discrimination (5.94 per cent); public assistance/unionisation (3.72 per cent); and social disruption (3.55 per cent).

The state scores on those leading components were used to map the pattern of spatial variation in those generalised territorial social indicators. Further methodological innovations used cluster analysis to group territorial unit scores on the social indicator components derived from a PCA, and correlation analysis and multiple regression analysis were used to explore the links between territorial unit scores on generalised social well-being dimensions and other characteristics of territorial units – for example, population size and growth rates, net migration and the concentration of employment in various industry sectors. An example of research employing such analytical methods was Coughlin’s (1970) study of 109 SMSAs across the US using step-wise multiple regression analysis. A study of territorial social indicators within the city of Tampa, Florida (Smith, 1973) – then later in another 19 cities – collected data for 1967 on 21 variables relating to six criteria: ● ● ● ● ● ●

housing conditions; physical conditions; health; crime and delinquency; unemployment; and welfare services.

Scores on the 21 variables were used to rank census tracts to identify ‘primary target areas’ with social well-being problems. The Tampa study by Smith (1973) was important as it focused on intra-city social conditions to show how a spatially disaggregated approach to territorial social indicators might be used to formulate and implement public urban planning policy, an important objective of the social indicators movement. The OECD ‘Society at a Glance’ Social Indicators In response to a growing demand for quantitative evidence on the social situation, trends and possible drivers, in the mid-2000s the Organisation for Economic Co-operation and Development (OECD) began developing a set of social indicators for nations, publishing an annual Society at a Glance report (OECD, 2015). Most indicators were objective measures relating to well-being, while some incorporate subjective measures. The objective was to: (1) assess and compare social outcomes that are currently the focus of policy debates; and (2) provide an overview of societal responses, and how effective policy actions have been in furthering social development.

Objective approaches to investigating and measuring quality of life and well-being  69 The ‘pressure–state–response’ (PSR) framework – widely used in environmental reporting – was used to group indicators into three areas: ● social context, which refers to general indicators that, while not usually direct policy targets are relevant information for understanding the social landscape; ● social status, which describes the social outcomes that policies try to influence; and ● societal response, which provides information about measures and activities to affect social status indicators. The framework also grouped social status and societal response indicators according to the broad policy fields they cover, namely: ● ● ● ●

self-sufficiency; equity; health status; and social cohesion.

Table 5.2 lists the indicators used. Table 5.2

The OECD social indicators framework

Indicators

Social Status

Responses

General context indicators

 

 

Employment

Education spending

Household income Fertility Migration Family Demographic trends Self-sufficiency indicators

Unemployment Skills Labour market entry Equity indicators

Income inequality

Social spending

Poverty Living benefits Recipients of out-of-work benefits Health indicators

Life expectancy

Health spending

Perceived health status Suicide Tobacco and alcohol spending Social cohesion indicators

Life satisfaction Trust Voting Crime and prisoners Social networks

Source: OECD (2015).

 

70  Handbook of quality of life research

INDICATORS OF QUALITY OF URBAN LIFE Using Weighted Objective Indicators of Quality of Urban Life Studies Studies inquiring into objective QOL/well-being have incorporated numerous objective measurements of characteristics of the broad urban environment, often combining or weighting objective indicators to generate an overall objective QOL ranking for places, in particular cities (see, Blomquist et al., 1988; Boyer and Savageau, 1981, 1985, 1989; Cicerchia, 1999; Cutter, 1985; Liu, 1975; Savageau and D’Agostino, 1999; Stover and Leven, 1992). Use of weighting systems has been criticised because of their ad hoc nature and because the place rankings can change markedly by using alternative sets of weights (Cutter, 1985; Landis and Sawicki, 1988; Rogerson et al., 1989). This raises questions about the objectivity of ‘objective’ QOL estimates. However, significant methodological developments have been made to derive more objective weights for estimating QOL of an urban place and to rank it for cities, including using hedonic price equations (for example, Blomquist et al., 1988; Stover and Leven, 1992). Some empirical studies of objective QOL emphasise the trade-off between positive and negative aspects of urban living. Examples include the following: 1. Blomquist et al. (1988) modelled the trade-off between housing costs, wages and amenities to develop QOL indexes for 253 SMSAs in the US. Hedonic wage and rent equations were used to derive implicit prices for various urban amenities, which in turn were used as weights in compiling an objective QOL index. The notion was that the ‘amenity value’ of a place is implied from spatial variation in housing costs and wages. Such models use implicit amenity prices as theoretical weights for amenities, which, it is argued, is a more objective weighting system. But those weights have also been criticised because they rely on a range of assumptions that can be challenged. 2. Rogerson et al. (1989) derived a subjective set of weights by taking the average subjective importance of various attributes of an urban environment obtained from a national opinion survey, which were then used to weight objective attributes of places to produce a ranked list of QOL in British cities. But averaging importance measures across residents ignores how individuals might disagree with the ranking of the cities, indicating that subjective assessments of the importance of the objective attributes needs to be considered. 3. Cicerchia (1999) theorised about the trade-off between ‘city effect’ and ‘urban load’. City effect relates to access to superior urban functions, opportunities and services available by virtue of the size of a city. Urban load relates to the negative consequences of urban growth (for example, congestion and environmental degradation). The purpose was to ascertain the ‘optimum centrality’ for a city, which was the size of a city where city effect is less as urban load is maximised. If the city size becomes too large, then an escalating urban load might exceed the city effect and create urban overload. 4. In another study, Schwirian et al. (1995) showed how population size and density can contribute to economic, social and environmental load or stress in cities.

Objective approaches to investigating and measuring quality of life and well-being  71 Ratings and Ranking of Cities and their QOL The upsurge of public and corporate interest in the QOL of cities spurred numerous commercial rating agencies to publish listings or rankings of cities and their QOL. Sometimes the term ‘liveability’ is used (discussed later). Cities are rated or ranked according to how they perform on a set of objective measures relating to QOL/well-being, usually comprising statistics relating to things such as: ● ● ● ● ● ● ● ● ●

average salaries; housing costs; the cost of healthcare; school performance; crime rates; public transport systems; planned infrastructure improvements; neighbourhood diversity; and access to public parks.

Sometimes those measures might be augmented by estimates of factors such as: ● ● ● ● ● ● ●

physical and technological connectivity; tolerance; the strength of local media and culture; the quality, range and independence of restaurants and retail shopping; late night entertainment; business investment climate; and planned infrastructure.

Assessing the performance of a city on such issues is inferred to relate to the QOL of a city. Examples of Agencies Undertaking Rankings of Cities Monocle magazine Since 2007, Monocle magazine has published a list of what it calls the world’s ‘most liveable cities’.1 The performance of about 40 cities across around 60 indicators are considered. In 2009, for example, the list was topped by Zurich, followed by Copenhagen, Tokyo, Munich and Helsinki. Monocle has also considered the way locals and visitors navigate and use everything from public parks to the local property market. Places considered to have the best QOL tend to be those with the ‘fewest daily obstructions, allowing residents to be both productive and free of unnecessary stress’ (Brûlé, 2009). The Monocle top 25 list (Table 5.3) has been dominated by European cities (excluding London). Apart from Tokyo and Singapore, Asian cities fare poorly, and cities in North America do not appear except for Vancouver. From Australia and New Zealand, Melbourne ranked #9, Sydney #13 and Auckland #20 in 2009. Not surprisingly, there is a tendency for the city rating criteria to change from year to year.

72  Handbook of quality of life research Table 5.3

The Monocle 2008–09 list of the world’s 25 ‘most liveable cities’

Rank 2009

City

Rank 2008

1

Zurich

4

2

Copenhagen

1

3

Tokyo

–

4

Munich

2

5

Helsinki

–

6

Stockholm

7

7

Vienna

6

8

Paris

10

9

Melbourne

–

10

Berlin

14

11

Honolulu

12

12

Madrid

13

13

Sydney

11

14

Vancouver

8

15

Barcelona

–

16

Fukuoka

17

17

Oslo

–

18

Singapore

22

19

Montreal

16

20

Auckland

–

21

Amsterdam

18

22

Kyoto

20

23

Hamburg

21

24

Geneva

23

25

Lisbon

25

Note: First time on list: Oslo, Auckland. Dropped off: Minneapolis, Portland. Source: Monocle magazine as reported by Brûlé (2009).

The Economist Intelligence Unit Another well-known city rating agency is The Economist Intelligence Unit, which ranks cities by their QOL. The term ‘liveability’ is used to produce a Global Liveability Ranking, liveability being measured by factors that provide QOL. A score is assigned to each city derived from measures on some 30 factors across five broad categories: ● ● ● ● ●

stability; healthcare; culture and environment; education; and infrastructure.

In June 2013 the Global Liveability Ranking listed Melbourne in Australia in top place as the world’s most liveable city out of 140 cities, followed by Vienna in Austria.2 In 2019, the top-ranked city was Vienna, followed by Melbourne and Adelaide in Australia.3 Again, unsurprisingly, the rank position of cities can change over time.

Objective approaches to investigating and measuring quality of life and well-being  73 Mercer Quality of Living Ranking Sometimes a city rating agency might also solicit so-called ‘expert opinion’ from business and professional expatriates who have worked in a city, as with the Mercer Quality of Living Ranking of 215 cities across the world.4 It focuses on measuring cost of living and housing for employees of companies sent abroad to live. In 2009, Vienna ranked top, followed by Zurich, Geneva, Vancouver and Auckland. The top Asian city was Singapore at rank #256, and the top US city was Honolulu at rank #29. In the UK, London was the lead city at rank #38. By 2019, Vienna remained the top-ranked city, followed by Zurich, Vancouver and Munich. Some Issues Stimson and Marans (2011) discuss how undertaking such city QOL/liveability ratings or rankings is a difficult business. Not surprisingly, rating agencies are coming up with different results using different indicators, with a lack of transparency in exactly what measures are used and in how the city rating scores are derived. But there is some consistency in the results from the various city QOL rating agencies, with the top-rating cities tending to be: ● ● ● ●

medium-sizes cities in developed countries; cities that offer culture and recreation; cities that have low crime; and cities with fewer infrastructure problems than the very large cities.

LIVEABILITY AND QUALITY OF LIFE A different take to that discussed above in developing ratings or rankings of places using objective measures of QOL/well-being is an explicit focus on the liveability of places, for which there has been an upsurge of research and policy interest (see Couclelis, 1990; Newton, 2012; Pacione, 1990; Southworth 2003, 2016; Türkoğlu, 2015). Studies seek to measure liveability using objective – and sometimes subjective – factors that may enhance or detract from the QOL of a place, and even impact the happiness of people. Ley and Newton (2010, pp. 191–2) define ‘liveability’ as: a place-based concept that generally refers to those elements of a home, neighbourhood or city that contribute to quality of life or wellbeing … Liveability, as represented by human wellbeing and the quality of a city’s environment, derives significantly from the performance of key urban systems and processes in the cities where people live and work.

Those systems and processes may relate to things such as: ● ● ● ● ● ● ●

housing market characteristics, especially affordability; transport networks providing mobility without car dependence; low carbon-emitting energy; water recycling and waste water capture; air quality; open space provision; an advanced industrial base;

74  Handbook of quality of life research ● economic competitiveness; and ● housing provision well connected with employment and services. Ley and Newton (2010) discuss the connection or nexus between liveability and sustainable development, which is typically a focus in research into ‘place liveability’. Planners, urban designers and public health researchers have placed considerable emphasis on how place liveability is enhanced through the ‘walkability’ of a place such as a suburb and a new urban area (see Lowe et al., 2020; Southworth, 2003). The walkability of a place might be the product of urban form and design, and especially density. Studies investigating liveability and walkability suggest there is a link between neighbourhood built environment and physical activity. Frank et al. (2010) proposed a methodology for characterising neighbourhood built environment to develop an integrated index for operationalising walkability using land parcel-level information. Such studies infer that not only are there health benefits to walking but also positive impacts on QOL that can result from a walkable neighbourhood (Rogers et al., 2011). Objective urban environmental indicators are used to compute a walkability index of a local area, with survey data on local residents’ assessment of subjective QOL also being used. Analysis investigates the mediating effect of walkability on people’s QOL (see Jaśkiewicz and Besta, 2014). The degree of walkability of a neighbourhood may positively impact its liveability, enhancing QOL. However, Forsyth (2015) suggests that those relationships between urban design, walkability and QOL might be problematic because ‘what is considered a walkable place varies substantially between definitions leading to substantially different designs’ (p. 274). Promoting the liveability of a city – and achieving a sustainable QOL – has become a significant policy concern (see Reiter, 2012) for attracting businesses and people – especially for tourism – as cities compete with each other, and it is being used as a branding mechanism. The Economist Intelligence Unit is promoting liveability criteria – including walkability – in developing city ranking systems.

CONCLUSION There are fundamental differences in objective and subjective measures of QOL/well-being and it is important to use both approaches when investigating QOL/well-being. The emergence from the 1960s of the social indicators movement, in which objective measures of well-being are used, has proliferated a vast literature. Initiatives emerged that incorporate a measure – or measures – of the well-being or QOL of society as a whole and of subgroups of society that went beyond the traditional economic indicators (such as gross domestic product per capita) and to focus more directly on ‘output measures’ (see Andrews, 1989), with increasing efforts incorporating such measures into national accounts, as evident in the initiatives from the 1990s in the UK Office for National Statistics (ONS). There have been challenges, including ‘the importance of getting the measures adopted as policy drivers, how to challenge the continuing dominance of economic measures, sustainability and environmental issues, international comparability and methodological statistical question’ (Hand and Allin, 2017, p. 3). Unfortunately, in the US, by late 1982, the upsurge in interest in using social indicators to inform public policy would wane (Innes, 1989).

Objective approaches to investigating and measuring quality of life and well-being  75 Nonetheless, the objective indicators used to measure the QOL/well-being of places and changes over time remains important and can be useful in a policy context to see the degree to which objective standards are being met, while subjective indicators of satisfaction measure the extent to which subjective standards or expectations are being met. McCrea (2007, p. 183) makes this point: ‘measuring the extent to which expectations have been met is as important to urban planners and policy makers as measuring objective changes over time. For example, subjective evaluations are related to residential relocation (Clark and Ledwith, 2006; Lu, 1998) and participating in community action (Dahmann, 1985)’. As discussed by Allin and Hand (2014), the continuing policy interest in well-being – including at a national level – is more than the sum of the well-being of everyone in a country. It requires definition and specification of measurements, along with a critical look at the uses to which such measures are to be put. The well-being of society is a ‘wicked problem’ that needs to be understood and addressed through policy action (see Bache and Reardon, 2016). An issue for research investigating QOL/well-being is the cost of collecting subjective measures through surveys, while it is relatively easy and inexpensive to use existing aggregate spatial data sources – such as the census – to generate measures of objective QOL/well-being. Stimson and Marans (2011) make the point that attempts to rate or rank places (be they cities or neighbourhoods within a city) according to their objective QOL/well-being are somewhat futile if different types of residents are attracted to a place or a local area according to what is important to them. They warn that using an ‘objective’ set of weights to calculate objective QOL for a place is arbitrary and may not be valid, as Rogerson et al. (1989) indicated in their study of British cities.

NOTES 1. See https://​monocle​.com/​search/​most​-liveable​-city/​. 2. See http://​www​.citymayors​.com/​environment/​eiu​_bestcities​.html. 3. See https://​www​.eiu​.com/​n/​the​-global​-liveability​-index​-2019/​. 4. See https://​mobilityexchange​.mercer​.com/​Insights/​quality​-of​-living​-rankings.

REFERENCES Agoe, C. (1970), ‘Social indicators: selected readings’, Annals of the Association of American Academy of Political Science, 388, 127–32. Allin, P. and Hand, D.J. (2014), The Wellbeing of Nations: Meaning, Motive and Measurement, Chichester: John Wiley & Sons Ltd. Andrews, F.M. (1989), ‘The evolution of a movement’, Journal of Public Policy, 9, 401–5. Bache, I. and Reardon, L. (2016), The Politics and Policy of Wellbeing, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Bauer, R.A. (ed.) (1966), Social Indicators, Cambridge, MA: MIT Press. Blomquist, G.C., Berger, M.C. and Hoehn, J.P. (1988), ‘New estimates of quality of life in urban areas’, The American Economic Review, 78, 89–107. Boyer, R. and Savageau, D. (1981, 1985, 1989), Places Rated Almanac, Chicago, IL: Rand McNally. Brûlé, T. (2009, 12 June), ‘The city of your dreams’, Financial Times, https://​www​.ft​.com/​content/​ 766d1c92​-561e​-11de​-ab7e​-00144feabdc0. Cicerchia, A. (1999), ‘Measures of optimal centrality: indicators of city effect and urban overloading’, Social Indicators Research, 46, 276–99.

76  Handbook of quality of life research Clark, W.A.V. and Ledwith, V. (2006), ‘Mobility, housing stress, and neighbourhood contexts: evidence from Los Angeles’, Environment and Planning A, 38, 1077–93. Couclelis, H. (1990), ‘Urban liveability: a commentary’, Urban Geography, 11, 42–7. Coughlin, R.E. (1970), ‘Goal attainment levels in 101 metropolitan areas’, RSRI Discussion Paper Series No. 41, Regional Science Research Institute. Culyer, A.J., Lavers, R.J. and Williams, A. (1972), ‘Health indicators’, in A. Schonfield and S. Shaw (eds), Social Indicators and Social Policy, London: Heinemann, pp. 94–118. Cutter, S. (1985), Rating Places: A Geographer’s View on Quality of Life, Washington, DC: Association of American Geographers. Dahmann, D.C. (1985), ‘Assessments of neighborhood quality in metropolitan America’, Urban Affairs Quarterly, 20, 511–35. D’Andrea, S.S. (1998), ‘Italian quality of life’, Social Indicators Research, 44, 5–39. Diener, E. and Suh, E. (1997), ‘Measuring quality of life: economic, social and subjective indicators’, Social Indicators Research, 40, 189–216. Forsyth, A. (2015), ‘What is a walkable place? The walkability debate in urban design’, Urban Design International, 20, 274–92. Frank, L.D., Sallis, J.F. and Saelens, B.E. et al. (2010), ‘The development of a walkability index: application to the Neighborhood Quality of Life Study’, British Journal of Sports Medicine, 4, 924–33. General Statistical Office (1970), Social Trends, No. 1, London: HMSO. Gross, B.M. (1966a), ‘The state of the nation: social system accounting’, in R.A. Bauer (ed.), Social Indicators, Cambridge, MA: MIT Press, pp. 154–271. Gross, B.M. (1966b), The State of the Nation: Social Systems Accounting, London: Tavistock Publications. Gross, B.M. (ed.) (1967), The Annals of the American Academy of Political and Social Science: Social Intelligence for America’s Future: Exploration in Societal Problems (I and II, Vols 371 and 373), Philadelphia, PA: American Academy of Political and Social Sciences. Hand, D.J. and Allin, P. (2017), ‘New statistics for old? Measuring the wellbeing of the UK’, Journal of the Royal Statistical Society Series A – Statistics in Society, 180, 3–43. Hoffenberg, M. (1970), ‘Comments on “Measuring progress towards social goals: some possibilities and at national and local levels”’ [Terleckyj, 1970], Management Science, 16, B779–B783. Innes, J. (1989), ‘Disappointments and legacies of social indicators’, Journal of Public Policy, 9, 429–32. Jaśkiewicz, M. and Besta, T. (2014), ‘Is easy access related to better life? Walkability and overlapping of personal and communal identity as predictors of quality of life’, Applied Research Quality Life, 9, 505–16. Johnson, L.B. (1964, 22 May), ‘Remarks at the University of Michigan’, The American Presidency Project, https://​www​.presidency​.ucsb​.edu/​documents/​remarks​-the​-university​-michigan. Jones, M.V. and Flax, M.J. (1970), The Quality of Life in Metropolitan Washington, D.C.: Some Statistical Benchmarks, Washington, DC: The Urban Institute. Land, K.C. (1970), ‘Social indicators’, in R.B. Smith (ed.), Social Science Methods, New York: Free Press, pp. 216–32. Landis, J.D. and Sawicki, D.S. (1988), ‘A planner’s guide to the Places Rated Almanac’, Journal of the American Planning Association, 54, 336–46. Ley, A. and Newton, P. (2010), ‘Creating and sustaining liveable cities’, in K.E. Seetharam and B. Yuen (eds), Developing Living Cities: From Analysis to Action, Singapore: World Scientific Publishing Co., pp. 191–230. Liu, B. (1975), Quality of Life Indicators in US Metropolitan Areas, 1970, Washington, DC: Washington Environmental Research Center. Lowe, M., Arunde, J. and Hooper, P. et al. (2020), ‘Liveability aspirations and realities: implementation of urban policies designed to create healthy cities in Australia’, Social Science and Medicine C, 245, 1–13. Lu, M. (1998), ‘Analyzing migration decision making: relationships between residential satisfaction, mobility intentions, and moving behaviour’, Environment and Planning A, 30, 1473–95. McCrea, R. (2007), ‘Urban quality of life: linking objective dimensions and subjective evaluations of the urban environment’, unpublished PhD thesis, The University of Queensland.

Objective approaches to investigating and measuring quality of life and well-being  77 McVeigh, T. (1971), Social Indicators: A Bibliography. Exchange Bibliography No. 215, Monticello, IL: Council of Planning Librarians. Moser, C. (1970), ‘Measuring quality of life’, New Society, 428, 1042–3. Newton, P. (2012), ‘Liveable and sustainable: socio-technical challenges for twenty-first-century cities’, Journal of Urban Technology, 21, 81–102. Noll, H. (2002), ‘Social indicators and quality of life research: background, achievements and current trends’, in N. Donovan and D. Halperin (eds), Advances in Sociological Knowledge Over Half a Century, Paris: International Social Science Council, pp. 151–82. Organisation for Economic Co-operation and Development (OECD) (2015), Society at a Glance, Paris: OECD Publishing. Pacione, M. (1990), ‘Urban liveability: a review’, Urban Studies, 11, 1–30. Perle, E.D. (1970), Social Reporting in Michigan: Problems and Issues (Technical Report A 37), State of Michigan, Office of Planning Coordination, Bureau of Policies and Programs. Perloff, H. (1969), ‘A framework for dealing with the urban environment: introductory statement’, in H. Perloff (ed.), The Quality of the Urban Environment, Washington, DC: Resources for the Future, pp. 3–25. Perz, S.G. (2000), ‘The quality of urban environments in the Brazilian Amazon’, Social Indicators Research, 49, 181–212. Reiter, A. (2012), ‘Liveable city – sustainable quality of life as success driver for urban branding’, in R. Conrady and M. Buck (eds), Trends and Issues in Global Tourism, Cham: Springer, pp. 69–76. Rogers, S.H., Halstead, J.M. and Gardner, K.H. et al. (2011), ‘Examining walkability and social capital as indicators of quality of life at the municipal and neighborhood scales’, Applied Research Quality Life, 6, 201–13. Rogerson, R.J., Findlay, A.M., Morris, A.S. and Coombes, M.G. (1989), ‘Indicators of quality of life – some methodological issues’, Environment and Planning A, 21, 1655–66. Santos, L.D. and Martins, I. (2007), ‘Monitoring urban quality of life: the Porto experience’, Social Indicators Research, 80, 411–25. Savageau, D. and D’Agostino, R.B. (1999), Places Rated Almanac, 1999, Foster City, CA: IDG Books Worldwide Inc. Schonfield, A. and Shaw, S. (eds) (1972), Social Indicators and Social Policy, London: Heinemann. Schwirian, K.P., Nelson, A.L. and Schwirian, P.M. (1995), ‘Modeling urbanism – economic, social and environmental stress in cities’, Social Indicators Research, 35, 201–23. Sheldon, E.B. and Moore, W.E. (eds) (1968), Indicators of Social Change, New York: Russell Sage Foundation. Smith, D.M. (1972), ‘Towards a geography of social well-being: inter-state variations in the United States’, in R. Peet (ed.), Geographical Perspectives on American Poverty, Worcester, MA: Antipode, pp. 17–46. Smith, D.M. (1973), The Geography of Social Well-Being in the United States: An Introduction to Territorial Social Indicators, New York: McGraw Hill. Smith, D.M. and Gray, R.J. (1972), Social Indicators for Tampa, Florida, Gainsville, FL: Urban Studies Bureau, University of Florida. Southworth, M. (2003), ‘Measuring the liveable city’, Built Environment, 29, 343–54. Southworth, M. (2016), ‘Learning to make liveable cities’, Journal of Urban Design, 21, 570–73. Stagner, R. (1970), ‘Perceptions, aspirations, frustrations, and satisfactions: an approach to urban indicators’, Annals of the American Academy of Political and Social Science, 388, 59–68. Stimson, R. and Marans, R.W. (2011), ‘Objective measurement of quality of life using secondary data analysis’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 33–54. Stover, M.E. and Leven, C.L. (1992), ‘Methodological issues in the determination of the quality-of-life in urban areas’, Urban Studies, 29, 737–54. The Urban Institute (1971), ‘Developing urban indicators: some first steps’, Search, 1, May–June. Türkoğlu, H. (2015), ‘Sustainable development and quality of urban life’, Procedia – Social and Behavioral Sciences, 202, 10–14. US Department of Health, Education, and Welfare (1966), Toward a Social Report, Washington, DC: USGPO.

78  Handbook of quality of life research US Office of Education (1969), Toward Master Social Indicators: Research Memorandum EPRC 6747-2, Washington, DC: Bureau of Research. US President’s Research Committee on Social Trends (1933), Recent Trends in the United States, New York: McGraw-Hill. Wilson, J. (1969), Quality of Life in the United States, Kansas City, MO: Midwest Research Institute.

6. Investigating subjective quality of life: using survey research methods1 Robert J. Stimson and Robert W. Marans

INTRODUCTION A vast literature has developed on the subjective approach to measuring and analysing quality of life (QOL). As discussed by Diener and Suh (1997), this approach can take on various forms, all of which reflect ‘people’s conscious experiences – in terms of hedonistic feelings or cognitive satisfaction’ (p. 191). In addition, Carley (1981) stated that subjective QOL is based on ‘reports from individuals on the “meaning” of aspects of their reality, and as such represent psychological variables’ (p. 31). That reality incorporates consideration of people’s evaluations/assessments of aspects of the environmental setting in which they live. Typically, primary data are collected from individuals using sample surveys in which people’s level of satisfaction with their overall QOL and specific QOL domains are measured using a Likert scale. As discussed in Chapters 1 and 2, there is no standard model for investigating subjective QOL. Similarly, it was pointed out that there is no agreed-upon definition of QOL. Various terms have been used in the subjective approach to QOL, including happiness (see Chapter 3) and subjective well-being, with those terms often being used interchangeably. Following an earlier overview of the subjective approach to analysing QOL (McCrea et al., 2011), this chapter discusses the evolution of research seeking to measure and model the subjective evaluation or assessment of QOL, and the use of survey methods to do so. It then discusses the ‘domains of life’ approach and ‘levels of domain specificity’. Finally, the chapter discusses some of the theories and modelling approaches researchers have used in investigating subjective QOL.

INVESTIGATING THE SUBJECTIVE ASSESSMENT OF QUALITY OF LIFE In very early investigations of QOL there seemed to be a focus on people’s happiness, which could be approached from either an eudaimonic or a hedonic perspective. The former was evident in the work of Aristotle, who advocated living a ‘good and virtuous life’ (Aristotle, [circa 350 BCE] 1998). Much later, the latter perspective – as advocated by Jeremy Bentham ([1789] 1998) and John Stuart Mill ([1863] 1998) – advocated maximising pleasure or satisfaction, a positive view that lent itself to the development of empirical approaches to the investigation of subjective QOL. It was in the 1960s and particularly in the 1970s when there began a proliferation of research into QOL that adopted an empirical positivist approach, as exemplified by the work 79

80  Handbook of quality of life research of researchers in the Institute for Social Research (ISR) at the University of Michigan (see Andrews and Withey, 1976; Campbell et al., 1976; Marans and Rodgers, 1975). There was also the emergence of research into the notion of subjective well-being (see Diener, 1984; Diener et al., 1999), which had three dimensions, namely: ● pleasant affect; ● unpleasant affect; and ● life satisfaction. The first two affective dimensions might be thought of as people’s positive or negative feelings, while life satisfaction was about a subjective evaluation or cognitive judgement. The distinction between feelings and judgement is important. Judgements refer to particular ‘targets’ or ‘objects’ and can be influenced by ‘standards of comparison’ (Abele and Gendolla, 1999; Campbell et al., 1976; Kahnemann, 1999; Micholas, 1985; Schwartz and Strack, 1999). In contrast, feelings may be generalised and may not relate to specific targets (Forgas, 1995). Feelings are an important component of subjective well-being, while research into specific domains of QOL has been more focused on judgements and the subjective evaluations of targets within those domains. (For a discussion of the affective and cognitive dimensions of QOL, see Andrews and Withey, 1976.) While encompassing a focus on evaluating people’s subjective satisfaction with life as a whole and levels of satisfaction with a series of life domains and sub-domains, the investigation of people’s satisfaction with QOL was also to focus on different levels of scale – such as the dwelling, the neighbourhood, and the wider urban environment – and with many characteristics of the environmental context in which people live – for example, the provision of and access to transport. As discussed in earlier chapters, such was the proliferation of studies investigating QOL that by the turn of the twenty-first century, there were more than 35 000 publications on QOL identified in five main electronic databases (Evans and Huxley, 2002). However, we are reminded by McCrea et al. (2011), that ‘despite that flurry of research into QOL, there still remains no generally accepted definition of QOL nor agreement about how best to measure it, even after considerable debate within the International Society of Quality of Life Studies (Andelman et al., 1998). But in the broadest sense, QOL seems to mean some evaluation of human circumstances’ (p. 57). The Domains of Life Approach It has been common in the subjective analysis of QOL to use a domains of life approach, in addition to focusing on the assessment of people’s satisfaction with their overall life. But there has been considerable debate surrounding the nature and number of independent domains that might be involved, reflective of the wide range of disciplines involved in investigating QOL. For example, Cummins et al. (1994) identified seven main life domains, namely: ● ● ● ● ● ●

material well-being; health; productivity; intimacy; safety; community; and

Investigating subjective quality of life: using survey research methods  81 ● emotional well-being. Satisfaction in such domains may be used to predict a more global evaluation of people’s satisfaction with life, which has been represented as the sum or average of the QOL domain components. Those sums or averages effectively give each domain the same weight in the overall score (called ‘unit weighting’), but, alternatively, satisfaction ratings for various life domains may be multiplied by their importance ratings and then summed, thus generating a weighted sum or weighted average indicating overall QOL. Oliver et al. (1995) note how this reflects the notion that individuals will assign unique weightings to various life satisfaction domains, with lower weights being allocated to those domains that contribute little to the person’s life satisfaction. However, much research shows that both unit weightings and weighting by subjective importance ratings tend to produce similar results. Consequently, most subjective QOL research does not weight subjective evaluations by importance ratings. As discussed by Veenhoven (2000), subjective evaluations of life domains can be both internal and external to the individual. For example, evaluations of neighbourhood crime would be an evaluation of an external neighbourhood quality, while how safe people feel in their neighbourhood would be an evaluation of an internal quality within the individual. Veenhoven (2000) identified four interdependent categories of QOL by distinguishing between those inner and outer qualities and between ‘life chances’ and ‘life outcomes’: ● ● ● ●

the liveability of the environment; the life-ability of the individual; the external utility of life; and the inner appreciation of life.

Thus, it is apparent that aspects of QOL are indeed imbedded within the situational setting where people live and will have an environmental context. Explicitly Addressing the Situational/Environmental Context McCrea at al. (2011, p. 59) discuss how it is useful to make a distinction between: (1) QOL that is derived from the environmental context of where people live – that is, satisfaction derived from domains at different levels of scale, such as housing, neighbourhood, community and region; and (2) QOL as experienced within that environmental context, which might include levels of satisfaction across life domains, such as work, social relationships, neighbourhood, and so forth. Note that the former is related to the links between objective dimensions of the environmental context of where people live, which is discussed in detail in Chapter 7. Research by geographers has investigated QOL in an explicitly spatial context. For example, Pacione (2003) has identified various dimensions of QOL in an urban context using both objective and subjective measures of QOL or well-being, which may vary between social groups. Pacione refers to objective and subjective planes on which there are also two other dimensions, namely: (1) domain specificity (for example, life domains and sub-domains); and (2) levels of geographic/spatial scale. ● Levels of domain specificity: levels of satisfaction with overall life consist of satisfaction across a range of life domains. Examples include: ● levels of satisfaction with work (Hart, 1999; Heller et al., 2002);

82  Handbook of quality of life research ● levels of satisfaction with social relationships (Acock and Hurlbert, 1993; Evans et al., 1993; Foroughi et al., 2001); and ● levels of satisfaction with health (John, 2004; Michalos et al., 2001). ● Geographic scale: levels of satisfaction at explicit levels of spatial scale relate to the environment where people live and include levels of satisfaction with: ● housing (the dwelling people occupy); ● the neighbourhood where people live; ● the community to which people relate; and ● the wider context in which people live (such as a city or region). Researchers have paid much attention to studying the first three of these domains (see, for example, Bruin and Cook, 1997; Campbell at al., 1976; Marans and Rodgers, 1975; Parkes et al., 2002; Sirgy and Cornwell, 2002), while satisfaction in a regional context has tended to receive less attention (examples include McCrea et al., 2005; Turksever and Atalik, 2001). It is not just the level of subjective satisfaction with these domains that has concerned researchers, it is also the factors that might predict those levels of satisfaction. It is not surprising, therefore, that researchers have found that people’s levels of satisfaction with those domains may be interrelated. For example, studies have found the following relationships: ● Levels of housing satisfaction are not only predicted by dwelling age, size, structure and tenure (Campbell et al., 1976; Lu, 1999), but also by surrounding features such as neighbours, housing in the local community and community size (Campbell et al., 1976; Lu, 1999; Parkes et al., 2002). And they can also be predicted by regional characteristics, such as geographic location within the region (Lu, 1999). ● Levels of neighbourhood satisfaction have been found to be predicted by a range of physical, economic and social features (see Sirgy and Cornwell, 2002). ● Levels of neighbourhood satisfaction may also be linked to satisfaction with other domains, including housing and community (ibid.), and neighbourhood social features are a predictor of community satisfaction (Campbell et al., 1976). ● Levels of community satisfaction and housing satisfaction may be interrelated (Sirgy and Cornwell, 2002). ● Community satisfaction, neighbourhood satisfaction and housing satisfaction may all be interrelated (ibid.), with relationships with neighbours predicting satisfaction with all three domains; however, community satisfaction is related more to neighbourhood satisfaction than to housing satisfaction (Campbell et al., 1976). ● Regional satisfaction and community satisfaction can be related to a range of factors – such as health, climate, crowding, sporting facilities, housing conditions and pollution – while factors such as overcrowding and travel to work are uniquely related to levels of regional satisfaction (Turksever and Atalik, 2001). Thus, as Ng and Fisher (2013) emphasised, taking account of how subjective QOL and the subjective well-being of people operates at multi-levels is particularly important and needs to be incorporated in research designs investigating QOL.

Investigating subjective quality of life: using survey research methods  83 Using Survey Research Methods to Collect Data on Subjective QOL Generating the data: measuring subjective QOL The investigation of subjective QOL necessitates the generation of individual-level primary data, which inevitably means using survey research methods to collect it. Typically, data on people’s subjective QOL have been measured in a systematic way using a survey instrument (commonly called a questionnaire) where respondents are asked to evaluate or assess various aspects of their life, often using the QOL domains approach discussed above. Over the years, there have been various ways of measuring subjective QOL using surveys to collect the data. By the 1970s, researchers on both sides of the Atlantic were working – sometimes in collaboration – towards that end. One of the pioneering contributions to measuring QOL was by Claus Moser (1970) in the UK. There followed a proliferation of studies measuring subjective QOL and subjective well-being and framing subjective QOL indicators both: (1) through contributions to the academic literature, as exemplified by Campbell et al. (1976) at the University of Michigan in the US and by many contributions to the new journal Social Indicators Research initiated by Alex Michalos in 1974; and (2) through official reports by public agencies, the latter exemplified in the UK by Quality of Life Surveys in Britain undertaken by the Social Science Research Council (SSRC) Survey Unit between 1971 and 1975 (for an overview of that SSRC work, see Hall, 1976). It was significant that the findings of the SSRC Quality of Life Surveys demonstrated clear links between objective and subjective indicators, especially for housing and the local neighbourhood. In measuring subjective QOL, it has been usual to ask the survey respondents to rate their level of satisfaction with their QOL as a whole, and also with respect to QOL domain. The survey questionnaires used a standard response format, typically being undertaken using a Likert scale (1–5), which yields a numerical rating (see Cummins, 1996; Headey and Wearing, 1992; Salvatore and Muñoz Sastre, 2001; Trauer and Mackinnon, 2001). But as referred to earlier, aspects of subjective QOL might not hold the same importance for everyone, thus the evaluation of the importance of each aspect of QOL might need to be built into the survey questionnaire (Campbell et al., 1976; Gill and Feinstein, 1994). Systematic collection of subjective QOL/well-being As mentioned above, the collaboration occurring in the early 1970s between researchers at the University of Michigan in the US and at the SSRC Survey Unit in the UK initiated a succession of major surveys. Many of the surveys – particularly in the UK – created a range of subjective QOL indicators that were relevant to areas of social policy concern – such as housing, health, employment and the environment. The surveys generated data measuring life satisfaction in general and levels of satisfaction with various life domains and sub-domains. These included levels of satisfaction with living domains – including housing and neighbourhoods. At the same time, the surveys covered self-reported ‘objective’ measures of a respondent’s current situation with each domain and sub-domain. The researchers were also aware that personality traits might determine variation in satisfaction levels, including personal efficacy and disposition to trust other people. The surveys became increasingly comprehensive, enabling not only the investigation of people’s general sense of subjective well-being, but also the importance of and variability in satisfaction with many life domains and sub-domains. In addition, it was

84  Handbook of quality of life research possible to investigate the links between subjective indicators and the self-reported objective measures. The design of those surveys on both sides of the Atlantic were used to develop valid and reliable subjective social indicators that could be set alongside standard economic and statistical indicators of national importance. They became widely used by researchers, and some of the indicators were incorporated into major national surveys as governments in numerous countries adopted them as measures of national well-being. This has particularly been the case in Europe – for example, the European Social Survey, and the Organisation for Economic Co-operation and Development’s efforts to measure subjective well-being.

MODELLING APPROACHES Various theoretical and modelling approaches have been used by researchers in investigating subjective QOL. McCrea et al. (2011, pp. 60–68) provide an overview, summarised in what follows. A Broad Conceptual Model Framework A significant model framework for investigating subjective QOL across the different levels of scale domains referred to above was that developed by researchers at the University of Michigan (Campbell et al., 1976; Marans and Rodgers, 1975). That model framework is shown in Figure 6.1. Importantly, the figure incorporates two-way arrows indicating the potential interrelations between subjective QOL and the housing, neighbourhood and community domains and with attributes of those domains. That is, the model sought to investigate the potential determinants of satisfaction with the situational or environmental setting or context in which people live. The model framework thus sought to link objective characteristics and levels of satisfaction with dwelling, neighbourhood and community domains, and with people’s subjective assessment of overall life. As indicated in the figure, the framework may also be used to investigate other potential relationships, such as the moving intentions of people. This broad conceptual modelling approach has incorporated other models that might relate to parts of the model framework. McCrea et al. (2011, pp. 61–2) discuss how, for example, the subjective judgements of people can be influenced by both bottom-up and top-down processes (see Lance et al., 1989, 1995; Michalos and Zumbo, 1999). Bottom-up and top-down models A bottom-up model might be incorporated into Figure 6.1, whereby satisfaction in life domains – such as neighbourhood and community satisfaction – may be predicted by satisfactions in sub-domains like neighbourhood safety or friendliness of neighbours, with such specific subjective evaluations being used to predict more global subjective evaluations (examples include Campbell et al., 1976; Cummins, 1996; Ibrahim and Chung, 2003; Marans and Rodgers, 1975; Sirgy and Cornwell, 2001, 2002). However, as McCrea et al. (2011, p. 62) note, subjective evaluations of environment also need to relate to objective characteristics of an environmental setting (see Galster and Hesser, 1981; Marans and Rodgers, 1975; McCrea, 2007). Chapter 7 discusses that integrated approach to investigating QOL.

Investigating subjective quality of life: using survey research methods  85

Source: Derived from Marans and Rodgers (1975) and Campbell et al. (1976).

Figure 6.1

A broad model framework for investigating subjective assessment of determinants of satisfaction with the residential environment

A top-down model may be incorporated into Figure 6.1 by the arrow from people’s personal characteristics – such as personality and self-esteem – to satisfaction in domain levels and satisfaction in life domains (see Diener, 1984; Hart, 1999; Hayes and Joseph, 2003; Vitterso, 2001; Vitterso and Nilsen, 2002). As McCrea et al. (2011, p. 62) show, such top-down models reflect stable individual differences that might influence subjective evaluations (see Headey and Wearing, 1992). Mood bias models Psychologists have found that mood biases affect a range of subjective judgements. These include, for example: ● ● ● ●

persuasion (Petty et al., 1993); stereotyping (Roesch, 1999); self-conceptions (Sedikides, 1995); and life satisfaction (Abele and Gendolla, 1999; Schwarz and Strack, 1999; Schwarz et al., 1987).

Mood biases may be controlled using measures for positive and negative affect, which may also control for personality traits such as extroversion (Diener et al., 1999). However, as discussed by McCrea et al. (2011, p. 63), there is debate about the underlying mechanisms causing mood bias. Positive and negative moods might be used as information if one consults one’s feelings when making subjective judgements (see Clore and Tamir, 2002;

86  Handbook of quality of life research Schwartz and Strack, 1999). And affect may be more likely to be consulted as information in more global subjective judgements such as overall life satisfaction – which are complex judgements – that may be used as a simplifying heuristic to reduce the cognitive burden (see Schwartz and Strack, 1999). Conversely, affect might be less likely to be consulted in more specific and less complex evaluations involving specific life domains. Positive moods may facilitate retrieval of positive memories, while negative moods may facilitate retrieval of negative memories when making subjective judgements irrespective of domain specificity (see Bower, 1981; Clark and Williamson, 1989; Forgas, 1995). However, it is interesting that McCrea (2007) found little mood bias when predicting more specific subjective evaluations of the urban environment, while about 3 per cent bias was found when predicting more global domains such as subjective QOL. This finding supports the ‘affect-as-information’ mechanism in QOL. Subjective judgement models Michalos (1985) has reviewed a range of theories that incorporate ‘standards of comparison’ into subjective judgement models. Examples are: ● ● ● ● ●

aspiration theory; equity theory; cognitive dissonance theory; reference group theory; and social comparison theory.

Subjective judgement models are common in QOL research (for example, Abele and Gendolla, 1999; Brickman and Campbell, 1971; Brickman et al., 1978; Marans and Rodgers, 1975; Meadow et al., 1992; Michalos, 1985, 1986; Schwarz et al., 1987; Wright, 1985). Subjective judgement models suggest that people’s subjective evaluations depend on the differences between the attributes of a judgement ‘target’ and the ‘standards of comparison’ rather than just on the attributes of a judgement target. As a result, individual variations in ‘standards of comparison’ may potentially significantly influence subjective evaluations. For example, people might evaluate the same environment differently in terms of being positive or negative about it. As pointed out by McCrea et al. (2011, p. 63), this may weaken the relationships between objective dimensions and subjective evaluations of an environmental setting such as an urban environment. Subjective judgement models may be used to investigate the effect of psychological processes on people’s subjective evaluations of objective characteristics. Adaptation models While not incorporated in Figure 6.1, adaptation is another behaviour that might weaken relationships between objective dimensions and people’s subjective evaluations of domains. As stated in McCrea et al. (2011, p. 64): ‘with adaptation a person’s perceptions of standards of comparison merge over time such that initially strong positive or negative subjective evaluations become more moderate over time’. Kahneman (1999) shows that this merging over time of ‘perceptions’ and ‘standards of comparison’ occurs through two different processes, namely: (1) by adjusting sensory perceptions, whereby over time initially striking perceptions of phenomena may become less noticeable – what is referred to as the ‘hedonic treadmill’; and (2) by adjusting standards of comparison, whereby standards become increasingly influenced

Investigating subjective quality of life: using survey research methods  87 by everyday expectations associated with living in a specific environmental setting – what is referred to as the ‘satisfaction treadmill’. Cummins (2000) and Cummins and Nistico (2002) have proposed another adaptation model called the theory of homeostasis, in which cognitive biases serve as functional devices promoting positive or negative evaluations in a person’s life circumstances. This involves adjustment of targets and standards that weaken the relationships between ‘targets’ and subjective evaluations. Diener et al. (2006) provide evidence for this in various life domains, but it is unclear how important adaptation is in domains in settings such as an urban area. Subjective importance models Individual and social group differences are also important influences in the subjective importance that people ascribe to attributes of environmental settings and this may influence the links between objective and subjective perceptions people have of the situational environment. McCrea (2007) shows how this might be investigated by weighting objective dimensions by subjective importance to predict subjective evaluations of an environmental setting. There is considerable research that has used weighting satisfaction in life domains and sub-domains by the subjective importance people ascribe to them in predicting more global subjective evaluations of satisfaction. In Figure 6.1 this is encapsulated by the arrow from personal characteristics to relationships between subjective evaluations and satisfaction in a situational environment such as a city. While there is debate over whether it is necessary to weight subjective evaluations when predicting more global subjective evaluations (see Hsieh, 2003, 2004; Trauer and Mackinnon, 2001; Wu and Yao, 2006), it has been shown that weighting subjective evaluations by importance does not significantly improve prediction of satisfaction in more global domains (see Andrews and Withey, 1976; Campbell et al., 1976; Cummins et al., 1994; Mastekaasa, 1984: Russell et al., 2006). Residential relocation models Following the landmark study by the sociologist Rossi (1955) on why families move, researchers in many of the disciplines in the social sciences have been interested in understanding the processes involved in people’s decisions to relocate and choose a dwelling. In the context of QOL research, the broad conceptual model depicted in Figure 6.1 has been used and extended to investigate behavioural processes, such as residential relocation decision and choice, which have been of particular interest to geographers. This would involve extending the model in Figure 6.1 by adding another arrow from the ‘moving intentions’ box back to the objective characteristics of the environmental setting such as an urban area to produce a feedback loop. The notion is that differences in the subjective importance of attributes in the situational environment might influence those links between objective dimensions and subjective evaluations of the urban environment by influencing people’s decisions on where to live. This has also been discussed in detail by Brown and Moore (1970) and Golledge and Stimson (1997). Research investigating the residential relocation process has been pursued by taking both a functionalist and a behavioural perspective: ● The functionalist approach assumes that people are rational in their housing choice by seeking to maximise utility, minimise costs, trade off travel and housing costs, and max-

88  Handbook of quality of life research imise house expenditure, or trade off housing quality with location status (see Phe and Wakely, 2000). ● The behavioural approach examines the underlying processes in people’s decision-making (see Brown and Moore, 1970), which involves two phases: ● In the first phase, people (households) compare their objective environment, evaluating their levels of satisfaction with elements of that environment along with making subjective judgements about whether their needs and aspirations are being met. This process thus involves identifying what are called stressors, which may be both location specific and dwelling specific. Stressors are based on the subjective assessment of various attributes of the urban environment and of a dwelling through consideration of the resident’s needs and aspirations. If that cognitive appraisal is unfavourable then the resident becomes dissatisfied with their place utility and may decide to move. It may lead to the person/household undertaking a search process to find a new residential location and dwelling, which is the second phase of the process. ● In the second phase of the process, the criteria for evaluating and choosing a new location and dwelling may be adjusted as part of the search process as people’s aspirations may not be achievable and will need to be modified. Researchers have developed and tested a number of residential relocation models that encapsulate these behavioural processes. They have incorporated consideration of a wide range of objective elements of both the urban environment and of a dwelling and what may influence people’s subjective evaluations of those elements is meeting a range of needs and aspirations that may play a part in shaping their decision to undertake a search process and the subsequent residential relocation choice of both location and dwelling. By way of example, the reader is referred to the initial modelling framework proposed by Brown and Moore (1970), and to a large number of later modelling studies (see Amerigo and Aragones, 1997; Chiang and Hsu, 2005; Clark et al., 2000; Clark and Huang, 2003, 2004; Clark and Ledwith, 2006; Desbarats, 1983; Dieleman and Mulder, 2002; Fredland, 1974; Ge and Hokao, 2006; Kim et al., 2005; Pacione, 1990; Speare et al., 1975). Of specific significance in the context of subjective QOL research is the study by McCrea (2007) who explicitly sets out to adapt the Brown and Moore (1970) model of the residential relocation processes to explore the potential effects of the links between objective dimensions and subjective evaluations of the urban environment in the Brisbane-South East Queensland region in Australia. Using Agent-based Modelling A useful methodological development in examining subjective QOL is agent-based modelling, which ‘facilitates the examination of system-level outcomes of the heterogeneous outcomes of a set of heterogeneous agents’ (Fernandez et al., 2005, p. 799). McCrea et al. (2011) suggest that this has the potential to enrich the analysis of QOL data by ‘providing insights into how agents act in the urban development process and how that interfaces with subjective – and indeed objective – QO(Urban)L’ (p. 68). Agent-based modelling has been used in research into a number of behaviours in spatial contexts using both data from aggregate outcomes to evaluate phenomena such as city form, and data on individual or agent decisions involving role-playing to collect information on agents. For example, Fernandez et al. (2005) developed an application of agent-based model-

Investigating subjective quality of life: using survey research methods  89 ling using subjective QOL data from the Detroit Area Study (DAS; see Marans and Kweon, 2011). Modelling was used to investigate land use and land cover change as a means to better understand human–environment interactions at the urban fringe of the region. Fernandez and colleagues set out to model land development patterns as a function of agent preferences with respect to proximity to jobs and services, landscape aesthetics and quality, and different urban densities, and to consider the influence of agent characteristics (age, marital status, race, income). Data from the DAS study covering people’s residential preferences were used. The modelling involved the comparison of approaches to characterising heterogeneous preferences of agents based on a factor analysis of resident responses to survey questions about their reasons for moving to their current residential location. First cluster analysis determined the number of different types of residents according to respondent preferences. Then relationships between their demographic and socio-economic characteristics and their location preferences were evaluated using regression analysis to examine the ‘fit’ of the relationship to ascertain the degree to which individual characteristics of residents predicted preferences. The modelling enabled the researchers to identify the degree to which preferences are independent of the demographic and socio-economic characteristics of residents. This then enabled the researchers to determine whether to use preference groups or to assign the preferences of agents based on the demographic and socio-economic characteristics of agents.

CONCLUSION Empirical analysis of subjective QOL has proliferated since the 1970s using individual unit record data collected through questionnaires administered as part of sample surveys. Studies have used a variety of model frameworks to analyse how psychological processes – such as feelings – and a host of other factors might affect people’s subjective evaluations/judgements of their overall QOL. This chapter has discussed the use of sample surveys to generate primary data to measure subjective well-being, QOL, and its various domains including those related to place. Such surveys have included people’s assessments of their surroundings, ranging from the individual dwelling to the wider community and its environmental setting. The chapter has also shown how subjective measures of QOL and its various domains might be related though conceptual models. Additional models are likely to emerge over time as theoretical advances are made in fields ranging from psychology and psychiatry to architecture, landscape architecture and urban and environmental planning. Furthermore, social surveys will likely continue as the dominant methodological approach to studying QOL and the quality of its various domains including those related to place.

NOTE 1.

The authors acknowledge the contributions of John F. Hall, a pioneering scholar in the development of subjective social indicators in the United Kingdom and elsewhere during the 1970s. Now retired, Hall was a senior research fellow at the Survey Unit of the UK’s Social Science Research Council and later taught at London Metropolitan University. In the early stages of writing this chapter, Hall provided valuable input on the historical aspects of survey research dealing with quality of life.

90  Handbook of quality of life research

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92  Handbook of quality of life research Kim, T.K., Horner, M.W. and Marans, R.W. (2005), ‘Life cycle and environmental factors in selecting residential and job locations’, Housing Studies, 20, 457–73. Lance, C.E., Lautenschlager, G.J., Sloan, C.E. and Varca, P.E. (1989), ‘A comparison between bottom-up, top-down, and bidirectional models of relationships between global and life facet satisfaction’, Journal of Personality, 57, 601–24. Lance, C.E., Mallard, A.G. and Michalos, A.C. (1995), ‘Tests of the causal directions of global life facet satisfaction relationships’, Social Indicators Research, 34, 69–92. Lu, M. (1999), ‘Determinants of residential satisfaction: ordered logit vs. regression models’, Growth and Change, 30, 264–87. Marans, R.W. and Kweon, B.-S. (2011), ‘The quality of life in Metro Detroit at the beginning of the millennium’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 163–83. Marans, R.W. and Rodgers, W. (1975), ‘Toward an understanding of community satisfaction’, in A. Hawley and V. Rock, (eds), Metropolitan America in Contemporary Perspective, New York, Halsted Press, pp. 299–352. Mastekaasa, A. (1984), ‘Multiplicative and additive models of job and life satisfaction’, Social Indicators Research, 14, 141–63. McCrea, R. (2007), ‘Urban quality of life: linking objective dimensions and subjective evaluations of the urban environment’, unpublished PhD thesis, The University of Queensland. McCrea, R., Marans, R.W., Stimson, R.J. and Western, J. (2011), ‘Subjective measurement of quality of life using primary data collection and the analysis of survey data’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 55–75. McCrea, R., Stimson, R. and Western, J. (2005), ‘Testing a moderated model of satisfaction with urban living using data for Brisbane-South East Queensland, Australia’, Social Indicators Research, 72, 121–51. Meadow, H.L., Mentzer, J.T., Rahtz, D.R. and Sirgy, M.J. (1992), ‘Life satisfaction measure based on judgment theory’, Social Indicators Research, 26, 23–59. Michalos, A.C. (1985), ‘Multiple discrepancies theory (MDT)’, Social Indicators Research, 16, 347–413. Michalos, A.C. (1986), ‘An application of multiple discrepancies theory (MDT) to seniors’, Social Indicators Research, 18, 349–73. Michalos, A.C., Hubley, A.M., Zumbo, B.D. and Hemingway, D. (2001), ‘Health and other aspects of the quality of life of older people’, Social Indicators Research, 54, 239–74. Michalos, A.C. and Zumbo, B.D. (1999), ‘Public services and the quality of life’, Social Indicators Research, 48, 125–56. Mill, J.S. ([1863] 1998), ‘Utilitarianism’, in L.P. Pojman (ed.), Ethical Theory: Classical and Contemporary Readings, 3rd edition, Melbourne: Wadsworth Publishing Company, pp. 149–51. Moser, C. (1970), ‘Measuring quality of life’, New Society, 428, 1042–3. Ng, E.C. and Fisher, A.T. (2013), ‘Understanding well-being in multi-levels: a review’, Health, Culture and Society, 5, 308–23. Oliver, N., Holloway, F. and Carson J. (1995), ‘Quality of life’, Journal of Mental Health, 4, 1–4. Pacione, M. (1990), ‘Urban liveability: a review’, Urban Geography, 11, 314–39. Pacione, M. (2003), ‘Quality-of-life research in urban geography’, Urban Geography, 24, 314–39. Parkes, A., Kearns, A. and Atkinson, R. (2002), ‘What makes people dissatisfied with their neighbourhoods?’, Urban Studies, 39, 2413–38. Petty, R.E., Schumann, D.W., Richman, S.A. and Strathman, A.J. (1993), ‘Positive mood and persuasion: different roles for affect under high- and low-elaboration conditions’, Journal of Personality and Social Psychology, 64, 5–20. Phe, H.H. and Wakely, P. (2000), ‘Status, quality and other trade-off: towards a new theory of urban residential location’, Urban Studies, 37, 7–35. Roesch, S.C. (1999), ‘Modelling the direct and indirect effects of positive emotional and cognitive traits and states on social judgements’, Cognition and Emotion, 13, 387–418. Rossi, P.H. (1955), Why Families Move: A Study in the Social Psychology of Urban Residential Mobility, Glencoe, IL: Free Press.

Investigating subjective quality of life: using survey research methods  93 Russell, L.B., Hubley, A.M., Palepu, A. and Zumbo, B.D. (2006), ‘Does weighting capture what’s important? Revisiting subjective importance weighting with a quality of life measure’, Social Indicators Research, 75, 141–67. Salvatore, N. and Muñoz Sastre, M.T. (2001), ‘Appraisal of life: “area” versus “dimension” conceptualizations’, Social Indicators Research, 53, 229–55. Schwarz, N. and Strack, F. (1999), ‘Reports of subjective well-being: judgmental processes and their methodological implications’, in D. Kahneman and E. Diener (eds), Well Being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation, pp. 61–84. Schwarz, N., Strack, F., Kommer, D. and Wagner, D. (1987), ‘Soccer, rooms, and the quality of your life – mood effects on judgments of satisfaction with life in general and with specific domains’, European Journal of Social Psychology, 17, 69–79. Sedikides, C. (1995), ‘Central and peripheral self-conceptions are differentially influenced by mood – tests of the differential sensitivity hypothesis’, Journal of Personality and Social Psychology, 69, 759–77. Sirgy, M.J. and Cornwell, T. (2001), ‘Further validation of the Sirgy et al.’s measure of community quality of life’, Social Indicators Research, 56, 125–43. Sirgy, M.J. and Cornwell, T. (2002), ‘How neighborhood features affect quality of life’, Social Indicators Research, 59, 79–114. Speare, A., Goldstein, S. and Frey, W.H. (1975), Residential Mobility, Migration, and Metropolitan Change, Cambridge, MA: Ballinger. Trauer, T. and Mackinnon, A. (2001), ‘The role of importance ratings in quality of life measurement’, Quality of Life Research, 10, 579–85. Turksever, A.N.E. and Atalik, G. (2001), ‘Possibilities and limitations for the measurement of the quality of life in urban areas’, Social Indicators Research, 53, 163–87. Veenhoven, R. (2000), ‘The four qualities of life: ordering concepts and measures of the good quality’, Journal of Happiness Studies, 1, 1–39. Vitterso, J. (2001), ‘Personality traits and subjective well-being: emotional stability, not extraversion, is probably the important predictor’, Personality and Individual Differences, 31, 903–14. Vitterso, J. and Nilsen, F. (2002), ‘The conceptual and relational structure of subjective well-being, neuroticism, and extraversion: once again, neuroticism is the important predictor of happiness’, Social Indicators Research, 57, 89–118. Wright, S.J. (1985), ‘Health satisfaction – a detailed test of the multiple discrepancies theory model’, Social Indicators Research, 17, 299–313. Wu, C.H. and Yao, G. (2006), ‘Do we need to weight satisfaction scores with importance ratings in measuring quality of life?’, Social Indicators Research, 78, 305–26.

7. Integrating subjective and objective measures in quality of life research Robert J. Stimson, Rod McCrea, Robert W. Marans and Noah J. Webster

INTRODUCTION Chapter 1 discussed how quality of life (QOL)/well-being is a broad concept encompassing notions about a life that is good, valued, satisfying and happy. QOL is primarily about people’s subjective well-being (SWB), which has a positive or a negative effect on an individual or a group such as a household (Andrews and Withey, 1976; Diener et al., 1999). While QOL is typically investigated in terms of people’s perceptions and experiences, investigations also need to take account of the situational, place or spatial context of where people live, work and play. A key question is how do objective environmental settings at various levels of scale – from the dwelling to the neighbourhood and the wider city or region where people live – affect people’s subjective evaluations or assessments of their QOL as a whole and/or for specific QOL domains? This integrative approach to the investigation of QOL/well-being is particularly important from a policy, planning and design perspective as it infers that people’s QOL may be enhanced through interventions shaping or changing the objective environment. This integrative approach to investigating QOL can become complex, involving more than linking measures of the subjective evaluation of QOL and objective measures of phenomena relating to people and their situational context. Pacione (2003) proposes a useful framework for thinking about those complex relationships. This framework integrates six dimensions relating to QOL, namely: objective, subjective, time, domain specificity, geographic scale and social group (Figure 7.1). This chapter discusses key aspects of the integrative approach to investigating QOL. It explores the nature of linkages between the objective environment in which people live out their daily lives, the socio-economic and demographic characteristics of people and households, and their subjective assessment of that environmental context and ultimately their subjective assessment of their overall QOL. Specifically, the chapter discusses how that integration may enhance our understanding of the complex relationships between the subjective and objective elements of QOL, which has been identified as a significant research gap by many researchers (such as Andelman et al., 1998; McCrea, 2007). The chapter draws on the overview of the integrated approach to investigate QOL provided by McCrea et al. (2011).

94

Integrating subjective and objective measures in quality of life research  95

Source: Pacione (2003).

Figure 7.1

A six-dimensional framework for QOL research

THEORETICAL PERSPECTIVES ON LINKING SUBJECTIVE AND OBJECTIVE APPROACHES In pursuing an integrated approach to investigate QOL/well-being, researchers have used several theoretical perspectives – discussed below – that relate physical and social dimensions of environmental settings to subjective evaluations of people’s assessment of their QOL in those settings and the characteristics of individuals and households. Theories Relating to the Environmental Setting Optimal centrality theory Optimal centrality theory (see Archibugi, 2001; Cicerchia, 1999) relates to urban density and access to urban services and the overloading of urban structure. It is postulated that there is an optimum urban scale or urban size that may maximize the benefits the city scale effect and the costs or negative consequences (such as overcrowding, pollution and environmental degradation) of urban load. That represents a trade-off. Also, the costs of urban load – which is about the negative externalities associated with a large city, such as congestion and high housing prices – can detract from the QOL of individuals and households. The theory has been extended to consider the effect of urban density on QOL (see below), suggesting that higher density induces subjective overload as well as having benefits associated with scale.

96  Handbook of quality of life research Access to services and facilities Access to urban services and facilities is an important component of subjective QOL (see Glaeser et al., 2000; Rogerson et al., 1989, 1996). Access is also important in enhancing community satisfaction (Brown and Moore, 1970; Campbell et al., 1976; Sirgy and Cornwell, 2001; Sirgy et al., 2000; Turksever and Atalik, 2001), and in making residential location decisions (Chiang and Hsu, 2005; Dokmeci and Berkoz, 2000; Ge and Hokao, 2006; Mitrany, 2005). However, in examining the strength of the relationships between objective access and subjective access to services and facilities, McCrea (2007) demonstrates that it is not just about proximity, with research showing how the relationship might be weakened by individual variations in the perceived importance of access to services and facilities (Dokmeci and Berkoz, 2000; Kim et al., 2005; McCrea et al., 2014). Urban density and overloading Research shows that high density and rapid urban growth environments are associated with increased economic, social and environmental stress (Perz, 2000; Schwirian et al., 1995), with people preferring lower-density urban environments (Brown et al., 1997; Filion et al., 2006; Schwanen and Mokhtarian, 2004; Senecal and Hamel, 2001). However, these environments may also be predictors of better subjective QOL (Baldassare and Wilson, 1995), a contradiction possibly explained by optimal centrality theory, whereby residents living in higher-density urban environments have better access to services and facilities (Mitrany, 2005), which compensates for increasing urban load. It is evident that a range of problems may be associated with urbanisation, which negatively impacts subjective QOL. Kemp et al. (1997), Marans (2003), McCrea et al. (2005) and McCrea (2007) have examined these problems at a broad level, investigating the relationships between objective density and perceived overloading, where the latter was a composite measure of urban problems such as pollution, loss of natural areas, traffic congestion and cost of housing. Natural environments Proximity to natural environments – such as rural and coastal – have been found to facilitate recovery from stress (Berto, 2005; Kaplan, 1995; Ulrich et al., 1991). In contrast, higher levels of stress are frequently found in denser and more crowded urban environments (Walmsley, 1988). Preferences for suburban and low-density living might then be explained in part by closer proximity as well as people’s attraction to natural environments due to their restorative effects on stress associated with urban living (Van den Berg et al., 2007). Thus, proximity to rural and coastal (and other water) environments can be associated with favourable subjective evaluations of the natural environment, which in turn should be positively associated with subjective QOL. However, preferences for the natural environment vary between people (Vogt and Marans, 2004), with families with children being more likely to prefer living in neighbourhoods with green space and recreational opportunities (Kim et al., 2005). Differences between people’s preferences and their residential location choices may weaken the relationship between proximity to natural environments and subjective evaluations of the urban environment.

Integrating subjective and objective measures in quality of life research  97 Theories Relating to the Social Environment Subjective evaluations of social environments are related to subjective QOL via the satisfaction of the social needs of residents, such as favourable neighbourly relations and a sense of community (Davidson and Cotter, 1991; Farrell et al., 2004; Sirgy and Cornwell, 2002). Favourable neighbourly relations are one component of social capital, which also incorporates trust and reciprocity (Coleman, 1988; Putnam, 1995), as well as general friendliness between neighbours. A sense of community might be met through a shared commitment and a sense of belonging (McMillan and Chavis, 1986). Research shows that a sense of community is closely related to positive relations among neighbours (Farrell et al., 2004; Prezza et al., 2001). This relationship supports examining the subjective social environment as a broad construct. Objective dimensions of social environments often relate to household structure, socio-economic status and ethnicity. In a factorial ecology study of the social and spatial structure of the Brisbane-South East Queensland metropolis in Australia, Western and Larnach (1998) identified these dimensions, as well as a disadvantage dimension (that is, unemployment, single parenthood and public housing occupancy). McCrea (2007) has examined these objective social dimensions of the urban environment in relation to respondents’ subjective evaluations of their QOL. Social disorganisation theory This theory predicts that neighbourhood social ties would be stronger (that is, more organised) in neighbourhoods that are: ● ● ● ●

more stable (that is, with less residential mobility); more affluent (for example, more community facilities and resources); less disadvantaged (for example, fewer social problems); and more ethnically homogeneous (for example, fewer ethnic minorities).

This was shown in the pioneering research of Shaw and McKay (1942) and Sampson and Groves (1989). Social disorganisation theory can be used to study how objective social dimensions may impact people’s subjective evaluations of the social environment since ‘socially organised’ neighbourhoods also have more favourable neighbourly interactions and a greater sense of community, thus supporting relationships between objective social dimensions and subjective evaluations of the social environment. For example: ● less social capital and sense of community may be found in more disadvantaged neighbourhoods (Cantillon et al., 2003; Kawachi et al., 1999); ● less social cohesion among neighbours may be found in disadvantaged and less residentially stable neighbourhoods (Sampson et al., 1997); and ● higher neighbourhood attachment and involvement may be found in more affluent and more residentially stable neighbourhoods (Taylor, 1996). However, the direct effects of objective social dimensions on subjective evaluations of the social environment have not been found to be strong.

98  Handbook of quality of life research Sub-culture theory This postulates that in urban environments, when the population becomes large enough, the formation of sub-cultures to manifest spatially becomes more possible by allowing people of similar social backgrounds and with similar lifestyles to live in close proximity. Once areas become associated with particular sub-cultures, they attract others of similar backgrounds through selective residential location decisions (Savage et al., 2003). The underlying process is driven by a preference of many residents to live in neighbourhoods with similar others, which is a form of ‘homophily’ (see Lazarsfeld and Merton, 1954; McPherson et al., 2001), facilitating the generation of intra-urban spatial variation in sub-cultures (Fischer, 1984) as opposed to a more general urban way of life (see Simmel, 1950; Wirth, 1938). Homophily can be literally translated as ‘love of the same’ and is encapsulated in the phrase ‘birds of a feather flock together’. Urban sub-cultures and homophily might also explain the weak relationships between objective social dimensions and subjective evaluations of the social environment. As with other dimensions of the urban environment, the strength of relationships may depend on the extent to which people consider an attribute of the urban environment to be important. Regarding social dimensions, similarity with others also depends on the social characteristics of the residents. For example, people who consider that living near similar others is important may well evaluate the social environment more favourably if they live in a neighbourhood that has social dimensions similar to their own social characteristics. This type of homophily is what Lazarsfeld and Merton (1954) call ‘status homophily’. Lazarsfeld and Merton (1954) also distinguished between ‘status homophily’ and ‘value homophily’ (McPherson et al., 2001). It is suggested that intra-urban spatial variation in sub-cultures may not only develop around social characteristics of residents such as socio-economic status or ethnicity, but may also be based on different values or lifestyles (Curry et al., 2001; Ge and Hokao, 2006; Walmsley, 1988). However, according to McCrea (2007), these two types of homophily are not mutually exclusive due to finding that both the role of status homophily based on social characteristics of people and their neighbourhood play a role in understanding the relationships between objective social dimensions and subjective evaluations of the social environment. Other General Theories Linking Objective and Subjective Quality of Life McCrea (2007) discusses a number of more general theories linking objective and subjective QOL indicators, including: ● ● ● ● ● ● ● ●

bottom-up models; top-down models, mood bias models; subjective judgement models; adaptation models; subjective importance models; residential relocation models; and agent-based models.

A common theme across these theories is that psychological processes, individual differences and residential relocation processes serve to weaken direct links between objective and sub-

Integrating subjective and objective measures in quality of life research  99 jective indicators of QOL. Nonetheless, we might still expect to find significant relationships between subjective evaluations and objective characteristics of the urban environment.

EMPIRICALLY INVESTIGATING THE LINKS BETWEEN OBJECTIVE PHENOMENA AND SUBJECTIVE QUALITY OF LIFE Many studies have attempted to overcome the apparent dichotomy between the subjective and the objective approaches to the study of QOL/well-being by including both types of indicators. Nevertheless, few investigations interrelate them by developing research studies designed to explicitly examine the links between objective characteristics of the urban environment and people’s subjective evaluations of the urban environment. Studies attempting to bridge that gap include the pioneering research conducted at the Institute for Social Research (ISR) at the University of Michigan in the US (Campbell et al., 1976; Marans and Rodgers, 1975), along with more recent research by Marans (2003) and a group at The University of Queensland in Australia (McCrea, 2007; McCrea et al., 2006). In general, studies have found the direct links between objective and subjective indicators to be weak. Reviews by Evans and Huxley (2002) and McCrea (2007) concluded that objective circumstances do not greatly influence subjective QOL (for example, Bowling and Windsor, 2001; Headey et al., 1984; Schwarz and Strack, 1999) and that life domain satisfactions are better predictors of overall life satisfaction than objective circumstances (see Andrews and Withey, 1976). However, it also seems that objective circumstances are more related to satisfaction in related specific life domains than to overall life satisfaction, suggesting that relationships between objective circumstances and overall life satisfaction are mediated via satisfaction in various life domains (Evans and Huxley, 2002). To enhance both objective and subjective QOL research, designs are needed that explicitly seek to investigate the links between the objective characteristics of the urban environment and the subjective evaluations of the urban environment. Research by McCrea and collaborators (McCrea, 2007; McCrea et al., 2006) is an example of how that may be done. Unfortunately, most of the research on QOL focuses either on subjective evaluations or objective characteristics of the urban environment. While the objective and subjective approaches are discussed in Chapters 5 and 6, respectively, the sections that follow summarise some of the findings from research that has specifically focused on the analysis of the urban environment. A Focus on Subjective Evaluations of the Urban Environment Studies focusing primarily on the subjective evaluation of QOL have found that people’s subjective evaluations of many aspects of the urban environment can contribute to satisfaction with urban living and overall life satisfaction. Examples include the following: ● Michalos and Zumbo (1999) predicted life satisfaction from 14 life domains for seven different time periods between 1979 and 1997. This included the following explicitly urban domains relating to QOL: ● housing was significant in six time periods; ● recreational activity in five;

100  Handbook of quality of life research ● transportation in four; ● government services in three; and ● residential area was significant in two time periods (although it was not included in one of the seven time periods). Thus, satisfaction in various urban domains was shown to be predictors of overall life satisfaction. ● Examining different geographic levels of subjective QOL in the Brisbane-South East Queensland metropolis in Australia, McCrea et al. (2005) found that: ● regional satisfaction was best predicted by people’s evaluation of regional services (such as health and education) and the cost of living; ● neighbourhood satisfaction was best predicted by evaluations of social interactions, neighbourhood crime and public facilities (parks, libraries, etc.); and ● housing satisfaction was predicted best by age of home and home ownership. ● Turksever and Ataliks (2001) predicted life satisfaction in seven districts of Istanbul – as well as the Istanbul region as a whole – using satisfaction with 18 different aspects of living in the region. The significance of different predictors varied across the districts. However, for the Istanbul region as a whole, the significant predictors were: ● health; ● climate; ● crowding; ● sporting facilities; ● housing conditions; ● travel to work; and ● environmental pollution. ● Sirgy and colleagues (Sirgy and Cornwell, 2001; Sirgy et al., 2000) showed that a wide range of specific services provided by government, business and non-profit institutions contributed to community satisfaction and ultimately to overall life satisfaction in combination with satisfaction in other life domains. Many more studies could also be cited, but those referred to above show that subjective evaluations of many aspects of urban living influence subjective dimensions of QOL and overall QOL. The studies also demonstrate that the importance of those factors can vary with geographic scale within urban regions. A Focus on Objective Characteristics of the Urban Environment Studies focusing on objective QOL typically include many objective characteristics of the urban environment, often combining or weighting objective indicators to generate an objective QOL index used for ranking places (see Blomquist et al., 1988; Boyer and Savageau, 1981; Cicerchia, 1999; Stover and Leven, 1992). Interestingly, some studies have also emphasised the trade-off between positive and negative aspects of urban living. An example is Blomquist et al. (1988) who modelled the trade-off between housing costs, wages and amenities to develop QOL indices for 253 Standard Metropolitan Statistical Areas across the US. Hedonic wage and rent equations were used to derive implicit prices for various urban amenities, which in turn were used as weights in compiling an objective overall QOL

Integrating subjective and objective measures in quality of life research  101 index. The underlying idea was that the amenity value of an area is implied from areal variation in housing costs and wages. Another example is Cicerchia (1999) who theorised about the trade-off between city (urban) effect and urban load, finding that: (1) city effect related to access to superior urban functions, opportunities and services available by virtue of a city’s size; and (2) urban load related to a number of negative consequences of urban growth (for example, congestion and environmental degradation). The underlying idea was to ascertain the ‘optimum centrality’ for a city, which is the size of a city that maximises the difference between the city effect over urban load. If the city size becomes too large, then urban load may exceed the city effect and create urban overload.

MODELLING THE LINKS BETWEEN OBJECTIVE AND SUBJECTIVE INDICATORS OF QUALITY OF LIFE Numerous integrative frameworks or models have been proposed by researchers to explicitly investigate relationships between subjective evaluations of global QOL and specific QOL domains with objective phenomena relating to the characteristics of people and the situational setting and elements of the environment. An early model proposed by researchers at the University of Michigan’s ISR formulated a bottom-up model to investigate paths between dimensions of aspects of the objective conditions or attributes at different levels of scale and the subjective evaluation of QOL (Campbell et al., 1976) (refer to Figure 6.1 in Chapter 6). Subsequently, numerous studies have modified and extended that original model, one such modification being the study of QOL in the Brisbane-South East Queensland metro region conducted by researchers at The University of Queensland in Australia in the late 1990s and 2000s (McCrea, 2007; McCrea et al., 2005, 2006). The modified model is shown in Figure 7.2.

Source: McCrea (2007, p. 176).

Figure 7.2

An integrated model of QOL used in the Brisbane-South East Queensland study

102  Handbook of quality of life research The shaded boxes shown at the bottom of the figure indicate objective characteristics (such as traffic noise measured in decibels) might predict subjective perceptions (such as hearing the noise), which predicts subjective evaluations (that is, the traffic noise is too loud), which predicts satisfaction in various urban domains. The boxes in the model indicate that the subjective perceptions and evaluations of objective attributes can occur at three levels of scale – housing, neighbourhood and the wider community – all of which may be influenced by personal characteristics, and that these may interact to impact levels of satisfaction with those urban domains. Interactions among all of these may influence people’s residential moving intentions, impact their overall QOL and their assessment of specific QOL domains. Linking Objective and Subjective Indicators Using GIS Technology and Using Path Analysis: A Case Study When examining links between objective and subjective factors in QOL studies, the characteristics of an environmental setting need to be measured and linked to an individual’s subjective assessments. This may be done using geographic information system (GIS) tools, which can spatially integrate a wide range of data types, and using analytical tools such as structural equation modelling to investigate those links or pathways. McCrea et al. (2006) used this approach in the study of the Brisbane-South East Queensland metro region in Australia. Study methodology In the Brisbane-South East Queensland (SEQ) region QOL study, the survey used to collect data on subjective assessments of QOL involved designing a spatially stratified probability sample of households across the study region and the design of a questionnaire (for details see Chapter 10). The residential locations of survey respondents were geocoded, and other objective spatial data were linked to those locations using GIS tools. A rich and varied dataset was generated encompassing the subjective assessments of overall QOL, QOL domains, objective measures of characteristics of survey respondents and a wide range of objective elements of the situational environment (derived from numerous secondary datasets, including the Australian Census). Operationalising the model to investigate the links and paths among and between the objective and subjective variables involved using structural equation modelling. The modelling tested whether objective measures such as access and overcrowding might predict subjective evaluations of access and overcrowding, which in turn may predict subjective QOL. The strength of the associations between the objective and subjective measures could be explored as well as the extent to which the relationship was mediated by subjective measures of access and overcrowding. Testing hypotheses McCrea et al. (2006) proposed a series of hypotheses to test the links between the specified objective and subjective variables: H1: H2: H3: H4:

Higher objective access significantly predicts higher subjective access. Higher objective density significantly predicts higher subjective overcrowding. Higher objective cost of housing significantly predicts higher subjective overcrowding. Higher subjective access significantly predicts higher subjective QOL.

Integrating subjective and objective measures in quality of life research  103 H5: Lower subjective overcrowding significantly predicts higher subjective QOL. H6: Objective access, objective density and objective cost of housing are significantly correlated with each other. H7: Subjective access and subjective overcrowding are significantly correlated. H8: The relationship between subjective QOL and objective access is mediated by subjective access (that is, there is no direct relationship between subjective QOL and objective access). H9: The relationship between subjective QOL and objective density is mediated by subjective overcrowding (that is, there is no direct relationship between subjective QOL and objective density). H10: The relationship between subjective QOL and objective cost of housing is mediated by subjective overcrowding (that is, there is no direct relationship between subjective QOL and objective cost of housing). Using structural equation modelling McCrea et al. (2006) used structural equation modelling to test the aforementioned hypotheses linking objective dimensions and subjective evaluations of the urban environment. Structural equation modelling was used for two reasons: (1) it can easily test a number of mediated bottom-up paths simultaneously; and (2) it includes measurement models for the latent variables that separate out measurement error from the main structural model of latent variables. The modelling used: (a) 12 manifest subjective variables derived from the QOL survey to measure three latent subjective variables (subjective access, subjective overcrowding and subjective QOL); and (b) eight manifest objective variables to measure three latent objective variables (objective access, objective residential density and objective cost of housing). Subjective QOL was a latent variable measured using four manifest or measured variables: ● neighbourhood satisfaction was based on a question asking survey respondents how much they agreed or disagreed with the statement ‘I feel satisfied living in this neighbourhood’; ● local area satisfaction was based on a question asking survey respondents: ‘How satisfied are you with living in your local council area (that is, city or shire council area)?’; ● regional satisfaction was based on how satisfied the resident was with ‘living in the SEQ region’; and ● regional QOL was based on the question: ‘In general, how would you rate the overall quality of life in the SEQ region?’ A subjective access latent variable was included in the model and comprised the following four manifest variables: ● ● ● ●

secondary school access satisfaction; supermarket access satisfaction; sporting facility access satisfaction; and hospital access satisfaction.

These manifest variables were based on a question asking respondents how satisfied they were with their access to each of these facilities on a five-point Likert scale. The subjective overcrowding latent variable was measured with four manifest variables: ● air pollution; ● noise pollution;

104  Handbook of quality of life research ● traffic congestion; and ● cost of housing. These manifest variables were based on the degree to which residents thought these things were a problem in the SEQ region, responding on a five-point Likert scale (from 1 = ‘not a problem’ to 5 = ‘a very great problem’). The latent variable objective access was measured with four manifest variables: ● secondary school distance; ● regional shopping centre distance (there were 12 in the region); ● sporting facility distance (including parks, swimming centres, bowling centres, golf courses, rifle ranges, soccer fields and tennis courts); and ● hospital distance. Each manifest variable was measured using MapInfo StreetPro as the straight-line distance between the respondent’s residence and the closest facility of that type. Lower scores indicate better access (that is, shorter distances to facilities). The objective density latent variable was measured with two manifest variables: (1) population density per square kilometre; and (2) dwelling density per square kilometre. The density measures for each resident were based on the area of the Census Collection District in which each survey respondent resided and the associated population and dwelling counts from the census. The cost of housing was a latent variable measured with two manifest variables relating to the cost of renting and the cost of purchasing dwellings in the resident’s local area. The model The full structural equation model is represented in Figure 7.3: ● the boxes represent manifest variables; ● the ovals represent latent variables; ● in the measurement model, the straight arrows from the latent variables to the manifest variables indicated factor loadings; ● in the structural model, the arrows from one latent variable to another reflected regression coefficients and a dashed line indicates that a non-significant path was hypothesised; ● the paths between the latent objective measures of the urban environment and subjective urban QOL were hypothesised to be mediated by the latent subjective measures of the urban environment, and so the direct or unmediated paths were hypothesised to be non-significant; ● the double-headed curved arrows in the structural model indicated relationships with no directionality hypothesised (that is, simple correlations); and ● where there were no paths (that is, arrows), no relationships were hypothesised. The three-stage analysis undertaken used the correlation matrix of manifest variables as the data source: 1. In Stage 1, a mediated path structural model was hypothesised where the relationships between objective latent variables and subjective QOL were mediated by subjective meas-

Integrating subjective and objective measures in quality of life research  105

Source: McCrea et al. (2006).

Figure 7.3

A structural equation model to test the hypotheses about the relationships between subjective and objective QOL attributes

ures of the urban environment, and the fit of the model was evaluated (that is, paths for H8, H9 and H10 were not included). 2. In Stage 2, direct paths between the objective latent variables of the urban environment and subjective QOL were added to see whether these paths were significant (that is, H8, H9 and H10 were added to test whether subjective access and subjective overloading fully mediated these paths). 3. In Stage 3, other paths that significantly improved the fit of the model were identified by examining the modification indices and the standardised residuals, and then added to the model. The measurement part of the structural equation model was satisfactory, with the manifest variables loading well onto their latent variables. Summary of findings The findings from the modelling undertaken by McCrea et al. (2006) may be summarised as follows: ● There was not a strong link between the objective and subjective latent measures of the urban environment: ● there was not a strong relationship between objective access and subjective access; and ● relationships were weak between objective density and subjective overcrowding, as well as between objective cost of housing and subjective overcrowding.

106  Handbook of quality of life research Those objective latent variables thus explained little variance in subjective access and subjective overcrowding (as also found in other studies), indicating that objective indicators are generally weak predictors of satisfaction in related life domains (Cummins, 2000; Evans and Huxley, 2002). This suggests that care is needed when making inferences from improvements in objective indicators of QOL to improvements in subjective QOL. ● As expected, the objective latent variables were inter-correlated, with objective access and objective density being highly correlated. No significant correlation was found between subjective access and subjective overcrowding. However, among the subjective variables, subjective access and subjective overcrowding both predicted subjective QOL, with subjective access being the stronger predictor. In terms of optimal centrality theory (Cicerchia, 1999), this suggests that subjective access is more important than subjective overcrowding in the trade-off between the two when maximising subjective QOL. This finding helps explain the trend toward increasing urbanisation in the study area. ● The relationship between objective access and subjective QOL was fully mediated by subjective access. However, the relationship from objective density to subjective QOL was only partially mediated by subjective overcrowding, suggesting that other factors may also play a role in mediating this relationship. ● Similarly, the relationship from objective cost of housing to subjective QOL was only partially mediated by subjective overcrowding, suggesting that other factors are involved in the mediation. However, the direct relationship between cost of housing and subjective QOL was positive, indicating that any other factors associated with cost of housing would have a positive impact on subjective QOL (for example, neighbourhood quality factors). The suggestion of other mediating factors highlights a limitation of the McCrea et al. (2006) study in that only a small number of objective and subjective latent variables were included in the model. When linking objective and subjective indicators of QOL, other factors might also need to be investigated, such as social interaction and crime. Another limitation was the bottom-up model tested does not account for psychological processes and the systemic nature of QOL reflected in residential relocation decisions. These limitations were addressed in doctoral research by McCrea (2007). In this work, the integrated framework was first proposed, which was a modification of the general framework first proposed by Campbell et al. (1976) and Marans and Rodgers (1975). Some implications Even though the relationships found by McCrea et al. (2006) between objective indicators of QOL and subjective QOL were weak, this is an insufficient justification for complacency in continuing to improve the objective urban environment for residents (Kahneman, 1999). It is important to understand why the links are weak because different explanations have different urban policy implications. A systemic integrated model is needed to understand the impact of subjective judgement processes involved in individual differences in what is important for QOL as well as to understand the role of the residential relocation process on weakening links between objective and subjective indicators.

Integrating subjective and objective measures in quality of life research  107

CONCLUSION In the integrated approach to investigate QOL/well-being, researchers have largely taken an empirical positivistic approach to examining the relationships between objective dimensions of urban environments and people’s subjective evaluations of the urban environment. As discussed by McCrea et al. (2011, pp. 98–9), although QOL can be measured both objectively and subjectively, QOL is ultimately more subjective and this is evident in the model framework first proposed by Campbell et al. (1976) and modified models used by Marans and Rodgers (1975) and later by McCrea et al. (2006). Common across the multiple integrated models used to investigate QOL is that they have generally sought to measure people’s subjective assessment of QOL as a whole and of QOL domains, along with subjective assessment of QOL at various levels of scale – typically, the dwelling, the neighbourhood and the wider urban region. The models have also explicitly sought to investigate the links and paths among and between attributes of the objective environmental context in which people live and to evaluate the degree to which variations in those subjective assessments are influenced by objective attributes. McCrea (2007, pp. 1–2) has suggested that a range of possible explanations might be used to account for the strength or weakness of those relationships, each with different implications for QOL and urban planning. For example, he proposed the following: (1) if moderate to strong direct relationships are found, then that would imply an environmentally deterministic model with changes in broad objective dimensions of the urban environment directly impacting on subjective QOL; (2) if weak relationships are found, the implications may depend on the explanation found for the weakness. For example, if the weakness is best explained by psychological adaptation whereby residents simply adjust psychologically to changes in the objective urban environment, then that would imply that changes in broad objective dimensions of the urban environment have relatively little impact on subjective QOL over time. But if the weakness is explained by adjustment via residential relocation whereby dissatisfied residents tended to move to other locations while satisfied residents tended to stay, then that would imply a significant impact on subjective QOL. McCrea (2007) thus proposed that ‘it is important not only to examine the strength of links between objective dimensions and subjective evaluations of the urban environment, but also to examine a range of explanations that may account for the strength of these links’ (p. 1). The research by McCrea and colleagues (2006) tended to reveal that in general the direct links between objective dimensions and subjective evaluations of QOL tended to be weak. However, objective and subjective indicators of QOL can influence each other indirectly via an integrated system involving subjective judgements, residential relocation processes and individual differences in what people consider important in choosing where to live. McCrea (2007) also suggested that integrated explanation of subjective QOL may be advanced by paying attention to the distinctiveness of subjective and objective QOL in different local areas and examining different subcultures and lifestyles. Many challenges remain in developing operational frameworks to analyse QOL through integrated approaches incorporating GIS-enabled modelling, involve more care in model conceptualisation, and include likely more complex research designs, which will have resourcing implications for undertaking QOL research.

108  Handbook of quality of life research

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Integrating subjective and objective measures in quality of life research  109 Glaeser, E., Kolko, J. and Saiz, A. (2000), ‘Consumer city’, NBER Working Paper No. 7790, National Bureau of Economic Research. Headey, B., Holmstrom, E. and Wearing, A. (1984), ‘The impact of life events and changes in domain satisfactions on well-being’, Social Indicators Research, 15, 203–27. Kahneman, D. (1999), ‘Objective happiness’, in D. Kahneman, E. Diener and N. Schwarz (eds), Well-Being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation, pp. 3–26. Kaplan, S. (1995), ‘The restorative benefits of nature: toward an integrated framework’, Journal of Environmental Psychology, 15, 169–82. Kawachi, I., Kennedy, B.P. and Wilkinson, R.G. (1999), ‘Crime: social disorganization and relative deprivation’, Social Science and Medicine, 48, 719–31. Kemp, D., Manicaros, M. and Mullins, P. et al. (1997), Urban Metabolism: A Framework for Evaluating the Viability, Livability and Sustainability of South East Queensland, Brisbane: The Australian Housing and Urban Research Institute. Kim, T.K., Horner, M.W. and Marans, R.W. (2005), ‘Life cycle and environmental factors in selecting residential and job locations’, Housing Studies, 20, 457–73. Lazarsfeld, P.F. and Merton, R.K. (1954), ‘Friendship as a social process: a substantive and methodological analysis’, in M. Berger (ed.), Freedom and Control in Modern Society, New York: Van Nostrand, pp. 18–66. Marans, R.W. (2003), ‘Understanding environmental quality through quality of life studies: the 2001 DAS and its use of subjective and objective indicators’, Landscape and Urban Planning, 65, 73–83. Marans, R.W. and Rodgers, W. (1975), ‘Toward an understanding of community satisfaction’, in A. Hawley and V. Rock (eds), Metropolitan America in Contemporary Perspective, New York: Halsted Press, pp. 299–352. McCrea, R. (2007), ‘Urban quality of life: linking objective dimensions and subjective evaluations of the urban environment’, unpublished PhD thesis, The University of Queensland. McCrea, R., Shyy, T.-K. and Stimson, R. (2006), ‘What is the strength of the link between objective and subjective indicators of urban quality of life?’, Applied Research in Quality of Life, 1, 79–96. McCrea, R., Shyy, T.-K. and Stimson, R. (2014), ‘Satisfied residents in different types of local areas: measuring what’s most important’, Social Indicators Research, 118, 87–101. McCrea, R., Stimson, R.J. and Marans, R.W. (2011), ‘The evolution of integrative approaches to the analysis of quality of urban life’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 77–106. McCrea, R., Stimson, R.J. and Western, J. (2005), ‘Testing a moderated model of satisfaction with urban living using data for Brisbane-South East Queensland, Australia’, Social Indicators Research, 72, 121–52. McMillan, D.W. and Chavis, D.M. (1986), ‘Sense of community: a definition and theory’, Journal of Community Psychology and Health, 14, 6–23 McPherson, M., Smith-Lovin, L. and Cook, J.M. (2001), ‘Birds of a feather: homophily in social networks’, Annual Review of Sociology, 27, 415–44. Michalos, A.C. and Zumbo, B.D. (1999), ‘Public services and the quality of life’, Social Indicators Research, 48, 125–56. Mitrany, M. (2005), ‘High density neighborhoods: who enjoys them?’, GeoJournal, 34, 131–40. Pacione, M. (2003), ‘Quality-of-life research in urban geography’, Urban Geography, 24, 314–39. Perz, S.G. (2000), ‘The quality of urban environments in the Brazilian Amazon’, Social Indicators Research, 49, 181–212. Prezza, M., Amici, M., Roberti, T. and Tedeschi, G. (2001), ‘Sense of community referred to the whole town: its relations with neighboring, loneliness, life satisfaction, and area of residence’, Journal of Community Psychology, 29, 29–52. Putnam, R. (1995), ‘Tuning in, tuning out: the strange disappearance of social capital in America’, PS: Political Science and Politics, 28, 664–83. Rogerson, R.J., Findlay, A.M., Morris, A.S. and Coombes, M.G. (1989), ‘Indicators of quality of life: some methodological issues’, Environment and Planning A, 21, 1655–66. Rogerson, R.J., Findlay, A.M., Paddison, R. and Morris, A.S. (1996), ‘Class, consumption and quality of life’, Progress in Planning, 45, 1–66.

110  Handbook of quality of life research Sampson, R.J. and Groves, W.B. (1989), ‘Community structure and crime: testing social-disorganization theory’, American Journal of Sociology, 94, 774–802. Sampson, R.J., Raudenbush, S.W. and Earls, F. (1997), ‘Neighborhoods and violent crime: a multilevel study of collective efficacy’, Science, 277, 918–24. Savage, M., Warde, A. and Ward, K. (2003), Urban Sociology, Capitalism and Modernity, 2nd edition, New York: Palgrave Macmillan. Schwanen, T. and Mokhtarian, P.L. (2004), ‘The extent and determinants of dissonance between actual and preferred residential neighborhood type’, Environment and Planning B – Planning and Design, 31, 759–84. Schwarz, N. and Strack, F. (1999), ‘Reports of subjective well-being: judgmental processes and their methodological implications’, in D. Kahneman and E. Diener (eds), Well-being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation, pp. 61–84. Schwirian, K.P., Nelson, A.L. and Schwirian, P.M. (1995), ‘Modeling urbanism – economic, social and environmental stress in cities’, Social Indicators Research, 35, 201–23. Senecal, G. and Hamel, P.J. (2001), ‘Compact city and quality of life: discussions of the Canadian approach to sustainability indicators’, Canadian Geographer/Géographe canadien, 45, 306–18. Shaw, C.R. and McKay, H.D. (1942), Juvenile Delinquency and Urban Areas, Chicago, IL: University of Chicago Press. Simmel, G. (1950), The Sociology of Georg Simmel, New York: Free Press. Sirgy, M.J. and Cornwell, T. (2001), ‘Further validation of the Sirgy et al.’s measure of community quality of life’, Social Indicators Research, 56, 125–43. Sirgy, M.J. and Cornwell, T. (2002), ‘How neighborhood features affect quality of life’, Social Indicators Research, 59, 79–114. Sirgy, M.J., Rahtz, D.R., Cicic, M. and Underwood, R. (2000), ‘A method for assessing residents’ satisfaction with community-based services: a quality-of-life perspective’, Social Indicators Research, 49, 279–316. Stover, M.E. and Leven, C.L. (1992), ‘Methodological issues in the determination of the quality-of-life in urban areas’, Urban Studies, 29, 737–54. Taylor, R.B. (1996), ‘Neighborhood responses to disorder and local attachments: the systemic model of attachment, social disorganization, and neighborhood use value’, Sociological Forum, 11, 41–74. Turksever, A.N.E. and Atalik, G. (2001), ‘Possibilities and limitations for the measurement of the quality of life in urban areas’, Social Indicators Research, 53, 163–87. Ulrich, R.S., Simons, R.F. and Losito, B.D. et al. (1991), ‘Stress recovery during exposure to natural and urban environments’, Journal of Environmental Psychology, 11, 201–30. Van den Berg, A.E., Hartig, T.M. and Staats, H. (2007), ‘Preference for nature in urbanized societies: stress, restoration, and the pursuit of sustainability’, Journal of Social Issues, 63, 79–96. Vogt, C.A. and Marans, R.W. (2004), ‘Natural resources and open space in the residential decision process: a study of recent movers to fringe counties in southeast Michigan’, Landscape and Urban Planning, 69, 255–69. Walmsley, D.J. (1988), Urban Living: The Individual in the City, New York: John Wiley & Sons. Western, J. and Larnach, A. (1998), ‘The social and spatial structure of South East Queensland’, Australasian Journal of Regional Studies, 4, 215–37. Wirth, L. (1938), ‘Urbanism as a way of life’, American Journal of Sociology, 44, 1–24.

8. Assessing alternative air quality measures and their impact on quality of life: the case of Hong Kong1 Poh-Chin Lai, Chien-Tat Low, Si Chen, Robert J. Stimson, Ester Cerin, Wei Cheng, Jimmy Fung and Paulina Pui-Yun Wong

INTRODUCTION As discussed in Chapters 2 and 7, research investigating quality of life (QOL) often involves looking at the relationships between the subjective assessments of QOL domains and impacts of the objective measurement of a range of factors including the impact of environmental phenomena on QOL. This chapter uses Hong Kong as a case study to investigate how deteriorating air quality is impacting liveability and the QOL of its inhabitants. A hybrid model uses subjective data from a survey of QOL and objective measures of air to examine associations between perceptual and measurement differences in air quality impacting QOL. The study is part of a wider research project undertaken by the authors as part of a comprehensive study of QOL in Hong Kong, including a sample survey of the population in which an aim was to investigate the relationship between QOL and socio-economic, demographic and environmental phenomena. The chapter first discusses the context for the Hong Kong study. It then proceeds to discuss the methodology, data and analytic tools used for the study. The findings and implications are then discussed.

THE CONTEXT In many cities, air quality is a significant factor impacting QOL (Low et al., 2018; Pan et al., 2018), and it can be a significant urban health hazard. Inhaling fine particulate matter (PM) aggravates respiratory and cardiovascular conditions, such as asthma (Atkinson et al., 2014; Guarnieri and Balmes, 2014; Laumbach and Kipen, 2012). Studies show pollution in Hong Kong can be two times that of London and three times higher than that in New York (Lai et al., 2011). There is also increasing evidence in Asia – and especially in China’s cities – that, even with improved air quality in recent years, residents have considered leaving a city due to poor air quality, while international corporations have considered moving their headquarters elsewhere (Hedley et al., 2008; Lam et al., 2019; Levine et al., 2019; Qin and Zhu, 2018; Zhang, Hao et al., 2018). Air quality is usually measured at dispersed monitoring locations. In Hong Kong, 13 of 16 are located away from the roadside (Environmental Protection Department [EPD], 2013). Studies have cautioned the underreporting of air arising from traffic-related pollution amidst 111

112  Handbook of quality of life research complex building morphologies and urban street canyons (Lee et al., 2017; Wong et al., 2019). This deficiency could lead to gross underestimation of the public health burden from chronic respiratory diseases associated with pollutant inhalation. A hybrid model combining observation-based and simulation-based approaches has proved successful in reconstructing ground-level PM2.5 concentrations for Hong Kong using satellite aerosol optical depth values from remote sensing corroborated by sparsely measured ground-level data (Lin et al., 2015, 2019). This hybrid model is deemed suitable for simulating PM2.5 as a continuous phenomenon at the national, regional and urban scales. Although air cannot be seen, degraded visibility due to haze or urban smog is indicative of a high concentration of suspended particulates (Yue et al., 2017). Because neighbourhood perceptions may be more closely related to actual behaviours (Maass et al., 2014; Marans, 2015), insight into the perceived neighbourhood physical environmental factors associated with QOL is important. The level of people’s subjective satisfaction with the local environment has been shown consistently to correlate with the subjective appreciation of their surroundings, measured in terms of perceived air and water quality and proximity to green spaces (Liao et al., 2015; Wang and Wang, 2016; Yuan et al., 2018). It is also clear that subjective indicators are not perfect, and perceived feelings may be affected by different factors according to the liveability and the comparison theories (Diener and Lucas, 2000; Veenhoven and Ehrhardt, 1995). The development and wide use of geographic information systems (GIS) has supported studies examining the associations of environmental indicators with human behaviours and health. Driven by the liveability and comparison theories and building upon results from an earlier study (Low et al., 2018), the research discussed here uses PM2.5 values to estimate the level of correspondence between perceived (subjective) and actual (objective) air quality measurements among neighbourhoods of Hong Kong. It also attempts to explore factors affecting the perception of local air quality.

METHODOLOGY Hong Kong is a 1106 km2 high-density city with 7.5 million residents in 2021. Public health studies related to air quality have relied mostly on exposure maps created from objective measures using remote sensing (Lu et al., 2017) or modelled results based on ground truth data (Guo et al., 2009). Despite small differences, geostatistical interpolation produces more reliable estimates in populated areas with extensive ground monitoring networks (Lee et al., 2012, p. 1727). Studies involving the assessment of disease burden increasingly use individual-based exposure assessment using sophisticated tracking measurement and modelling technologies (Kelly and Fussell, 2015; Park and Kwan, 2017). Thus far, perceptual responses in public health studies have focused mainly on studying the exposure effects on age-specific physical activities or wellness (for example, Cerin et al., 2013; Zhang, Barnett et al., 2018). Here we investigate if there are differences between objective and subjective assessment of air quality that may influence people’s QOL in Hong Kong.

Alternative air quality measures and their impact on quality of life: Hong Kong  113 Research Hypotheses Focusing on the comparison of subjective (or perceived) and objective measures of air quality, two research hypotheses are investigated. H1: Perceived air quality and PM2.5 are interrelated Studies indicate there is a general correspondence between actual air quality and social media postings about air pollution based on individual perceptions (Hswen et al., 2019). An empirical analysis of a vector autoregression model by Dong et al. (2019) using daily data of 2068 days (from 2 December 2013 to 31 July 2019) in Shanghai showed heightened citizen concern on days of decreasing or poor air quality. However, a cross-sectional study using data from the Swiss Study on Air Pollution and Lung Diseases in Adults (Oglesby et al., 2000) suggested that objective air pollution estimates (annual mean values) were more consistent than the individual level or self-reported subjective scores. Regression of population mean annoyance or perception scores against annual mean PM10 and nitrogen dioxide concentrations showed linear and strong correlations (r > 0.85). H2: Perceived air quality and social/personal status (including wealth and health) are interrelated A study investigating inequality in health effects in urban China has found a non-linear relationship between exposure to air pollution and socio-economic gradient (Jiao et al., 2018). Furthermore, a questionnaire survey of residents in the French city of Lyon showed the relationship between objective and subjective measures of air pollution to be most apparent among the oldest people (Deguen et al., 2017). The Lyon study also found that the neighbourhood deprivation index – comprising gender, education, unemployment and health problems – affects the level of satisfaction with air quality. In general, people with low and moderate socio-economic status were more likely to reside in areas with poor air quality, as reported in studies about environmental injustice in air pollution (Li et al., 2018; Xu et al., 2019). Air Quality and Other Data Figure 8.1 summarises air quality assessment methods based on objective measures derived from ground-level PM2.5 (field measurements and observation-based algorithm), as well as the subjective perceptions of air quality (obtained from questionnaire surveys). Ground-level PM2.5 data for Hong Kong are processed using: (1) geostatistical interpolation to yield estimated seasonal (summer and winter) PM2.5 surfaces (see Wong et al., 2019); and (2) observation-based algorithm to integrate observed and simulated data (Lin et al., 2015; Liu et al., 2018) to yield a continuous surface of annual mean PM2.5 concentration. Perceived air quality data involved a random sample of households across Hong Kong with demographic, social and economic characteristics (see Low et al., 2018). The QOL survey required respondents to rate, using a five-point Likert scale, the importance of a set of factors that may have influenced their decision to choose their current residential location. The subjective scores about air quality for Hong Kong range from 1 to 5 (1 = very bad; 2 = bad; 3 = average; 4 = good; and 5 = very good).

114  Handbook of quality of life research

Source: The authors.

Figure 8.1

Subjective and objective measures of air quality

Statistical Analysis A one-way analysis of variance (one-way ANOVA; DeCoster and Claypool, 2004) is used to examine differences in perceived air pollution among subgroups of participants differing in demographic, socio-economic and health-related characteristics. The analysis method comprises a two-stage statistical examination using ordinal logistic regression (Model 1 and Model 2), followed by cluster analysis (Model 3) (Figure 8.2). 1. A base model (Model 1) with subjective air quality as the dependent variable and objective PM2.5 measures as independent variables (including cool/winter seasonal mean warm/ summer seasonal mean and annual mean) aims to identify individual determinants of subjective measures of air quality using multiple correspondence analysis. 2. An extended version of the base model is used with potential confounders (Model 2; see also Table 8.1) adjusted for individual and contextual characteristics that might have influenced the relationships between the subjective and objective measures of air pollution. The potential confounders are identified using one-way ANOVA. Only statistically significant independent variables (p < 0.05) are selected for inclusion in the ordinal logistic regression model (Model 2). 3. K-means clustering (Model 3) is then applied to define groups based on the characteristics provided. It aims to establish if the association between subjective perception of air quality and the objective measures of PM2.5 is influenced in specific ways by various spatial, demographic, socio-economic and other characteristics. To avoid arbitrary class breaks, participants were assigned to mutually exclusive k-means groups based on the internal

Alternative air quality measures and their impact on quality of life: Hong Kong  115

Note: * Model 1 is the base model. Source: The authors.

Figure 8.2

Two-stage ordinal logistic regression modelling and cluster analysis

Table 8.1

Independent variables used in the study

Variable

Typea

Values

n

1 = Hong Kong Island; 2 = Kowloon; 3 = New Territories East; 4 = New Territories

Spatial Regions

West Demographic Age group

o

1 = 18–24; 2 = 25–44; 3 = 45–64; 4 = 65 or over

Gender

n

Female; male

Type of dwelling

n

Public rental; government subsidised sales; private; others

Flat size

o

1 = < 400; 2 = 400–599; 3 = 600 or above

Tenure of accommodation

n

Fully owned; being paid off; rented; provided or partially subsidised by employers

Household income

o

1 = < 20 000; 2 = 20 000–39 999; 3 = 40 000 or above

Money paid for housing

o

< 30%; 30–40%; > 40%

Education

n

No schooling and primary school; lower secondary; upper secondary or

Occupation

n

Socio-economic

matriculation; post-secondary diploma or bachelor’s degree; postgraduate Managers and administrators; professionals; associate professionals; clerical support professionals; service and sales; craft and related workers; plant and machine operators and assemblers; elementary occupation; other Others Exercise level

o

1 = never; 2 = occasionally; 3 = at least weekly; 4 = at least twice a week; 5 = daily

Voluntary work

o

1 = not any; 2 = 1–2 hrs per week; 3 = 3–4 hrs per week; 4 = 5–10 hrs per week; 5 =

Marital status

n

single; divorced/separated; married/partner; widowed

Anomieb score

o

1 = low; 2 = medium low; 3 = medium; 4 = medium high; 5 = high

Health status

o

1 = very poor; 2 = poor; 3 = fair; 4 = very good; 5 = excellent

over 10 hrs per week

Note: a Type of data measurement: n = nominal; o = ordinal. b Anomie is a term coined by sociologists to describe feelings of disintegration (such as isolation and loneliness) in a society. A series of questions were asked and responses were used to compute anomie as a ‘state of mind’ and an indicator of well-being (Western et al., 2007). The result computes to a score ranging between 1 (low) and 5 (high), where lower scores indicate greater optimism and high scores indicate pessimism and depression.

116  Handbook of quality of life research similarity of independent variables within each cluster. The associations between perceived and objective air quality are examined by ordinal logistic regression modelling with k-means clusters included as covariates to control for spatial and population characteristic effects.

RESULTS Descriptive Analysis The Hong Kong QOL survey conducted in 2015 comprised 1169 subjects aged 18 and above. It assessed factors influencing people’s perception of Hong Kong’s QOL that included air quality as an indicator. Figure 8.3 shows the spatial distribution of the respondents and their subjective ratings of air quality using a five-point Likert scale. The distribution of participants shows some clustering effect (Figure 8.3a), reflecting that residents in the New Territories are more willing to participate in surveys than those living in Kowloon and on Hong Kong Island. Over 80 per cent of respondents believed that the air quality was average and above average (Figure 8.3b).

Source: The authors.

Figure 8.3

Spatial distribution of respondents and their subjective perception of air quality of Hong Kong (based on 1169 subjects participating in the 2015 QOL survey)

Table 8.2 shows the difference in mean scores of the perception of air quality by demographic, socio-economic and health-related characteristics: ● region, health status, exercise level and anomie are the top four variables in differentiating air quality perception;

Alternative air quality measures and their impact on quality of life: Hong Kong  117 ● people living in New Territories West tend to give a higher score to air quality, while New Territories East scores lowest; ● in general, people with better health status and those who exercise daily give a higher score to air quality than their counterparts; ● conversely, participants with higher anomie scores, who are considered more depressed and pessimistic, tend to provide air quality a lower score; ● other variables – such as type of housing, flat size and monthly household income – are also significant predictors (p < 0.05) of perceived air quality, with people of higher social-economic status (that is, higher household income and living in larger flats) giving a higher score to air quality, although with a slight anomaly in the HK$20 000–39 999 income group; and ● people living in village houses and employer-provided quarters are more positive about the perceived air quality, followed by those living in government subsidised, public rental and private housing. Regression Analysis Figure 8.4 displays the spatial variation of air quality based on different measurement techniques: ● subjective perception based on surveyed participants; ● objectively derived mean PM2.5 concentration at a ground level based on the observation-based algorithm; ● objectively derived mean PM2.5 for the warm/summer season based on field measurement; and ● objectively derived mean PM2.5 for the cool/winter season based on field measurement. A series of regression models for ordinal dependent variables are used to see how well the subjective perception of air quality corresponded to various objective measures of PM2.5 without adjustment (Model 1) and with adjustment for residents/respondents characteristics (Model 2), as well as by distinct population groups identified using cluster analysis (Model 3). Ordinal logistic regression: Model 1 This analysis reveals statistically significant associations between perceived and objectively measured air quality except for winter PM2.5 (Table 8.3). The results suggest that a decrease in the summer PM2.5 is associated with an increase in the odds of giving a higher score of air quality (odds ratio, OR = 0.957, p < 0.001). A similar but less significant association suggests a decrease in the annual PM2.5 is linked with an increase in the odds of giving a higher score of air quality (OR = 0.959, p < 0.05). Ordinal logistic regression: Model 2 A second ordinal logistic regression model includes seven ordinal independent variables exhibiting statistically significant within-group variations in the mean score of the perception of air quality (refer back to Table 8.2). These independent variables are religion, type of housing, flat size, household income, exercise level, health status and anomie score. As an extended version of Model 1, Model 2 examines possible confounding of these independent

118  Handbook of quality of life research Table 8.2

Differences in the mean scores of perceived air quality between participants with different demographic, socio-economic and health-related characteristics Perception of Air Quality

Demographic and Socio-economic Groups

Mean score based on a five-point Likert scale

Region

Count

 

 

Hong Kong Island

3.29

183

Kowloon

3.34

306

New Territories East

3.13

313

New Territories West

3.40

357

Health status

 

 

Very poor

2.94

18

Not very good/poor

3.11

148

Fair

3.29

608

Very good

3.54

336

Excellent

3.73

50

Exercise level

 

 

Never

3.17

88

Occasionally

3.32

457

At least weekly

3.21

184

At least twice weekly

3.41

251

Daily

3.59

175

Anomie score (depressed)

 

 

Not at all

3.75

8

Low

3.59

170

Mild

3.40

561

Moderate

3.25

370

Severe

2.70

54

Type of housing

 

 

Public rental housing

3.34

351

Govt. subsidised sales flat

3.43

244

Private housing

3.28

492

Others (village houses and employer-provided quarters)

3.66

68

Flat size

 

 

˂ 400 ft2

3.27

350

400–599 ft2

3.34

398

≥ 600 ft2

3.44

399

Household income

 

 

˂ HK$20 000

3.37

328

HK$20 000–39 999

3.25

345

≥ HK$40 000

3.43

463

Age group

 

 

18–24 years

3.30

164

25–44 years

3.31

378

45–64 years

3.34

459

65 years and over

3.54

149

Gender

 

 

Female

3.39

511

Male

3.32

655

Alternative air quality measures and their impact on quality of life: Hong Kong  119 Perception of Air Quality Demographic and Socio-economic Groups

Mean score based on a five-point Likert scale

Marital status

Count

 

 

Single

3.31

390

Divorced/separated

3.33

69

Married/partner

3.36

655

Widowed

3.68

34

Tenure of accommodation

 

 

Provided/partially subsidised

3.15

13

Rented

3.28

406

Fully owned

3.38

446

Being paid off

3.43

271

Money paid for housing

 

 

˂ 30%

3.38

729

30–39%

3.35

194

≥ 40%

3.20

107

Education

 

 

No schooling; primary school

3.19

48

Lower secondary school

3.39

151

Upper secondary; matriculation

3.38

261

Post-secondary; diploma; bachelor’s degree

3.35

538

Postgraduate

3.37

151

Occupation

 

 

Manager/administrator

3.47

145

Professional

3.39

285

Associate professional

3.39

67

Clerical support professional

3.32

171

Service/sales worker

3.34

169

Craft and related worker

3.27

41

Plant/machine operator & assembler

3.15

34

Elementary occupation

3.35

57

Voluntary work

 

 

1–2 hrs

3.35

189

3–4 hrs

3.50

189

5–10 hrs

3.43

28

> 10 hrs

3.54

26

variables on the relationship between perceived and objectively measured air quality (see Table 8.4). The table shows that none of the objective measures of air quality has a statistically significant association with the perception of air quality in Model 2 after controlling for the effects of the seven potential confounders. In this model, all seven confounders are significantly related to perceived air quality. Model 2 suggests that: ● people with a low anomie score (that is, less depressed or optimistic) tend to rate air quality better than those with higher anomie scores; ● in particular, the group with the lowest anomie score (= 1) is about four times more likely (OR = 4.345, p < 0.05) than that of the highest anomie score (= 5) in giving a better rating on the perception of air quality;

120  Handbook of quality of life research

Source: The authors.

Figure 8.4

Air qualities in Hong Kong by subjective perception and objective measurements

● low exercise level (OR < 1, p < 0.05), poor health status (OR < 1, p < 0.05), smaller flat size (OR < 1, p < 0.05) and low household income (OR < 1, p < 0.05) are also associated with lower scores in the perception of air quality; and Table 8.3

Model 1: perception vs objective air quality measures (ref: air quality = ‘very bad’)

Explanatory Variables Objective air quality measures

OR (95% CI)

p-value

 

 

Annual PM2.5

0.959 (0.924, 0.997)

0.031

Summer PM2.5

0.957 (0.937, 0.977)

< 0.001

Winter PM2.5

1.001 (0.983, 1.018)

0.995

Note: OR = odds ratio; CI = confidence intervals.

Alternative air quality measures and their impact on quality of life: Hong Kong  121 Table 8.4

Model 2: Perception versus objective air quality measures with potential confounders (ref: air quality = ‘very bad’)

Explanatory Variables Objective air quality measures

OR (95% CI)

p-value

 

 

Annual PM2.5

0.988 (0.94, 1.037)

0.607

Summer PM2.5

0.988 (0.959, 1.018)

0.396

Winter PM2.5

0.998 (0.979, 1.018)

0.798

Region (ref: New Territories West)

 

 

0.91 (0.606, 1.367)

0.646

Kowloon

0.654 (0.466, 0.918)

0.014

New Territories East

1.193 (0.848, 1.679)

0.313

Hong Kong Island

Type of housing (ref: others)

 

 

Public rental housing

0.879 (0.515, 1.498)

0.633

Govt. subsidised sales flat

0.741 (0.439, 1.253)

0.263

Private housing

0.485 (0.297, 0.794)

0.004

Flat size (ref: > 600 ft2 )

 

 

˂ 400 ft2

0.688 (0.486, 0.974)

0.035

400–599 ft2

0.860 (0.650, 1.137)

0.289

Household income (ref: ≥ HK$40 000)

 

 

˂ HK$20 000

0.839 (0.611, 1.153)

0.279

HK$20 000–39 999

0.751 (0.567, 0.995)

0.045

Exercise level (ref: daily)

 

 

Never

0.567 (0.346, 0.929)

0.024

Occasionally

0.652 (0.460, 0.923)

0.016

At least weekly

0.529 (0.351, 0.797)

0.002

At least twice weekly

0.665 (0.455, 0.973)

0.035

Health status (ref: excellent)

 

 

Very poor

0.408 (0.143, 1.167)

0.094

Not very good/poor

0.375 (0.199, 0.709)

0.003

Fair

0.484 (0.272, 0.860)

0.013

Very good

0.724 (0.403, 1.301)

0.280

Anomie score (depressed) (ref: 5 = severe)

 

 

4.345 (1.028, 18.394)

0.046

2 = Low

4.060 (2.246, 7.338)

< 0.001

3 = Mild

2.910 (1.708, 4.958)

< 0.001

4 = Moderate

2.467 (1.441, 4.225)

0.001

1 = Not at all

Note: Ref = reference class; OR = odds ratio; CI = confidence intervals. Only significant confounders from Table 8.2 were included in this model.

● Kowloon residents tend to rank their air quality lower than New Territories West residents (OR = 0.654, p < 0.05), while private housing residents rank air quality lower than those of other housing types (OR = 0.485, p < 0.01). K-means regression: Model 3 Figure 8.5 shows boxplots of three clusters of survey participants derived from the k-means clustering method. The three clusters are differentiated in terms of the seven independent variables used in Model 2. Clusters based on exercise level (Figure 8.5b) appear the most distinguishable from each other, followed by flat size (Figure 8.5e), household income (Figure 8.5g), region (Figure 8.5a), type of housing (Figure 8.5c; except for public housing group in

122  Handbook of quality of life research cluster 3). Health status (Figure 8.5f) and anomie (Figure 8.5d) do not exhibit uniquely different scores among the three clusters.

Source: The authors.

Figure 8.5

K-means clustering differentiate participants into three clusters

Alternative air quality measures and their impact on quality of life: Hong Kong  123 Table 8.5

Model 3: objective air quality measures vs k-means clusters (ref: air quality = ‘very bad’)

Explanatory Variables Objective air quality measures

OR (95% CI)

p-value

 

 

Annual PM2.5

0.963 (0.926 1.002)

0.057

Summer PM2.5

0.956 (0.936 0.976)

< 0.001

Winter PM2.5

0.999 (0.981 1.017)

0.829

K-means cluster (ref: MELI = Cluster 3)

 

 

HEMI = Cluster 1

1.533 (1.166 2.016)

0.002

LEHI = Cluster 2

1.115 (0.855 1.453)

0.423

Note: Ref = reference category; OR = odds ratio; CI = confidence intervals.

It is thus possible to describe the three clusters based on most distinctive qualities from three of the seven variables as follows: ● Cluster 1 (HEMI) = High Exercise level and Medium household Income or medium flat size; ● Cluster 2 (LEHI) = Low Exercise level and High household Income or large flat size; and ● Cluster 3 (MELI) = Moderate Exercise level and Low household Income or small flat size. The k-means clusters are then entered as covariates in an ordinal logistic regression model (Model 3) to adjust for potential confounding effects of demographic, socio-economic and health-related characteristics on the relationship between perceived and objective measures of air quality, as shown by Model 3 in Table 8.5. Model 3 suggests that a one-unit increase in the Summer PM2.5 decreases the odds of getting a better perception rating of air quality by 4.4 per cent. The odds of Cluster 1 (HEMI) giving a higher score for the perceived air quality is 1.533 times or 53 per cent more than that of Cluster 3 (MELI) (p < 0.01).

DISCUSSION Overview In the global assessment of air and water quality of 153 countries in 2006–10 (Pugliese and Ray, 2012), Hong Kong ranked first (that is, worst), with 69 per cent of its population dissatisfied with its air quality compared with only 21 per cent with its water quality. Traffic, shipping and electricity generation contribute to poor air quality in Hong Kong, which is further aggravated by emissions from heavy industries in the nearby Pearl River Delta region. However, the study reported here reveals that over 80 per cent of respondents are satisfied with the air quality, likely because the sample comprises mainly of local-born residents (about 77 per cent) and that the air quality in Hong Kong has been improving in recent decades (Lee et al., 2017). It is also noted that people’s subjective judgement of air quality varies from place to place. For example, people in countries with a high concentration of PMs, such as Poland and Turkey, do not seem much more dissatisfied with air quality than those in countries with significantly lower concentrations (Organisation for Economic Co-operation and Development [OECD], 2011).

124  Handbook of quality of life research There seems to be good agreement between subjective perceptions of air quality and ground-observed summer PM2.5 averages (p < 0.001) and annual mean PM2.5 values (p < 0.05). The results of ordinal logistic regression (Model 1) suggest that a decrease in the summer PM2.5 and annual PM2.5 is associated with an increase in the positive perception of the air quality, thus confirming the first hypothesis. However, the effect of poor winter PM2.5 (see Figure 8.4d) on perceptions of air quality is not significant possibly because acclimatisation has taken place. In addition, engaging in outdoor activities less often in the wintertime than in the summertime would make people less aware of the air quality level in different periods of the year to enable accurate evaluation. Results of the extended ordinal logistic regression (Model 2) do not show any association between summer PM2.5 or annual PM2.5 with the subjective perception of air quality, having accounted for possible participant-level confounders, but the former only become significant in Model 3 after grouping participants into three clusters as shown in Table 8.4 and Figure 8.5. The finding suggests that the group of respondents who exercise frequently are more conscious of air quality changes from time to time and pay more attention to the improved air quality of Hong Kong in recent decades, thus supporting the second hypothesis. Limitations There is a certain amount of truth in the comparative assessments, but there are inherent limitations and complex assumptions beneath all these findings. For example: ● objective measured pollutant readings underwent averaging and modelling procedures to yield representative surfaces of air quality; ● subjective perceptions of air pollution represent a snapshot of opinions of the surveyed population; ● attaching subjective perceptions to certain residential locations without taking into account the participants’ activity space may have inferential errors due to the uncertain geographic context problem (Kwan, 2012); ● the inadequacy of the well-being constructs and health status are constrained by the sample size; and ● results of analyses of individual exposure can differ according to area-based characteristics, including, but not limited to, size, land use, traffic intensity and network density. A new generation of inexpensive and portable air quality sensors can offer better data collection options to manage the problem of spatial uncertainty and enhance the precision of individual exposure (Karagulian et al., 2019).

CONCLUSION This chapter provides a case study demonstrating how statistical modelling tools may be used to investigate relationships between objective measures of environmental phenomena and people’s subjective perceptions of QOL. In the Hong Kong case study, the focus has been on the relationship between air quality and QOL. The health impact arising from pollutant exposure has become a major issue in many Asian cities experiencing rapid economic growth but with less stringent environmental policies.

Alternative air quality measures and their impact on quality of life: Hong Kong  125 Air quality degradation in overcrowded cities with complex infrastructures can bring serious health problems. Health impacts are assessed at regional, communal or individual scales, but different methodologies used to measure pollutant exposure could lead to inconsistent and even conflicting results between studies. Considering PM2.5 as a measure of air quality and an important component in assessing urban liveability and QOL, the findings of the analyses reported here have demonstrated some associations between objective and subjective air pollution measures. Given that subjective perceptions of air quality can be influenced by place-based and individual context, it is clearly necessary to integrate both subjective and objective indicators in characterising the QOL of people in large-scale urban settings. The global air quality has improved a great deal during the COVID-19 pandemic with more people working from home and less traffic (Zhang et al., 2020). However, recurring lockdowns and movement restraining orders have seriously impacted the QOL of people in cities worldwide. The situations between air quality and QOL concern not only physical health but also the psychological well-being of the population and people’s relation to their living environments and the social settings. Future research should better define QOL and clarify people’s needs and wants in improving QOL.

NOTE 1. This research was supported by funding from the Hong Kong Research Grants Council (Project 746412) and the Graduate School of the University of Hong Kong. The authors gratefully acknowledge data support from the Public Opinion Programme of the University of Hong Kong and Professor Benjamin Barratt of King’s College London. Corresponding author: Paulina Pui-Yun Wong.

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126  Handbook of quality of life research Guo, H., Jiang, F. and Cheng, H.R. et al. (2009), ‘Concurrent observations of air pollutants at two sites in the Pearl River Delta and the implication of regional transport’, Atmospheric Chemistry and Physics, 9, 7343–60. Hedley, A.J., McGhee, S.M. and Barron, B. et al. (2008), ‘Air pollution: costs and paths to a solution in Hong Kong – understanding the connections among visibility, air pollution, and health costs in pursuit of accountability, environmental justice, and health protection’, Journal of Toxicology and Environmental Health, Part A, 71, 544–54. Hswen, Y., Qin, Q., Brownstein, J.S. and Hawkins, J.B. (2019), ‘Feasibility of using social media to monitor outdoor air pollution in London, England’, Preventive Medicine, 121, 86–93. Jiao, K., Xu, M. and Liu, M. (2018), ‘Health status and air pollution related socio-economic concerns in urban China’, International Journal for Equity in Health, 17, Article 18. Karagulian, F., Gerboles, M. and Barbiere, M. et al. (2019), Review of Sensors for Air Quality Monitoring, Luxembourg: Publications Office of the European Union. Kelly, F.J. and Fussell, J.C. (2015), ‘Air pollution and public health: emerging hazards and improved understanding of risk’, Environmental Geochemistry and Health, 37, 631–49. Kwan, M.P. (2012), ‘The uncertain geographic context problem’, Annals of the Association of American Geographers, 102, 958–68. Lai, H.K., Wong, C.M., McGhee, S. and Hedley, A. (2011), ‘Assessment of the health impacts and economic burden arising from proposed new air quality objectives in a high pollution environment’, The Open Epidemiology Journal, 4, 106–22. Lam, J.C., Cheung, L.Y., Wang, S. and Li, V.O. (2019), ‘Stakeholder concerns of air pollution in Hong Kong and policy implications: a big-data computational text analysis approach’, Environmental Science and Policy, 101, 374–82. Laumbach, R.J. and Kipen, H.M. (2012), ‘Respiratory health effects of air pollution: update on biomass smoke and traffic pollution’, Journal of Allergy and Clinical Immunology, 129, 3–11. Lee, M., Brauer, M. and Wong, P. et al. (2017), ‘Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong’, Science of the Total Environment, 592, 306–15. Lee, S.J., Serre, M.L. and Van Donkelaar, A. et al. (2012), ‘Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States’, Environmental Health Perspectives, 120, 1727–32. Levine, R., Lin, C. and Wang, Z. (2019), ‘Pollution and human capital migration: evidence from corporate executives’, NBER Working Paper No. 24389, National Bureau of Economic Research. Li, V.O., Han, Y. and Lam, J.C. et al. (2018), ‘Air pollution and environmental injustice: are the socially deprived exposed to more PM2.5 pollution in Hong Kong?’, Environmental Science and Policy, 80, 53–61. Liao, P.S., Shaw, D. and Lin, Y.M. (2015), ‘Environmental quality and life satisfaction: subjective versus objective measures of air quality’, Social Indicators Research, 124, 599–616. Lin, C., Lau, A.K.H. and Fung, J.C.H. et al. (2019), ‘Assessing the effect of the long-term variations in aerosol characteristics on satellite remote sensing of PM2.5 using an observation-based model’, Environmental Science and Technology, 53, 2990–3000. Lin, C., Li, Y. and Yuan, Z. et al. (2015), ‘Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5’, Remote Sensing of Environment, 156, 117–28. Liu, T., Lau, A.K.H., Sandbrink, K. and Fung, J.C.H. (2018), ‘Time series forecasting of air quality based on regional numerical modeling in Hong Kong’, Journal of Geophysical Research: Atmospheres, 123, 4175–96. Low, C.T., Stimson, R. and Chen, S. et al. (2018), ‘Personal and neighbourhood indicators of quality of urban life: a case study of Hong Kong’, Social Indicators Research, 136, 751–73. Lu, X., Lin, C. and Li, Y. et al. (2017), ‘Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation’, Environment International, 98, 160–70. Maass, R., Lindstrøm, B. and Lillefjell, M. (2014), ‘Exploring the relationship between perceptions of neighbourhood resources, sense of coherence and health for different groups in a Norwegian neighbourhood’, Journal of Public Health Research, 3, Article 208. Marans, R.W. (2015), ‘Quality of urban life and environmental sustainability studies: future linkage opportunities’, Habitat International, 45, 47–52.

Alternative air quality measures and their impact on quality of life: Hong Kong  127 Oglesby, L., Künzli, N. and Monn, C. et al. (2000), ‘Validity of annoyance scores for estimation of long-term air pollution exposure in epidemiologic studies: the Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA)’, American Journal of Epidemiology, 152, 75–83. Organisation for Economic Co-operation and Development (OECD) (2011), How’s Life?: Measuring Well-being, Paris: OECD Publishing. Pan, X.I., Chahal, J.K. and Ward, R.M. (2018), ‘Quality of urban life among older adults in the world major metropolises: a cross-cultural comparative study’, Ageing and Society, 38, 108–28. Park, Y.M. and Kwan, M.P. (2017), ‘Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored’, Health & Place, 43, 85–94. Pugliese, A. and Ray, J. (2012, 14 May), ‘Air quality rated better than water quality worldwide’, Gallup, https://​news​.gallup​.com/​poll/​154646/​air​-quality​-rated​-better​-water​-quality​-worldwide​.aspx (accessed 19 July 2023). Qin, Y. and Zhu, H. (2018), ‘Run away? Air pollution and emigration interests in China’, Journal of Population Economics, 31, 235–66. Veenhoven, R. and Ehrhardt, J. (1995), ‘The cross-national pattern of happiness: test of predictions implied in three theories of happiness’, Social Indicators Research, 34, 33–68. Wang, F. and Wang, D. (2016), ‘Place, geographical context and subjective well-being: state of art and future directions’, in D. Wang and S. He (eds), Mobility, Sociability and Well-being of Urban Living, Cham: Springer, pp. 189–230. Western, J., McCrea, R. and Stimson, R.J. (2007), ‘Quality of life and social inclusion’, International Review of Sociology, 17, 523–37. Wong, P.P., Lai, P.C. and Allen, R. et al. (2019), ‘Vertical monitoring of traffic-related air pollution (TRAP) in urban street canyons of Hong Kong’, Science of the Total Environment, 670, 696–703. Xu, Y., Jiang, S. and Li, R. et al. (2019), ‘Unraveling environmental justice in ambient PM2.5 exposure in Beijing: a big data approach’, Computers, Environment and Urban Systems, 75, 12–21. Yuan, L., Shin, K. and Managi, S. (2018), ‘Subjective well-being and environmental quality: the impact of air pollution and green coverage in China’, Ecological Economics, 153, 124–38. Yue, R.P., Lee, H.F. and Hart, M.A. (2017), ‘Perceptions of visibility degradation in Hong Kong’, Journal of Environmental Planning and Management, 60, 1073–91. Zhang, C.J., Barnett, A. and Sit, C.H. et al. (2018), ‘Cross-sectional associations of objectively assessed neighbourhood attributes with depressive symptoms in older adults of an ultra-dense urban environment: the Hong Kong ALECS study’, BMJ Open, 8, Article e020480. Zhang, H., Lin, Y. and Wei, S. et al. (2020), ‘Global association between satellite-derived nitrogen dioxide and lockdown policies under the COVID-19 pandemic’, Science of the Total Environment, 761, Article 144148. Zhang, Z., Hao, Y. and Lu, Z.N. (2018), ‘Does environmental pollution affect labor supply? An empirical analysis based on 112 cities in China’, Journal of Cleaner Production, 190, 378–87.

9. How neighbourhood social and built environments influence social interactions: differences between life stages1 Piret Veeroja, Greg Foliente, Rod McCrea, Hannah Badland, Chris Pettit and Jennifer Day

INTRODUCTION This chapter provides an overview of research examining how neighbourhood social and built environments – especially third places – are associated with well-being and quality of life (QOL). The results of a study conducted in Melbourne, Australia, in 2015 are discussed. That study sought to quantify those relationships, comparing frequency of and the perceived satisfaction of residents with their social interactions with comparisons across four different age groups. The implications for planning and future research are discussed.

OVERVIEW OF RESEARCH Social Interactions, Neighbourhood Social and Built Environment Studies investigating aspects of QOL have shown that social interactions have been positively associated with psychological well-being, mental health (Kawachi and Berkman, 2001), happiness (Helliwell and Putnam, 2004), life satisfaction (ibid.), overall health (Sander et al., 2017) and longevity (Shor and Roelfs, 2015). Social interactions contribute to development of various psychological states, such as increased sense of purpose, sense of belonging and sense of self-worth (Kawachi and Berkman, 2001). Those psychological states motivate people and encourage them to take better care of themselves, which leads to improved mental health (ibid.). Less-frequent social interactions, however, have been found to lead to lower self-esteem, reduced sense of belonging and meaningful existence (Sander et al., 2017). This was especially noted during 2020–21 with the imposition of physical distancing and lockdowns on human activities across the world to manage the COVID-19 pandemic (Moser et al., 2020). Thus far, the published research investigating this topic has found that both (1) the neighbourhood built environment, such as density (Delmelle et al., 2013; Weijs-Perrée et al., 2015), accessibility, distance (Delmelle et al., 2013; Sharmeen et al., 2014) and walkability (Leyden, 2003), and (2) the social environment, including social capital (Windsor et al., 2012), belonging to community, sense of community (Van den Berg et al., 2017) and place attachment (Dempsey, 2008), contribute to neighbourhood social interactions. However, most of the research has focused on the relationship between understanding either neighbourhood social interactions and the social environment (Hickman, 2013; Weijs-Perrée 128

How neighbourhood social and built environments influence social interactions  129 et al., 2015) or social interactions and the built environment (Brueckner and Largey, 2008; Sharmeen et al., 2014; Wang and Lin, 2013). The study discussed in this chapter explicitly examines social interaction in relation to both social and built environments. As such, the chapter draws together these components to examine the broad range of measures (both perceived and objective, social and built environments and socio-demographic attributes) that could contribute to neighbourhood social interactions. Regarding the built environment, the study reported here focuses on third places. Oldenburg (1989) divided places in neighbourhoods into three general types: ● first (home); ● second (work); and ● third places. He later defined third places as being ‘informal public gathering places’ (Oldenburg, 1997, p. 6), which are also welcoming and comfortable. Third places provide opportunities for ‘pure sociability’, defined by Simmel and Hughes (1949) as situations where social interactions occur between people who do not know each other at all or very well. As such, third places can be thought of as a blend of built and social environments facilitating social interaction. Examples of third places are parks, squares, shops and cafés. Neighbourhood Social Interactions Social interactions vary from interactions in the most intimate of relationships (for example, an intimate partner, close family members) to ‘weak ties’ (for example, neighbours, colleagues) (Kawachi and Berkman, 2001) and strangers. Spontaneous meetings with neighbours, brief conversations and even waving to greet others engenders trust and connection among people. In this chapter, we focus on social interactions that take place within neighbourhoods, and particularly those with weak ties (for example, neighbours and strangers). In an urban planning context, quantitative studies have commonly focused on: ● the frequency of neighbourhood social interactions (Brueckner and Largey, 2008; Easthope and McNamara, 2015; Sharmeen et al., 2014; Van den Berg et al., 2016); ● the number of social contacts (Wang and Lin, 2013); and ● social network size (Kalmijn, 2012). Some qualitative studies have investigated social interactions more generally (Hickman, 2013). Some – including those by Gibson et al. (2010), Pinquart and Sorensen (2001) and Van den Berg et al. (2016) – have suggested that the quality of social interactions may be more important than their frequency in terms of their impact on people’s QOL. However, satisfaction with neighbourhood social interactions has been investigated in only a limited number of studies in the urban planning context (Bonsang and Van Soest, 2012; Delmelle et al., 2013; Weijs-Perrée et al., 2015). Socio-emotional selectivity theory claims that people’s social networks change as they age (Lansford et al., 1998). Younger adults have been found to be more future-oriented, aiming to gain information, experiences and new social contacts when developing relationships, while older adults tend to prefer smaller but emotionally more meaningful interactions (Wright and Patterson, 2006). Empirical evidence confirms that the size of social networks decreases

130  Handbook of quality of life research as people age (Kalmijn, 2012; Lansford et al., 1998). In addition to age, life events such as parenthood (Bost et al., 2002), marriage, divorce and widowhood (Kalmijn, 2012) have been found to influence the types of people with whom a person interacts.

THE MELBOURNE STUDY We now turn to discuss the 2015 Melbourne, Australia, Community Functioning and Well-Being study undertaken by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), which investigated how relationships between characteristics of the neighbourhood built environment and social environment affect the nature of perceptions of satisfaction and the frequency of neighbourhood social interactions. How neighbourhood social interactions differ across life stages is explicitly studied. Methodology The sample The study was based on a survey conducted in six Local Government Areas (LGAs) in metropolitan Melbourne (Figure 9.1).

Source: The authors.

Figure 9.1

The Melbourne study areas

How neighbourhood social and built environments influence social interactions  131 Survey participants were selected randomly by a third-party survey company, and the survey was carried out using a computer-assisted telephone interview (CATI) or an online survey. To be eligible, respondents needed to be 18 years or older and be able to speak English. The survey took approximately 30 minutes to complete. Three quotas of age (18–34 years/35–54 years/55+ years), sex (male/female) and employment status (working/not working) were imposed. Perceived or subjective measures Thirteen measures were selected that reflected perceived social and built environments and the perceived importance of eight types of third places for social interactions (Table 9.1). Each were measured using a five-point Likert scale. Table 9.1

Measures of perceived social and built environment, and third places

Measures

Statements

Perceived social environment measuresa

 

Belonging to suburb

(a) I feel that I belong to this suburb; (b) I am pleased to come back to the area, if I go away; (c) Overall, I feel very attached to this suburb

Safety

(a) It is safe to walk alone in the street during the day; (b) It is safe to walk alone outside at night; (c) It is safe to leave the car on the side of the road at night

Community spirit

(a) People can rely upon one another for help; (b) People have friendly relationships; (c) People can work together if there is a serious problem; (d) Overall, I am satisfied with community spirit in my suburb

Community inclusion

(a) Suburb is welcoming of newcomers; (b) Suburb is welcoming of people of different cultures; (c) Overall, suburb includes everyone no matter who they are

Levels of trust

(a) There are local community leaders I can trust; (b) People that you see around [insert suburb] can generally be trusted; (c) Local Council can be trusted; (d) Overall, I am satisfied with levels of trust in my suburb; (e) State Government can be trusted; (f) Private development companies can be trusted

Participation in community groups

(a) You regularly help out a local group as a volunteer; (b) You have attended several community events in your suburb in the past year; (c) You are a very active member of a local organisation or club; (d) Overall, you participate regularly in a variety of community activities in your suburb

Community well-being

(a) This suburb is suitable for young children; (b) This suburb is suitable for teenagers; (c) This suburb is suitable for seniors; (d) Overall, this suburb offers a good quality of life; (e) Overall, I am happy living in this suburb

Perceived built environment measures

 

General appearanceb

(a) Cleanliness; (b) Level of graffiti; (c) Greenery and parks; (d) Walkways and paths; (e) Neighbourhood character; (f) Overall satisfaction with general appearance

General environmentb

(a) Amount of traffic; (b) Quality of the air; (c) Level of noise; (d) Overall quality of the general environment

Densitya

My suburb is becoming denser

Land-use mixa

My suburb is becoming more varied, mixed and interesting

Accessibilitya

My suburb is becoming harder to get around

Feelings towards urban growtha

(a) Overall, urban growth is changing my suburb; (b) Urban growth represents problems for my suburb; (c) Urban growth represents opportunities for my suburb

132  Handbook of quality of life research Measures

Statements

Perceived importance of third placesc

 

Cafés, bars and restaurants

How important are these places to you for interacting socially in and around [insert

Shops

suburb]? 

Services (such as doctor) Public transport stops and hubs Natural environments Footpaths Local streets and squares Community places

Note: a 1 strongly disagree to 5 strongly agree. b 1 very dissatisfied to 5 very satisfied. c 1 not at all important to 5 very important.

Survey items were combined into composite measures through calculating their means. Social interaction satisfaction, density, mix and access, and the importance of third places were treated as single item measures. Objective built environment measures The objective built environment and third places measures of survey participants’ neighbourhoods were obtained using geographic information system (GIS) analysis (ArcGIS 10.3.1 software). The neighbourhoods were street network distance buffers of 1000 metres (m) from the respondents’ homes. That buffer represents a large enough area that is often associated with walkability and translates into a 15–20 minute walk (Ellis et al., 2016; Hogendorf et al., 2020; Maas et al., 2008). The objective built environment was measured with 14 density, land-use mix and accessibility measures (Table 9.2). Table 9.2

Objective built environment measures

Measure

Formula

Data Custodian

Objective density measures Population density

No .  of persons resident in NH NH area ​(​ha​)​

_____________________ ​         ​​

Dwelling density

Lot coverage

Statistics (2017)

The State of Victoria ∑ ​Building footprints area in NH ​(​ha​)​ ___________________________     ​        ​ ∑ Parcels area in NH ​(​ha​)​

in neighbourhoods

Statistics (2017) Australian Bureau of

No .  of dwellings in NH _________________ ​          ​ NH area ​(​ha​)​

Number of third places

Australian Bureau of

No .  of one type of third place  NH area ​(​ha​)​

 ​_____________________ ​         ​​ ∑ No .  of all types of third places _______________________          ​ ​  NH area ​(​ha​)​

(2016b)

 

How neighbourhood social and built environments influence social interactions  133 Measure

Formula

Data Custodian

Objective land-use mix measures Percentage of third

 

places

Land-use mix

∑ type of third places in NH  * 100 No .  of all types of third places in NH 

___________________________ ​     ​    

‘Calculate land-use mix’ tool in AURIN platform

Australian Urban Research Infrastructure Network (AURIN) (2010–15)

Objective accessibility measures Block size

The State of Victoria ∑ Block sizes in NH _______________ ​    ​​    No .  of blocks in NH

Number of 3+

(2016b)

The State of Victoria

intersections

No .  of 3+ way intersections in NH _________________________ ​           ​ NH area ​(​ha​)​

Cycle path length (two

∑ ​Bike path length in NH ​(​m)​ ​

 ​_____________________ ​         ​​

measures)

∑ ​Road length in NH ​(​m)​ ​

(2016e)

The State of Victoria (2016a)

∑ ​Bike path length in NH ​(​m)​ ​ _____________________        ​​ ​  NH area ​(​ha​)​

Footpath length (two

∑ ​Footpath length in NH​(​m)​ ​

____________________  ​   ​      ​​ ∑ Road length in NH​(​m)​ ​

measures)

∑ ​Footpath length in NH ​(​m)​ ​ _____________________        ​​ ​  NH area ​(​ha​)​

Closest public transport

Street network distance to closest public transport stop

stop (m)

Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2015)

The State of Victoria (2016c)

Third places Cafés, bars restaurants

Cafés, restaurants, taverns, pub, bars, takeaway food services

Australian Business

Shops

Book and magazine, clothing and footwear, department stores, electronics,

Australian Business

fish and seafood, flowers, fresh poultry, fruit and vegetable, furniture,

Register (2019)

Register (2019)

grocery, jewellery and watches, meat, newsagents, sport and camping, stationary, supermarkets, tobacco and toy shops Services

Automotive, banking, chiropractic, dermatologist, general practitioner,

Australian Business

hairdressing and beauty, hospitals, laundry and dry-cleaning, other medical

Register (2019)

services, pathologist, pharmaceutical, postal services, real estate agencies, travel agent Education Public transport stops

Primary school, secondary school, tertiary education, university, special

The State of Victoria

school, further education

(2016d)

Bus stops, tram stops, train stops

The State of Victoria (2016c)

134  Handbook of quality of life research Measure

Formula

Data Custodian

Natural environment

Parks, reserves, gardens

The State of Victoria

Community places

Health and medical facilities, sport and swimming, art and culture,

The State of Victoria

religious places, community and neighbourhood centres, libraries, aged

(2016d)

(2016e)

care facilities, childcare

Note: NH = neighbourhood.

Covariates A range of variables identified from the literature that typically influence social interactions were adjusted in the analysis, including: ● sex (male/female); ● household structure (couple with no children; couple with children; one-parent family; single person; household; group household); ● dwelling type (separate house; semi-detached dwelling; flat, unit or apartment); ● household income (less than $40 000; $40 000–$80 000; $80 000–120 000; $120 000–160 000; more than $160 000); ● employment (working full time; working part time; looking for paid work; studying full time; caring or home duties full time; retired); ● education (less than Year 12; completed Year 12; certificate, diploma or trade qualification; bachelor’s degree or higher); ● days in and around suburb; ● years in suburb; ● satisfaction with health; ● satisfaction with mobility; and ● satisfaction with standard of living. Dependent variables Two outcome measures were adopted: ● Social interaction frequency was measured by combining five survey items into one mean composite measure using a five-point Likert-type scale (1 = strongly disagree and 5 = strongly agree). Included items were: ● I stop and talk to my neighbours in my suburb; ● I stop and talk to strangers in my suburb; ● I say hello to people in my suburb; ● I go out together socially with others in my suburb; and ● I enjoy simply seeing others when out in my suburb. ● Social interaction satisfaction was a single item, measured on the same five-point scale: ● Overall, I am satisfied with the amount of my social interaction in and around my suburb. Analytic strategy The importance of the objective and perceived measures in predicting social interaction frequency and social interaction satisfaction were tested using mediation analysis. It was hypoth-

How neighbourhood social and built environments influence social interactions  135 esised that the objective built environment would be an indirect rather than a direct predictor of perceived social interactions. Put simply, the objective built environment may influence people’s perception of the built environment, which, in turn, affect social interaction frequency and social interaction satisfaction. The perceived built environment was therefore added as the mediator of the objective built environment at the modelling phase. Mediation analysis used the steps described in Baron and Kenny (1986) to test for the meditated path. Multiple regression analysis was used to test the mediation in two models: (1) social interaction frequency was the dependent variable (Model 1); and (2) social interaction satisfaction was the dependent variable (Model 2). Social interaction frequency and satisfaction were both treated as continuous variables. Linearity was tested and confirmed prior to analysis. Analysis was carried out using StataCorp’s (2015) Stata 14 software. Results Socio-demographic characteristics of the sample The sample (n = 952) was divided as follows: ● ● ● ●

20.8 per cent of the respondents were young adults (aged 18–34 years); 29.0 per cent were early middle-aged adults (aged 35–54 years); 21.8 per cent were late middle-aged adults (aged 55–64 years); and 28.4 per cent were older adults (aged 65 years or older).

Most of the young adults (71.7 per cent), early middle-aged adults (79.3 per cent) and late middle-aged adults (59.9 per cent) were employed full or part time. Around a quarter of the late middle-aged adults (26.0 per cent) were retired, while most of the older adults were retired (81.9 per cent). About half of the young adults (56.1 per cent) had been less than five years in their suburb and 81.9 per cent of early middle-aged adults less than 20 years in their suburb. About half of the middle-aged adults (47.6 per cent) and older adults’ sample (54.8 per cent) had been living more than 20 years in their suburb. Younger adults spent less time in and around their neighbourhoods compared to older adults. About two-thirds of younger adults (61.0 per cent) spent five or less days in and around their suburb compared to late middle-aged adults and older adults who spent five or more days in their suburb (63.9 per cent and 76.3 per cent, respectively). Most of the respondents across the four age groups were satisfied or very satisfied with their personal circumstances. When asked about their health, 72.0 per cent of the youngest cohort, 67.0 per cent of early middle-aged, 67.9 per cent of late middle-aged adults and 78.6 per cent of older adults were satisfied or very satisfied. In terms of mobility, 70.8 per cent of young adults, 78.3 per cent of early middle-aged adults, 77.2 per cent of late middle-aged adults and 84.4 per cent of older adults were satisfied or very satisfied with mobility. Descriptive statistics and correlations for the social and built environment Descriptive statistics of the perceived social and built environment measures (including third places) are shown in Table 9.3. One-way ANOVA was conducted to determine if perceived social and built environment measures were different in the four age cohorts. The results of the Tukey post hoc tests for differences between age groups are shown in the table.

9.41*

0.44

7.16*

9.14*

Community spirit

Community inclusion

Level of trust

Participation in

 

Perceived built

0.99

2.64

3.10

1.99

General environment

Density

Land-use mix

Accessibility

Feelings towards urban

1.94

0.17

Natural environment

Footpaths

194

2.18

0.84

Services

Public transport

194

0.45

Shops

193

194

193

194

 

4.58*

Perceived third places

 

191

193

191

185

197

197

 

197

193

193

192

194

Cafés, bars, restaurants

growth

3.25

3.63*

General appearance

environment

14.8*

Community well-being

community activities

3.06*

Safety

197

 

20.21* 197

Social environment

 

27.14* 189

Belonging to suburb

satisfaction (SI S)

Social interaction

frequency (SI F)

28.17* 195

Social interaction

3.42

3.69

3.15

3.02

3.49

3.88 d

 

3.45

3.17

3.58

3.92

3.42

3.77

 

3.73cd

1.05

1.07

1.22

1.20

1.08

1.06

 

0.78

1.09

0.86

0.93

0.80

0.73

 

0.84

0.51

0.35

0.39

0.41

0.20

0.25

 

0.26

0.29

0.25

0.11

0.30

0.36

 

0.32

0.36

1.13

2.25

0.57

0.86

0.40

0.56

0.35

d

0.87

0.79

0.76

0.24

 

0.69

1.00

0.40

0.34

0.31

0.25

0.21

0.27

 

0.21

0.17

0.30

0.19

0.35

0.45

 

0.58

0.22

0.35

0.39

0.53

0.43

0.28

 

1.00

 

SI S

SI F

3.09b

3.67

3.41d

3.80

  0.87

 

0.95

0.93

3.78cd

3.25cd

2.97cd

r

r

274

274

272

271

273

273

 

269

270

265

268

276

276

 

275

271

270

267

270

276

276

 

275

276

3.45

3.77

3.01

3.10

3.47

3.84d

 

3.47

3.14

3.51

4.07

3.34d

3.74

 

3.81cd

2.17 cd

2.80ad

3.59

3.41d

3.77d

3.93cd

 

3.39cd

3.04cd

M

1.11

1.11

1.35

1.33

1.15

1.15

 

0.80

1.26

0.95

0.99

0.83

0.70

 

0.81

1.03

0.78

0.88

0.85

0.77

0.89

 

1.07

0.95

SD

0.52

0.42

0.22

0.27

0.35

0.36

 

0.07

–0.02

0.20

0.04

0.30

0.23

 

0.39

0.46

0.22

0.32

0.53

0.23

0.40

 

0.62

1.00

SI F

r

0.28

0.27

0.06

0.10

0.19

0.26

 

0.09

–0.10

0.15

0.16

0.30

0.24

 

0.37

0.27

0.21

0.40

0.52

0.20

0.29

 

1.00

 

SI S

r

Early Middle-aged Adults (35–54)

SD

n

M

n

F

Young Adults (18–34)

 

Variables

203

204

204

201

205

205

 

206

205

204

205

206

206

 

206

205

205

203

203

206

206

 

205

206

n

b

3.46

3.55

3.02

3.14

3.37

3.72

 

3.36

2.92

3.34

4.05

3.52

3.91

 

4.08ab

2.53

2.89

3.63

3.55

3.90

4.18ab

 

3.68abd

3.39abd

M

1.12

1.19

1.34

1.30

1.23

1.22

 

0.82

1.29

1.09

1.10

0.93

0.72

 

0.75

1.18

0.73

0.89

0.88

0.80

0.90

 

1.02

0.89

SD

0.38

0.28

0.22

0.17

0.24

0.36

 

–0.10

–0.08

0.13

0.04

0.16

0.25

 

0.33

0.43

0.33

0.40

0.50

0.39

0.44

 

0.64

1.00

SI F

r

0.25

0.14

0.10

0.09

0.15

0.29

 

–0.01

–0.12

0.26

0.02

0.21

0.32

 

0.38

0.25

0.33

0.38

0.59

0.41

0.49

 

1.00

 

SI S

r

Late Middle-aged Adults (55–64)

270

267

258

267

266

267

 

267

268

266

267

270

270

 

270

270

269

266

270

270

270

 

266

270

n

3.49

3.58

3.16

3.31

3.47

3.52ab

 

3.32

2.89

3.39

4.07

3.56b

3.89

 

4.13ab

2.62ab

3.05b

3.71

3.75ab

3.96b

4.34ab

 

4.00abc

3.63abc

M

1.18

1.25

1.40

1.35

1.23

1.31

 

0.91

1.30

1.01

1.12

0.85

0.75

 

0.71

1.19

0.78

0.81

0.81

0.77

0.78

 

0.90

0.87

SD

Older Adults (65+)

Descriptive statistics for the social and built environment (and third places), and their correlations with social interactions

 

Table 9.3

0.49

0.45

0.27

0.31

0.33

0.30

 

0.05

–0.07

0.25

0.04

0.36

0.35

 

0.43

0.34

0.41

0.31

0.61

0.24

0.42

 

0.66

1.00

SI F

r

0.33

0.29

0.06

0.11

0.26

0.25

 

0.01

0.05

0.15

0.12

0.28

0.30

 

0.37

0.34

0.36

0.36

0.59

0.19

0.43

 

1.00

 

SI S

r

136  Handbook of quality of life research

F

0.15

0.49

Variables

Open public spaces

Community places

193

194

3.55

3.43 1.05

1.06 0.40

0.48 0.28

0.36

SI S

SI F 272

272 3.50

3.46

M

1.20

1.12

SD

0.45

0.50

SI F

r

0.27

0.32

SI S

r

Early Middle-aged Adults (35–54) r

r

n

SD

n

M

Young Adults (18–34)

202

205 3.55

3.47

M

1.26

1.11

SD

0.28

0.28

SI F

r

0.25

0.14

SI S

r

Late Middle-aged Adults (55–64) n

262

268

3.63

3.41

M

1.17

1.17

SD

Older Adults (65+) n

0.36

0.49

SI F

r

0.30

0.29

SI S

r

Note: Superscripts for Tukey post hoc tests: a Significantly different from young adults. b Significantly different from early middle-aged adults. c Significantly different from late middle-aged adults. d Significantly different from older adults. F = ANOVA F statistic; n = number of respondents; M = mean; SD = standard deviation; r SI F = Spearman’s correlation with social interaction frequency; r SI S = Spearman’s correlation with social interaction satisfaction; * p < 0.05.

 

 

How neighbourhood social and built environments influence social interactions  137

138  Handbook of quality of life research Many of the respondents across all age cohorts were very satisfied or satisfied with their perceived social and built environments. Only the social environment measure of participation in community groups had a mean score below the mid-point (< 3.0) in all age groups. There were moderately high Spearman correlation coefficients between social interaction frequency and social interaction satisfaction (r = 0.69 in young adults, r = 0.62 in early middle-aged adults, r = 0.64 in late middle-aged adults and r = 0.66 in older adults). Correlation coefficients between community spirit and social interaction frequency (and social interaction satisfaction) were also moderately correlated (r > 0.50) in all age groups. Objective built environment measures All the correlation coefficients between social interaction frequency and social interaction satisfaction and the objective built environment measures were negative in direction, though weak (–0.22 > r > –0.17). That is, more built-up areas tended to have less social interaction frequency and satisfaction. Mediation analysis The mediation conditions were not met in all models. In particular, multiple regression analyses showed: (1) no significant relationships between the objective built environment measures and social interaction frequency and satisfaction; and (2) no significant relationships between the objective and perceived built environment measures. Further, when the objective and perceived built environment measures were entered into models to predict social interaction frequency and social interaction satisfaction, then the perceived measures did not behave as mediators. These results were like those from an initial study that only considered older adults (Veeroja, 2019). Thus, since the objective measures were not empirically linked with either social interaction frequency or satisfaction, they were excluded from further analyses. Frequency of social interactions The perceived social and built environment and perceived third places measures were entered into a multiple regression model predicting social interaction frequency, while statistically controlling for socio-demographic attributes. The social interaction frequency models are shown in Table 9.4. The models were statistically significant in all age groups, and in each model more than half the variance was explained by the independent variables. The results showed that: ● greater participation in community activities was significantly associated with more frequent social interactions among all age groups; ● greater community spirit, level of trust and community well-being were significantly associated with more frequent social interactions for young adults; ● for early middle-aged adults, greater community spirit and community well-being were important for predicting more frequent social interactions; ● for older adults, greater feeling of belonging to suburb and community spirit predicted more frequent social interactions; ● better accessibility was the only significant perceived built environment measure associated with more frequent social interactions; and ● in terms of third places, footpaths were associated with more frequent social interactions in early middle-aged and late middle-aged adults.

How neighbourhood social and built environments influence social interactions  139 Table 9.4

Regression coefficients for the social interaction frequency and satisfaction models

Predictors

Social Interaction Frequency

Social environment

Social Interaction Satisfaction Late Early

Young

Early

Late

Older

Young

adults

mid.-aged

mid.-aged

adults

adults

adults

adults

 

 

 

 

 

mid.-aged

mid.-aged

adults

adults

 

 

Older adults  

measures Social interaction

 

 

 

 

0.60**

0.60**

0.49**

0.42**

frequency

 

 

 

 

(0.07)

(0.08)

(0.08)

(0.07)

Belonging to

–0.04

0.12

0.12

0.17*

–0.13

–0.06

0.14

0.10

suburb

(0.08)

(0.07)

(0.08)

(0.07)

(0.07)

(0.08)

(0.08)

(0.08)

Safety Community spirit

0.05

–0.04

0.14

–0.11

0.05

–0.03

0.02

–0.14*

(0.11)

(0.08)

(0.10)

(0.07)

(0.09)

(0.09)

(0.10)

(0.07)

0.23*

0.44**

0.06

0.41**

0.12

0.29**

0.43**

0.42*

(0.11)

(0.07)

(0.10)

(0.08)

(0.10)

(0.09)

(0.10)

(0.09)

Community

0.01

–0.10

0.13

–0.05

0.05

0.19**

0.01

0.13

inclusion

(0.09)

(0.06)

(0.07)

(0.07)

(0.08)

(0.07)

(0.07)

(0.07)

Level of trust

0.21*

–0.10

0.08

–0.05

–0.28**

–0.05

–0.15

0.06

(0.09)

(0.07)

(0.09)

(0.08)

(0.08)

(0.08)

(0.08)

(0.08)

Participation

0.12*

0.21**

0.28**

0.16**

0.03

–0.03

0.00

0.13**

in community

(0.06)

(0.05)

(0.05)

(0.04)

(0.05)

(0.06)

(0.05)

(0.04)

activities Community

0.20*

0.17*

0.17

0.05

0.47**

0.07

–0.15

0.09

well-being

(0.10)

(0.07)

(0.10)

(0.09)

(0.08)

(0.09)

(0.10)

(0.09)

Built environment

 

 

 

 

 

 

 

 

measures General appearance –0.05

–0.11

–0.04

–0.11

0.01

–0.07

0.03

–0.01

(0.12)

(0.08)

(0.10)

(0.09)

(0.10)

(0.10)

(0.10)

(0.09)

General

–0.03

–0.02

0.01

0.11

–0.04

0.04

–0.03

–0.06

environment

(0.09)

(0.07)

(0.07)

(0.06)

(0.08)

(0.09)

(0.07)

(0.06)

Density Land-use mix Accessibility

–0.12

–0.09

0.06

0.01

–0.05

0.09

–0.08

0.08

(0.08)

(0.05)

(0.06)

(0.05)

(0.06)

(0.06)

(0.05)

(0.05)

0.07

–0.03

–0.02

–0.02

0.08

–0.05

0.11*

–0.05

(0.07)

(0.05)

(0.05)

(0.05)

(0.06)

(0.06)

(0.05)

(0.05)

0.13*

–0.03

0.06

–0.02

0.07

–0.08

–0.04

0.02

(0.06)

(0.04)

(0.05)

(0.04)

(0.05)

(0.05)

(0.05)

(0.04)

Feelings towards

0.01

0.11

–0.20

–0.04

–0.09

0.03

0.12

–0.08

urban growth

(0.09)

(0.07)

(0.08)

(0.07)

(0.08)

(0.08)

(0.08)

(0.07)

 

 

 

 

0.09

0.01

0.10

0.04

–0.15**

0.04

0.01

–0.01

Third places Cafés, bars and restaurants

(0.07)

(0.05)

(0.05)

(0.04)

(0.06)

(0.06)

(0.05)

(0.04)

Shops

–0.11

0.04

0.11

0.01

0.09

0.02

0.02

0.07

(0.06)

(0.05)

(0.06)

(0.05)

(0.05)

(0.06)

(0.06)

(0.04)

Services

0.05

–0.08

–0.05

0.00

–0.05

0.02

–0.02

–0.12**

(0.07)

(0.05)

(0.06)

(0.04)

(0.06)

(0.06)

(0.06)

(0.04)

Public transport

0.03

–0.05

0.01

–0.01

0.12*

–0.04

–0.05

  (0.04)

(0.07)

(0.05)

(0.05)

(0.04)

(0.06)

(0.06)

(0.05)

Natural

0.03

0.05

–0.11

0.11

–0.03

–0.03

–0.04

0.03

environment

(0.09)

(0.06)

(0.06)

(0.06)

(0.07)

(0.07)

(0.06)

(0.05)

140  Handbook of quality of life research Predictors

Footpaths Open public spaces Community places Socio-demographic

Social Interaction Frequency

Social Interaction Satisfaction Late Early

Young

Early

Late

Older

Young

adults

mid.-aged

mid.-aged

adults

adults

adults

adults

mid.-aged

mid.-aged

adults

adults

Older adults

0.09

0.18**

0.23**

0.07

0.01

–0.02

–0.02

0.09

(0.08)

(0.07)

(0.07)

(0.06)

(0.07)

(0.08)

(0.07)

(0.06)

0.02

0.07

0.01

0.09

0.01

–0.04

–0.11

–0.02

(0.10)

(0.07)

(0.08)

(0.07)

(0.08)

(0.08)

(0.08)

(0.07)

0.04

0.06

–0.05

–0.04

–0.04

–0.05

0.07

–0.02

(0.08)

(0.05)

(0.05)

(0.05)

(0.07)

(0.06)

(0.05)

(0.05)

 

 

 

 

 

 

 

 

attributes Employment (ref:

–0.16

–0.01

0.20

0.03

 

 

 

 

full time) part time

(0.16)

(0.11)

(0.13)

(0.25)

 

 

 

 

Looking for paid

–0.18

0.45

–0.06

 

 

 

 

 

work

(0.24)

(0.25)

(0.27)

 

 

 

 

 

Studying

–0.38*

0.19

0.23

 

 

 

 

 

(0.19)

(0.30)

(0.49)

 

 

 

 

 

Caring or home

0.14

0.29

–0.10

 

 

 

 

 

duties full time

(0.22)

(0.15)

(0.26)

 

 

 

 

 

Retired Other

–0.79

0.99**

–0.06

–0.08

 

 

 

 

(0.60)

(0.38)

(0.13)

(0.23)

 

 

 

 

 

0.39

0.39

0.70

 

 

 

 

 

(0.24)

(0.24)

(0.69)

 

 

 

 

Education (ref:

0.33

0.02

–0.07

–0.02

 

 

 

 

bachelor’s degree

(0.27)

(0.16)

(0.16)

(0.12)

 

 

 

 

or higher) less than Year 12 Completed Year 12

0.01

0.37*

–0.04

0.10

 

 

 

 

(0.19)

(0.15)

(0.18)

(0.15)

 

 

 

 

Certificate,

0.05

–0.20*

–0.34*

0.00

 

 

 

 

diploma or trade

(0.13)

(0.10)

(0.14)

(0.11)

 

 

 

 

qualification Health (ref: 5 –

–0.45

–1.03**

1.02*

–1.40**

 

 

 

 

satisfied) 1

(0.41)

(0.33)

(0.40)

(0.44)

 

 

 

 

2 3 4

–0.07

–0.26

0.57*

0.01

 

 

 

 

(0.33)

(0.24)

(0.26)

(0.28)

 

 

 

 

0.37

–0.05

0.16

–0.04

 

 

 

 

(0.24)

(0.15)

(0.18)

(0.16)

 

 

 

 

0.11

–0.13

0.07

–0.08

 

 

 

 

(0.17)

(0.11)

(0.16)

(0.11)

 

 

 

 

Mobility (ref: 5 –

0.85

0.78

–1.07*

0.51

–0.38

0.17

–0.21

0.58

very satisfied) 1

(0.71)

(0.42)

(0.47)

(0.48)

(0.61)

(0.49)

(0.39)

(0.45)

2 3 4

–0.00

0.09

–0.46

0.46

0.31

–0.25

–0.56*

0.71**

(0.34)

(0.27)

(0.27)

(0.27)

(0.24)

(0.31)

(0.26)

(0.23)

–0.34

0.21

–0.25

–0.07

–0.04

0.03

–0.37*

–0.02

(0.20)

(0.14)

(0.19)

(0.18)

(0.14)

(0.16)

(0.16)

(0.15)

–0.11

–0.20

–0.21

–0.09

–0.00

–0.06

–0.12

–0.08

(0.16)

(0.10)

(0.16)

(0.11)

(0.12)

(0.11)

(0.13)

(0.10)

How neighbourhood social and built environments influence social interactions  141 Predictors

Social Interaction Frequency

Social Interaction Satisfaction Late Early

Young

Early

Late

Older

Young

adults

mid.-aged

mid.-aged

adults

adults

adults

adults

mid.-aged

mid.-aged

adults

adults

Older adults

Standard of living

 

 

 

 

–1.07**

–0.68

–0.86

1.63*

(ref. 5 – very

 

 

 

 

(0.35)

(0.37)

(0.56)

(0.75)

satisfied) 1 2 3 4

 

 

 

 

–0.65*

–0.19

–0.31

–0.90

 

 

 

 

(0.32)

(0.26)

(0.46)

(0.46)

 

 

 

 

–0.12

–0.22

–0.21

–0.45**

 

 

 

 

(0.16)

(0.15)

(0.16)

(0.17)

 

 

 

 

–0.09

–0.00

–0.36**

–0.04

 

 

 

 

(0.13)

(0.13)

(0.12)

(0.09)

Constant

–0.27

0.54

–0.37

0.89

0.56

0.63

1.21*

0.89*

 

(0.48)

(0.40)

(0.55)

(0.50)

(0.47)

(0.49)

(0.52)

(0.43)

Observations

166

243

189

235

164

242

189

232

R-squared

0.63

0.63

0.59

0.57

0.73

0.58

0.68

0.62

Note: Standard errors in parentheses; ref: reference group; significant predictors in italic: * p < 0.1, ** p < 0.05.

Satisfaction with social interactions Social interaction satisfaction was modelled similarly to social interaction frequency, except that social interaction frequency was also included as a predictor of satisfaction. Results are shown in Table 9.4. The models were statistically significant for all of the four age groups, and about two-thirds of the variance in social interaction satisfaction was explained by the model predictors for young adults, late middle-aged adults and older adults. The results show more frequent social interactions were strongly associated with more satisfied social interactions in younger age groups, though not as strong in older age groups. In terms of the social environment: ● level of trust decreased younger adults’ satisfaction with social interactions, while community well-being increased it; ● younger adults’ community well-being increased social interaction satisfaction, though trust was negatively associated – this seems to be a model suppression effect as the correlations between trust and interaction satisfaction were positive; ● increased community spirit was associated with more satisfied social interactions in older age groups; ● higher community spirit and community inclusion were associated with more satisfied social interactions in early middle-aged adults; and ● better community spirit increased late middle-aged adults’ social interaction satisfaction, and higher community spirit and participation in community activities increased older adults’ social interaction satisfaction, but safety decreased it. In terms of the perceived built environment: ● land-use mix was associated with more satisfied social interactions in late middle-aged adults; ● cafés, bars and restaurants and public transport stops were significant third places for younger adults’ social interactions, though cafés, bars and restaurants were negatively associated with social interaction satisfaction – this also seems to be a suppression effect;

142  Handbook of quality of life research ● services decreased older adults’ social interaction satisfaction while public transport stops increased their satisfaction with social interactions; ● in young adults, lower standard of living impeded social interaction satisfaction, as did lower mobility score in late middle-aged adults; ● lower mobility levels and lower standard of living impeded social interaction satisfaction in older adults; and ● there was a positive coefficient for those older adults who were not satisfied with mobility – however, this category had only nine respondents and this result may be unreliable.

DISCUSSION This chapter has summarised research findings to date investigating the influence of a wide range of perceived and objective built environment measures and perceived social environment measures on social interaction frequency and satisfaction, with the Melbourne study explicitly looking at differences among four age groups. As such, it is one of the most comprehensive quantitative studies of social interaction in developed urban environments. Previous studies had either focused on specific or very limited sets of factors relating to built and social neighbourhood features with social interactions, or had focused only on the built or social environments. The neighbourhood environment is probably now even more important to all age groups considering the COVID-19 pandemic. The Melbourne study findings indicated that perceived social environment is more important to neighbourhood social interactions across all age groups. Community spirit and participation in community activities in neighbourhoods seem to be particularly important. However, how to achieve this may need to be reconsidered in the context of social distancing. That might include, for example: ● facilitating local social interactions online (via improving access to the Internet and people’s skills to use technology, and implementing online community events, forums and discussions); ● using built environment design (spacing seating and walkways, and marking out safe physical distancing); and ● innovative technologies to encourage social participation to help people to practise physical distancing (for example, robotics and touchless doors). The Melbourne study found that the effect of the objective built environment (including third places) on social interactions were not mediated by the perceived built environment. That was an unexpected finding. An explanation could be that people perceive (and use) their neighbourhoods more flexibly (Ivory et al., 2015) compared to the artificial ‘neighbourhood’ boundaries imposed in the Melbourne study. Ivory et al. (2015) argued the need to avoid thinking in terms of geographically fixed boundaries because participants’ activities defined their neighbourhoods. A future study could collect global positioning system (GPS) data to distinguish where people go, and to use that data to define their neighbourhoods instead of using fixed uniform buffers across all participants. This could be used in combination with SoftGIS for tracking and surveying respondents to better understand why certain locations are chosen over other locations for neighbourhood social interactions.

How neighbourhood social and built environments influence social interactions  143 The Melbourne study found that both the objective and perceived built environment measures were weak predictors of social interactions, with similar findings found in other research (French et al., 2014; McCrea et al., 2006; Nyunt et al., 2015; Orstad et al., 2017). Future studies are needed to investigate whether there are more appropriate objective and perceived measures of the built environment for predicting social interactions, and whether these attributes are more or less important depending on life stage. Limitations There are several limitations with the Melbourne study: ● the well-being survey was carried out across a limited number of local government areas in Melbourne, and the results may not be generalisable to cities with different densities, population sizes, and cultural and economic contexts; ● the quantitative analysis did not consider quality of the built environment or self-selection into the neighbourhood (that is, resident neighbourhood choice); and ● the objective third places were weighted equally in the study.

CONCLUSION A considerable amount of research has contributed to the understanding of relationships between social and built environments and neighbourhood social interactions, which has been shown to be a domain of QOL. The Melbourne study reported here has explicitly investigated a comprehensive set of variables across four age groups. The results showed that perceived social environment measures were stronger predictors of both social interaction frequency and satisfaction in all age cohorts than objective and perceived measures of the built environment, excepting some perceptions of third places. Future research is needed to better measure the quality of third places and micro-scale environments, which could assist policy- and decision-makers better plan urban environments that are more inclusive and support social interactions critical to people’s well-being and QOL. The research findings so far highlight the importance of government policymakers and planners, as well as non-governmental organisations and community groups, to actively focus on how to improve social environments (especially community spirit, and maximising participation in community activities) and better support social interactions and overall well-being of residents across all age groups.

NOTE 1. Analysis of the CSIRO-funded 2015 Community Functioning and Well-being Survey was supported by an RMIT University Vice-Chancellor’s Senior Research Fellowship.

144  Handbook of quality of life research

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How neighbourhood social and built environments influence social interactions  145 McCrea, R., Shyy, T.-K. and Stimson, R. (2006), ‘What is the strength of the link between objective and subjective indicators of urban quality of life?’, Applied Research in Quality of Life, 1, 79–96. Moser, D.A., Glaus, J., Frangou, S. and Schechter, D.S. (2020), ‘Years of life lost due to the psychosocial consequences of COVID19 mitigation strategies based on Swiss data’, European Psychiatry, 63, 1–14. Nyunt, M.S.Z., Shuvo, F.K. and Eng, J.Y. et al. (2015), ‘Objective and subjective measures of neighborhood environment (NE): relationships with transportation physical activity among older persons’, International Journal of Behavioral Nutrition and Physical Activity, 12, Article 108. Oldenburg, R. (1989), The Great Good Place: Café, Coffee Shops, Community Centers, Beauty Parlors, General Stores, Bars, Hangouts, and How They Get You Through the Day, New York: Paragon House Publishers. Oldenburg, R. (1997), ‘Our vanishing third places’, Planning Commissioners Journal, 25, 6–10. Orstad, S.L., McDonough, M.H. and Stapleton, S. et al. (2017), ‘A systematic review of agreement between perceived and objective neighborhood’, Environment and Behavior, 49, 904–32. Pinquart, M. and Sorensen, S. (2001), ‘Influences on loneliness in older adults: a meta-analysis’, Basic and Applied Social Psychology, 23, 245–66. Sander, J., Schupp, J. and Richter, D. (2017), ‘Getting together: social contact frequency across the life span’, Developmental Psychology, 53, 1571–88. Sharmeen, F., Arentze, T. and Timmermans, H. (2014), ‘Dynamics of face-to-face social interaction frequency: role of accessibility, urbanization, changes in geographical distance and path dependence’, Journal of Transport Geography, 34, 211–20. Shor, E. and Roelfs, D.J. (2015), ‘Social contact frequency and all-cause mortality: a meta-analysis and meta-regression’, Social Science and Medicine, 128, 76–86. Simmel, G. and Hughes, E.C. (1949), ‘The sociology of sociability’, American Journal of Sociology, 55, 254–61. StataCorp (2015), Stata Statistical Software: Release 14, College Station, TX: StataCorp LP. The State of Victoria (2016a), ‘Principal bicycle network: Vicroads’, https://​discover​.data​.vic​.gov​.au/​ dataset/​principal​-bicycle​-network​-pbn. The State of Victoria (2016b), ‘Property map polygons – Vicmap Property Simplified 1’, Department of Environment, Land, Water & Planning, https://​discover​.data​.vic​.gov​.au/​dataset/​vicmap​-property​ -simplified​-1. The State of Victoria (2016c), ‘Public transport: a collection of PTV datasets’, https://​www​.data​.vic​.gov​ .au/​data/​dataset/​public​-transport​-a​-collection​-of​-ptv​-datasets. The State of Victoria (2016d), ‘Vicmap features of interest’, https://​www​.land​.vic​.gov​.au/​maps​-and​ -spatial/​spatial​-data/​vicmap​-catalogue/​vicmap​-features​-of​-interest. The State of Victoria (2016e), ‘Vicmap transport – road network’, https://​www​.land​.vic​.gov​.au/​maps​ -and​-spatial/​spatial​-data/​vicmap​-catalogue/​vicmap​-transport. Van den Berg, P., Kemperman, A., De Kleijn, B. and Borgers, A. (2016), ‘Ageing and loneliness: the role of mobility and the built environment’, Travel Behaviour and Society, 5, 48–55. Van den Berg, P., Sharmeen, F. and Weijs-Perrée, M. (2017), ‘On the subjective quality of social interactions: influence of neighborhood walkability, social cohesion and mobility choices’, Transportation Research Part A: Policy and Practice, 106, 309–19. Veeroja, P. (2019), ‘The role of social and built environments in supporting older adults’ social interaction’, doctoral dissertation, The University of Melbourne. Wang, D. and Lin, T. (2013), ‘Built environments, social environments, and activity-travel behavior: a case study of Hong Kong’, Journal of Transport Geography, 31, 286–95. Weijs-Perrée, M., Van den Berg, P., Arentze, T. and Kemperman, A. (2015), ‘Factors influencing social satisfaction and loneliness: a path analysis’, Journal of Transport Geography, 45, 24–31. Windsor, T., Pearson, E. and Crisp, D.A. et al. (2012), Neighbourhood Characteristics and Ageing Well – a Survey of Older Australian Adults, Australian Government Department of Health and Ageing, https://​www​.cepar​.edu​.au/​publications/​report​-govt​-submissions/​neighbourhood​-characteristics​-and​ -ageing​-well​-survey​-older​-australian​-adults. Wright, K.B. and Patterson, B.R. (2006), ‘Socioemotional selectivity theory and the macrodynamics of friendship: the role of friendship style and communication in friendship across the lifespan’, Communication Research Reports, 23, 163–70.

PART III QUALITY OF LIFE IN METROPOLITAN AREAS, CITIES AND NEIGHBOURHOODS

10. Quality of life in large-scale, big-city urban environments: a world perspective Robert W. Marans and Robert J. Stimson

INTRODUCTION The growth of cities and their hinterlands has been prevalent in the literature of sociologists, geographers, political scientists and urban planners for more than a quarter-century (Ewing et al., 2002; Sassen, 2001). This growth pattern continues throughout the world and was highlighted in the US through the 2020 census (United Nations, 2019; US Census, 2022). Researchers have analysed the advantages and disadvantages of large-scale urban living, often supported by empirical evidence, including social indicators and data from quality of life (QOL) studies. This chapter references some of the key literature stressing the need to measure progress in nations using social indicators, often derived from QOL studies. Drawing on the work of scholars from the International Society for Quality-of-Life Studies (ISQOLS), it then refers to several widely used indicators used to measure the QOL in nations or places. Finally, the chapter reviews several QOL studies conducted early in the twentieth century in various parts of the world, focusing explicitly on large-scale urban areas.

OVERVIEW OF THE DEVELOPMENT OF SOCIAL INDICATORS ENCOMPASSING QOL During the latter part of the twentieth century, several governments, researchers and public policy organisations promoted the idea that economic indicators alone were not sufficient to measure and track societal well-being in nations (Bauer, 1966; Innes de Neufville, 1975; Sheldon and Moore, 1968). Initially, the focus was on objective social indicators of well-being (discussed in detail in Chapter 5), drawing on statistical data from public governmental agencies in reporting on social trends and the changing status of a society (see Gross, 1967) to supplement traditional economic indicators like gross domestic product (GDP) per capita. With a few exceptions, the indicators sought to measure the well-being of a nation as a whole and the QOL of its people. Little consideration was given to establishing indicators for sub-systems or spatial parts of nations, such as cities or communities within them. But in the 1970s, that quickly changed as studies included social indicators for cities and later for spatial units within a city (see Smith, 1973). There was also a movement to generate subjective indicators to reflect the perceptions of people regarding their well-being and QOL. As discussed in Chapter 6, the early work of Campbell et al. (1976) in the US and Hall (1976) in the UK pioneered research dealing with subjective QOL. Implicit was the notion that both subjective measures of well-being and QOL 147

148  Handbook of quality of life research generated from surveys, together with objective indicators derived from hard data, were necessary to inform governmental policies. In 2001, ISQOLS conducted a systematic assessment of 22 widely used social indexes in terms of their comprehensiveness, their reliability and validity, and their potential to inform and potentially influence public policy (Hagerty et al., 2001). Many of the indexes were developed in response to the social indicators movement referred to earlier, while other indexes evolved from policies of international organisations – such as the United Nations and the World Health Organization – and from publishing organisations. While many indexes relied on objective measures (see Chapter 5), others involved subjective data collected from social surveys (see Chapter 6). Hagerty and his colleagues reported that economic measures were most prevalent, followed closely by health measures and those associated with family life. Some considered spatial or environmental attributes or were associated with specific places. The Places Rated Almanac undertaken by Money magazine was a rare example of indexes developed explicitly for cities and metropolitan areas (Boyer and Savageau, 1981; Scharf, 2021). The review’s overall assessment of the indexes was unsatisfactory, particularly in terms of the validity of their measures and their lack of relevance to policymakers. A similar criticism could be made of the rating systems promoted by various organisations in their publications, offering readers listings of the best place to live or retire (such as Fuller, 2018; Savageau, 2007; Sperling and Sander, 2006).

QUALITY OF URBAN LIFE STUDIES As the twenty-first century was approaching, several scholars – including two editors of this Handbook – discussed possibilities for developing indices that would measure and track QOL in cities and metropolitan areas in various parts of the world. The intent was that the studies would take an integrated approach (see Chapter 7), developing indicators drawn from social surveys, collecting data on the subjective assessment/evaluation of QOL, and complemented by objective measures using secondary data. Moreover, the indicators should be comparable across each city and replicated over time. Several programmes of research were initiated aimed at measuring urban quality of living in a particular geographic area. The outcome of the collaborations was reported in chapters in an edited book on quality of urban life (Marans and Stimson, 2011). Four city case studies are discussed in this chapter. They are: ● the Detroit metropolitan region in Southeast Michigan in Midwest US, which includes a former industrial city undergoing considerable economic transformation and its surrounding counties; ● the Brisbane-South East Queensland metropolitan region, which includes Australia’s third largest city, a rapidly growing, sprawling low-density services-dominated metropolitan region with a significant focus on tourism; ● Istanbul and its surroundings, a diverse ancient city at the crossroads of Europe and Asia; and ● Hong Kong in China, a high-density mega city in Asia. In these four studies, similar survey questionnaires were employed to generate measures or indicators of subjective QOL, and similar analytical tools were used to model the interactions

Quality of life in large-scale, big-city urban environments: a world perspective  149 between the subjective assessment of QOL domains and objective measures of the elements of the social, natural and built environments of those cities. In the four large city case studies that follow, the emphasis is on outlining their methodologies rather than focusing in detail on empirical findings that are available in the publications cited.

METROPOLITAN DETROIT Following the seminal research in the 1970s at the University of Michigan (U-M) dealing with the quality of American life (Campbell et. al., 1976), several researchers in the Institute for Social Research (ISR) decided to replicate the research in the city of Detroit and its environs. In 1975, a household survey was conducted in Detroit and in three surrounding counties (Rodgers and Marans, 1975). While surveys were conducted annually as part of the Detroit Area Study (DAS) since 1951, none had dealt exclusively with place of residence and its contributions to people’s QOL.1 Twenty-five years later, there was an opportunity to continue research investigating QOL in metropolitan Detroit when Marans was selected as the faculty investigator for the DAS 2001 household survey that was conducted across the Detroit region encompassing seven counties, one of which includes the city of Detroit.2 Unlike the earlier DAS, the 2001 DAS survey would produce baseline data covering quality of urban life from the perspective of the region’s residents. In addition to the survey responses, the DAS 2001 compiled contextual data about the local communities and environments associated with each survey respondent, including housing and demographic characteristics, land-use characteristics and other characteristics of the communities where respondents live (that is, growth rates, employment, school statistics, etc.). In addition to measuring the QOL at the beginning of the twenty-first century, the DAS 2001 had other objectives, namely: ● to determine how public perceptions and salient aspects of community life throughout the region had changed since the mid-1970s; ● to provide accurate and credible information on various aspects of urban life that could inform government, corporate, institutional and community decision-makers; ● to establish a benchmark for assessing changes in environmental and community conditions throughout the twenty-first century; and ● to determine how much public perceptions corresponded to the community and environmental conditions associated with places people live. Methodology The DAS 2001 used a multi-method approach involving the collection of information using: ● survey questionnaires; ● the US census; and ● other secondary objective data about the respondents’ communities and their physical surroundings.

150  Handbook of quality of life research The content of questionnaires was determined in part by input from the sponsors of the study and from other key stakeholders (county planners and other government officials, corporate and non-governmental organisations, and other decision-making bodies operating in the region). They helped identify what information about their constituencies was necessary to better inform decision-making. Those needs were then combined with the interests of the research team, which included a desire to replicate questions asked in past regional surveys.3 The criterion used in the selection of questions was the centrality of each to the quality of community life theme.4 A summary of questionnaire content is shown in Table 10.1. Table 10.1

Quality of urban life topics included in the 2001 Detroit Area Study

Residential domain satisfaction

Residential mobility

Housing

Residential history

Neighbourhood

Factors influencing residential choice

Community

Moving intentions

County

Neighbourhood preference

Social capital

Government and taxes

Neighbouring Community involvement Public services and facilities

Environment problems and conservation of open land

Schools Police Road maintenance Willingness to pay for new/improved services and facilities

Prospect for the future

Travel

Other domain satisfactions

Public transit use

Job

Work trips

Health

Shopping trips

Standard of living Family Friends Leisure

Health and walking – physical activity

Household and respondent characteristics

Source: The authors.

Questionnaires Two types of questionnaire were used to collect the information. One was administered through face-to-face interviews, and a shorter version was administered by a mail self-administered survey. Face-to-face interviews Between April and August 2001, 315 face-to-face interviews were conducted with an adult drawn from a sample of households in three of metropolitan Detroit’s seven counties. The average length of the interviews was 60 minutes. In addition to asking each respondent a series of questions, interviewers recorded data about the respondent’s dwelling and the area around it. As an incentive, metro-park passes were mailed together with a cover letter to half of the sample households, while the remaining half received five dollars. An additional five dollars

Quality of life in large-scale, big-city urban environments: a world perspective  151 was given to respondents who completed the interview. The survey response rate was 59.8 per cent.5 Mail questionnaires To cover the remaining counties in the metropolitan Detroit area and expand the number of respondents in the initial three counties, a shorter version of the questionnaire used in the face-to-face interview survey was mailed to a random sample of over 7000 adults throughout the region. The mail questionnaire eliminated about half of the original questions to produce a questionnaire that could be completed in about 20 minutes. Metro-park passbooks and five dollars were again used as incentives. The mail survey yielded 4077 responses, representing a 56.4 per cent response rate. Data from the face-to-face interviews and the mail questionnaires were merged and weighted in order to represent the correct population distribution of counties in the region. Objective Information from Secondary Sources Several sources were used to measure objective community and environmental conditions associated with the places where the respondents lived. The address of each survey respondent was geocoded using a geographic information system (GIS). In addition to being associated with one of the seven counties, each respondent lived in a particular community (that is, a city, village or township). They were also assigned to a school district and a census unit (block, block group or census tract). Accordingly, contextual measures related to communities, neighbourhoods and census units were made and matched with the survey respondents and their answers to questions. The creation of separate data files covering survey data, community data, environmental data and census data and their consolidation allowed researchers to explore numerous relationships suggested by conceptual models similar to those described in the chapters in Part II of this Handbook. Among the particular community or Minor Civil Division (MCD) measures incorporated in the database were: ● ● ● ● ● ●

tax rates; building permits; other indicators of growth; crime statistics; health data; and school data such as expenditures per student and test scores associated with school districts.

The environmental data file included: ● land-use information (for example, the percentage in each land-use category, degree of mix, percentage of open space and natural resources, etc.); ● accessibility measures to recreational land; ● major employment centres such as shopping areas; and ● various density measures using census data – covering the number of housing units and the size of the population – for blocks, block groups and tracts.

152  Handbook of quality of life research The census data file used 2000 US Census statistics to calculate racial mix, poverty rates, housing tenure and median income for each block, block group and tract associated with respondents. Analysis, Feedback and Ongoing Work Examining interrelationships The files containing census data, environmental data and community data associated with each survey respondent were merged with the survey data file, as shown in Figure 10.1, enabling relationships between contextual data and questionnaire responses to be investigated using bivariate and multivariate analysis. For example, an analysis might address the question of how density (as reflected by multiple density measures) influences people’s responses to crowding, their knowing the names of neighbours, and their interactions with them. Another question might explore the degree to which objective data covering school districts – that is, student–teacher ratios, test scores, expenditures per student – are associated with people’s ratings of their public schools. Using multivariate analysis, an examination could be made of the significance and relative importance of several measures covering school districts in predicting ratings scores for respondents with varying numbers of school-age children living at home.

Source: Marans and Kweon (2011).

Figure 10.1

Merged survey data and objective datasets

Quality of life in large-scale, big-city urban environments: a world perspective  153 Overview of findings The DAS 2001 study addressed a range of topics that reflected the informational needs of study sponsors and key stakeholders as well as the interests of the research team. Many of the topics have been addressed in student papers, technical reports and conference presentations over the past two decades. Data covering other topics are waiting to be analysed by other scholars. Key findings and those related to place are reported in Marans and Kweon (2011) and deal with overall quality of urban life, neighbourhoods and neighbouring, transportation, and prospects for the region’s future. Among highlights in the findings, QOL in the region was viewed positively at the beginning of the twenty-first century. However, Detroit residents were significantly less satisfied with their lives and with its specific dimensions than were those residents living in other parts of the region. For Detroiters, satisfaction with housing was important in understanding QOL, while place components (dwelling, neighbourhood, community) were insignificant in predicting QOL among those living outside the city (see Marans and Kweon, 2011, p. 175). Further analyses showed that life satisfaction was relatively high in the region’s newer suburbs and in small town and rural areas, while it was relatively low in Detroit and in other large and medium-sized cities. Similarly, people’s assessments of the neighbourhoods, dwellings and local services such as parks, transportation and schools varied across the region depending on the type of place where they lived (see Kweon and Marans, 2011).

THE BRISBANE-SOUTH EAST QUEENSLAND REGION Research investigating QOL in the low-density, sprawling, rapidly growing sun-belt metropolis of the Brisbane-South East Queensland region in Australia was undertaken by a group of researchers at The University of Queensland in the late 1990s and again in the mid-2000s. At the time, the region had a population of approaching 2.5 million by 2003. The focus was on measuring the subjective assessment of overall QOL and of QOL domains of the region’s population using survey research designed to enable comparisons with the QOL study undertaken in the Detroit region discussed above. The research investigated relationships between people’s subjective evaluation of QOL and objective social and environmental aspects of both the neighbourhood and the region. The study findings are discussed in detail elsewhere (McCrea et al., 2005, 2006; Stimson et al., 2011). Methodology The Brisbane-South East Queensland QOL research involved a longitudinal design with surveys conducted in 1997 and 2003 using basically the same survey questionnaire investigating subjective QOL. A spatially stratified probability sample design was used to generate a random sample of households across the region, ensuring a minimum sample size of 100 within all ten sub-regions into which the region was divided. The surveys were undertaken using a computer-assisted telephone interviewing (CATI) facility in the Institute for Social Science Research (ISSR) at The University of Queensland. A random selection procedure was used to choose the person aged 18 years and over to be interviewed via telephone. The total size of the sample was a little over 1300 in 1997 and 1600 in 2003.

154  Handbook of quality of life research The research design enabled issues relating to subjective QOL to be investigated and to identify any changes occurring over time and to investigate variations in subjective QOL both across the region and between ten sub-regions, and between demographic and socio-economic groups. In addition, datasets were compiled using objective measures of QOL to enable an integrated investigation of the subjective QOL and objective aspects that might influence QOL. Issues Investigated The surveys collected data on: ● people’s subjective assessment/evaluation (using a five-point Likert scale) of: ● their assessment of QOL in general; and ● with respect to a series of QOL domains; and ● people’s assessment of QOL at different levels of scale – namely, the dwelling, the local area/neighbourhood, and the region as a whole. The QOL domains evaluated covered levels of satisfaction with domains such as: ● ● ● ● ● ●

personal relationships; health; independence; employment/work; money available; and recreational pursuits.

In addition, levels of satisfaction were investigated with respect to: ● economic and social issues; ● a range of environmental attributes such as climate and lifestyle; and ● the provision of and access to a range of services. As discussed by Stimson et al. (2011, p. 185): the data generated from the two QOL surveys provided significant insights into what people like and dislike about living in the SEQ [South East Queensland] region, and on a large number of issues relating to urban QOL focusing both on the SEQ region as a whole and on the local area or neighbourhood where people live.

This included information on things such as: ● the reasons people chose to live in their local neighbourhood and what they liked and disliked about it; ● people’s levels of satisfaction with their work; ● the nature of people’s work and their journey to and from work; ● people’s patterns of consumption and their use of local and regional services and facilities; and ● what people think are key planning issues for governments to address in making the region a better place to live.

Quality of life in large-scale, big-city urban environments: a world perspective  155 Overview of Findings Overall life satisfaction The level of overall satisfaction with life was very high, with 89 per cent expressing ‘satisfaction’ in 2003. This was marginally lower than in 1997 when 90 per cent of the sample expressed overall satisfaction with life as a whole. Those findings were consistent with those reported by Cummins (1996) in his earlier research into subjective QOL in Australia. Overall satisfaction with living in the region was a little higher at 91 per cent in 2003. Thus, overall, across time, the residents of the Brisbane-South East Queensland metropolitan region had very high levels of satisfaction with their life as a whole and also with living in the region. There was, however, some variation across the sub-region in the subjective assessment of people’s overall life satisfaction. Attributes that may impact people’s QOL The study identified variations in how people rated a range of attributes of the region that might impact their QOL. The climate, lifestyle, general services and facilities, the natural environment, and educational services were the top five rated attributes. Again, there were significant variations in the attribute ratings across the sub-regions of the metropolitan region. From a policy perspective the analysis was useful in identifying those attributes that people rated poorly – such as transport, economic conditions and health services – all issues that policy and planning might address, especially focused on the local level. Data collected in the QOL survey also identified a wide range of explicit issues that people saw as being a problem. The longitudinal aspect of the Brisbane-South East Queensland QOL study was especially useful in identifying stability and change over time in the assessment of QOL domains and the attributes that impact people’s QOL, both for the region as a whole and for its sub-regions.

ISTANBUL METROPOLITAN AREA In 2005, the Istanbul Metropolitan Planning and Urban Design Center (IMP) – a public– private partnership established by Istanbul’s mayor – began the process of developing a 2020 strategic plan for the city. IMP consisted of teams responsible for different components of the plan, including demographic projections, the natural environment, housing, transportation, commerce, industry, culture and QOL. The metropolitan area covered the city of Istanbul and included the Anatolian and European sides of the Bosphorus to the coasts of the Sea of Marmara and the Black Sea. This vast area contained a population of over 12 million people in 2006 (Türkoğlu et al., 2011). Methodology As part of the IMP effort, several researchers from the Istanbul Technical University (ITU) were asked to address issues of housing and QOL. The ITU researchers conducted two related surveys: one dealing with the physical conditions in housing and neighbourhoods; and the other issues addressing QOL as viewed by residents throughout the metropolitan area. For the survey of housing, the city’s neighbourhood administrative units were grouped using recorded

156  Handbook of quality of life research land values and residential densities, where land value was assumed to reflect neighbourhood attractiveness, and density represented its physical character. Using a combination of land value and density, nine types of neighbourhoods were identified and mapped for the region. Those ranged from low-density, low-land-value neighbourhoods to high-density, high-land-value neighbourhoods. A database was subsequently created covering all neighbourhoods with their residential structures and the number of dwelling units within each structure. Within each type of neighbourhood, a sample of 100 residential buildings was selected and a detailed assessment of the physical condition of each and its surroundings was made. In addition, samples of residential buildings and dwellings within those buildings were selected where household surveys would be conducted. Face-to-face interviews were subsequently conducted in 1635 dwellings in 423 buildings. In addition to responses to questions about various aspects of people’s QOL, interviewers collected information about the immediate environment and about the building in which the respondent’s dwelling was located. Those measures of environmental conditions based on interviewer observations complemented building and neighbourhood data collected as part of the survey of physical conditions. Issues Investigated Drawing from a series of focus groups and discussions with other IMP teams, the QOL survey identified the key indicators that would be addressed in the household survey. This included: ● the reasons why people chose to live in their dwelling and neighbourhood and what they liked and disliked about them; ● people’s assessment and use of public and private services, including transportation, maintenance and upkeep, schools, parks and recreation facilities, public safety, shopping and healthcare; ● the nature of people’s work and their journey to and from work; ● people’s patterns of consumption and their use of local and regional level services and facilities; ● people’s levels of satisfaction with their dwelling, residential building and neighbourhood; ● people’s involvement in the community and the extent of their neighbouring; and ● people’s level of satisfaction with their life as a whole and its domains.6 Overview of Findings Residents throughout Istanbul were fairly satisfied with the overall QOL. Factors influencing that level of satisfaction included people’s friends and family life, leisure and health. Dimensions of place (home, neighbourhood and community) were also significant in understanding how people felt about their life as a whole, being of lesser importance than the non-place domains of life. Those findings were consistent with those reported in the Detroit regional study discussed earlier in this chapter. Other significant findings dealing with the neighbourhood and neighbouring, transportation and prospects for the future of the region are reported in Türkoğlu et al. (2011). For example,

Quality of life in large-scale, big-city urban environments: a world perspective  157 the researchers found that the most problematic aspects of neighbourhood life were noise levels, feelings of overcrowding, heavy traffic and poor upkeep of outdoor areas (p. 222).

HONG KONG Few QOL studies have explicitly focused on a Chinese population or a Chinese urban context (Shek, 2010). This represents a significant gap in QOL research, particularly given the huge Chinese population and the mega-scale of cities in Asia with their rapid rate of urbanisation. That gap is partially filled by the Hong Kong Quality of Urban Life (QOUL) project, which began in 2014 and that sought to: ● develop objective indicators of QOL (in a social and built environment context); ● measure the subjective assessments/evaluations of QOL domains across different levels of scale; and ● take an approach that integrates subjective and objective measures of QOL. The project was undertaken in two phases. Phase 1 focused on the development of a set of indicators measuring objective QOL derived from secondary data analysis compiled for residential neighbourhoods across Hong Kong and using data from the census, along with GIS-based environmental and urban facilities data. A typology of residential areas based on a set of socio-economic and environmental objective measures of QOL was thus derived. Phase 2 involved a survey to investigate the subjective assessment of individuals’ overall QOL and of a set of QOL life domains, and their levels of satisfaction on the three levels of living domains: housing, neighbourhood and Hong Kong as a whole. Detailed accounts of the study methodology and findings are reported in Lowe et al. (2017) and Stimson et al. (2017). The Hong Kong QOUL survey questionnaire was designed to replicate many of the questions used in the QOL surveys that were undertaken in the Detroit and Brisbane-South East Queensland regions discussed earlier in this chapter, thus enabling comparative analysis of similarities and differences in subjective QOL in the three metropolitan regions. Methodology The QOUL survey was administered by the Public Opinion Programme at the University of Hong Kong using a survey frame designed to generate a random sample of households across Hong Kong that had been used as a panel of persons who regularly participate in social surveys. It was a random sample of some 2472 households. This sampling frame had a standard error of 1.6 per cent, with a sample error of ± 3.1 per cent at the 95 per cent confidence level. The survey focused on persons aged 18 years and over. The agreement of respondents was sought to provide their residential address so that their residential location could be geocoded to which objective social and environmental data from other sources could be linked. This resulted in a useable sample of 1169 respondents. The survey questionnaire was designed so that respondents were asked to evaluate or assess various aspects of their lives using a five-point Likert scale. They were: (1) widely used QOL domains relating to their life in general; and (2) people’s level of satisfaction with the three levels of QOL living domains mentioned above. Respondents were also asked to rate the importance of a set of factors that may have influenced their decision to choose their current

158  Handbook of quality of life research residential location, and to identify the top areas in Hong Kong that they considered had the best QOL. The survey collected information that enabled the researchers to derive measures of anomie and social capital (see Western et al., 2007). Finally, the survey instrument collected information on the usual demographic and socio-economic attributes of the survey respondents. The study also generated a range of objective measures relating to the situational context of the survey respondents to be used to investigate the interrelationships between subjective evaluations of QOL domains and objective situational or environmental attributes. The full list of variables – both subjective and objective – used in the study is shown in Table 10.2. Data Analysis The data generated was analysed using descriptive statistics and one-way ANOVA to compare respondent within-group and between-group variations. Multivariate analysis was then used to investigate the degree to which variations in: (1) people’s assessment of their overall QOL; and (2) people’s levels of satisfaction might be explained by 11 QOL life domains. It also enabled examination of the degree to which variations in individual levels of satisfaction with the three levels of QOL living domains – namely, people’s housing, their neighbourhood and Hong Kong as a whole – and how that might be explained by specific urban attributes relating to life in those living domains. Principal components analysis (PCA) was used to convert a set of observations on possibly correlated variables to sets of values of linearly uncorrelated variables (principal components) in which successive components extracted account for decreasing amounts of the total variance in the data. The PCA thus established relationships among the urban attribute variables and their contribution to each of the three levels of QOL living domains. Multiple regression analysis was then used to estimate the relationships among variables where the focus was on the dependent (outcome) variable and one or more independent (predictive) variables. The ordinary least square (OLS) regression was adopted to determine the degree of power of specific urban attributes as independent variables that account for the variance in people’s level of satisfaction with the three levels of QOL living domains. Overview of Findings Satisfaction with overall QOL There was some ambivalence regarding respondents’ ratings of their overall QOL, with 41 per cent saying they are ‘neither satisfied nor dissatisfied’, and only 43 per cent being positive in their assessment of their overall QOL, but only were 2 per cent ‘very satisfied’. A minority 16 per cent were negative about their overall QOL. The mean score on this general measure of Hong Kong people’s subjective satisfaction with their overall QOL was 3.29. Thus, subjective assessment of QOL in Hong Kong was much lower than that found for the Brisbane-South East Queensland study, while for the Detroit region study, subjective QOL was in between.

Quality of life in large-scale, big-city urban environments: a world perspective  159 Table 10.2

Subjective and objective measures used in the Hong Kong QOUL survey, 2015

Source: Stimson et al. (2017).

Satisfaction with aspects of QOL People’s levels of satisfaction with 11 QOL life domains in Hong Kong showed that: ● People in Hong Kong rated most positively those aspects of their life relating to domains of life linked to: ● relationships with family;

160  Handbook of quality of life research ● independence or freedom; ● social relationships; ● health status; and ● the amount of free time. ● People rated most negatively domain aspects relating to: ● housing situation; ● leisure activities; ● overall standard of living; ● the amount of money available personally; ● financial situation; and ● employment. Some of those findings differed markedly from those in the Brisbane-South East Queensland study where ratings on attributes of life tended to be more positive. The metropolitan Detroit study tested a more restricted set of QOL life domain, with those rated most positively being for ‘friends’, ‘family’ and ‘standard of living’, with lower levels of satisfaction with ‘health’, ‘leisure time’ and ‘employment’, while the domain with the lowest levels of satisfaction was ‘time to do things you want to do’. In the Hong Kong survey, respondents nominated their ‘health status’ as the single most important factor contributing to their QOL, followed a long way behind by their ‘financial status’, then their ‘living environment’ and their ‘housing’. Other factors were hardly mentioned. Levels of satisfaction with living domains Only 51 per cent of people in Hong Kong were positive about their current housing. That level of satisfaction with housing was considerably lower than was found in the Brisbane-South East Queensland study, while the findings from the Detroit study showed relatively high scores on the current housing scale, but the incidence of positive satisfaction was lower than for Brisbane, placing the greater Detroit region somewhat closer to Hong Kong. People in Hong Kong were somewhat more positive about living in their neighbourhood, with 51 per cent being ‘satisfied’. These levels of satisfaction with neighbourhood in Hong Kong were considerably lower than found in the Brisbane-South East Queensland study and also in the Detroit study. People in Hong Kong were very ambivalent about the overall QOL in Hong Kong as a whole, with 44 per cent being ‘neither satisfied nor dissatisfied’, and only 25 per cent being positive. Those findings contrasted markedly with the Brisbane-South East Queensland study, where 91 per cent of people were positive about living in the region as a whole. In the Detroit study, people were also largely positive about living in their region. Multivariate analysis of satisfaction with overall QOL and levels of living There were significant differences between demographic and socio-economic groups in their assessment of levels of satisfaction with overall QOL and with levels of living domains using mean scores on the Likert scale. For example: ● health status and size of dwelling accounted for significant within-group variation; ● household income and age were also significant;

Quality of life in large-scale, big-city urban environments: a world perspective  161 ● type of dwelling, engagement with work and marital status seemed to account for differences; ● housing tenure and housing costs could be important factors; ● type of occupation and level of education had some significance in accounting for between-group differences; ● place of birth and number of people in the household did not seem to be significant factors; and ● gender did not play a role. The effects of sets of urban attributes on explaining levels of satisfaction with levels of living domains The regression modelling undertaken to investigate the effect of sets of urban attributes in explaining variations in the levels of satisfaction of Hong Kong people with respect to the three levels of QOL living domains indicated the following: ● level of satisfaction with overall QOL was significantly affected by their assessments of their ‘overall standard of living’, their ‘housing situation’, their ‘financial situation’ and ‘employment status’, and by their ‘independence or freedom’; ● level of satisfaction with housing was significantly affected by their level of satisfaction with the ‘overall comfort level’ of housing, how their ‘current housing situation meets the family needs’, the ‘affordability/cost’ of housing and the ‘adequacy of rooms for the family’; ● level of satisfaction with the neighbourhood was significantly affected by urban attributes relating to ‘safety walking after dark’, ‘home safety (such as breaking and entering)’, the ‘willingness of people to help each other’, the ‘convenience to walk to stores, parks and other amenities’ and the ‘performance of local councillors in terms of hearing voices of residents’; and ● level of satisfaction with Hong Kong as a whole was significantly affected by ‘economic conditions’, the ‘cultural environment’, ‘climate’, ‘air quality’, ‘noise pollution’, the ‘natural environment’, ‘transportation’, the ‘provision of educational facilities’, the ‘provision of health services’, ‘services and facilities (retail and entertainment)’ and ‘social conditions’ in Hong Kong. Much further analysis could be undertaken to explore these relationships more thoroughly, in particular to model the links in the relationships between people’s subjective assessment of their overall QOL and their levels of satisfaction with the levels of QOL living domains and the explanatory roles of both those subjective assessments and the moderating effects of sets of objective attributes of the urban environment and of the personal characteristics of the survey respondents. In addition, variation between subjective assessment of the overall QOL against objective measurements of the living environment might be investigated, particularly in reference to urban morphology, air quality, service provision and convenience at three spatial levels of housing, neighbourhood and Hong Kong as a whole.

162  Handbook of quality of life research

CONCLUSION Explicit in each of the four city case studies discussed in this chapter was the development of QOL indicators that could be replicated over time in order to assess social and environmental change in each urban setting. With the exception of Brisbane-South East Queensland and Istanbul where two QOL surveys were conducted, the expectation of follow-up QOL research in the metropolitan areas of Detroit and Hong Kong has yet to be realised. As noted, the Brisbane-South East Queensland QOL research involved a longitudinal design, with the surveys conducted in 1997 and 2003 using the same survey questionnaire investigating subjective QOL. The 2003 study was especially useful in identifying stability and change over time in people’s assessment of QOL domains, including those related to place. In metropolitan Istanbul, researchers at ITU conducted a follow-up QOL survey in 2013 using many of the same questions used in their 2006 study. Overall, many of the indicators of QOL, including those dealing with the home environment, showed a decline over the seven-year period. The research in Istanbul was widely publicised and it has prompted research on QOL in other Turkish cities, including Bursa and Ismir (Türkoğlu, personal communication, 30 January 2023). It is unclear whether a third effort to measure QOL in Istanbul will take place at some future date. In the Detroit region, several factors impeded plans for more QOL research. One was the decision by the U-M to terminate DAS, a convenient and relatively inexpensive vehicle enabling such large-scale research to take place. Another factor was an economic recession in the region later in the decade when another QOL survey was planned. At that time, local governmental agencies and foundations were unwilling to invest in research where its immediate value in informing public policy was questionable. Nonetheless, there was potential for measuring QOL in the Detroit region a decade later. In 2014, a new U-M program using survey research emerged as a way of understanding Detroit and its environs. However, unlike DAS, the Detroit Metro Area Communities Study (DMACS) was not intended to be a vehicle for training students in the processes of survey research. Rather, DMACS wanted to engage local stakeholders (not students) in all phases of the survey process. That is, both the research sponsors and the people under study would be involved from the early stages of research design through to the completion of the study.7 Furthermore, DMACS surveys conducted since 2014 focused solely on Detroit residents and issues affecting them. There was little interest among potential funders in the entire metropolitan area. In large part, this focus in Detroit was the result of the city’s declaration of bankruptcy in 2013. Since that time, there has been little interest nor funding opportunities to support QOL research for residents living outside Detroit. It is unclear whether this situation will change as Detroit continues its economic recovery and improves its image. The expectation is that future research would show little difference in the quality of life between Detroiters and residence in its surroundings. To date, in Hong Kong, follow-up work to investigate QOL has not eventuated. The research team – primarily from the University of Hong Kong – that carried out the 2014 study with the support of a foundation grant has disbanded. Based on communications with the principals of the work undertaken some years ago, there is currently little interest among researchers or foundations in initiating and supporting further QOL work focusing on place.

Quality of life in large-scale, big-city urban environments: a world perspective  163

NOTES 1. As it was initially conceived, DAS was intended to train graduate students from the social sciences and professional schools in quantitative social science techniques and provide a facility for faculty to engage in empirical investigations. For a discussion of the DAS and its history, see Couper et al. (2002) and Freedman (1953). 2. An early regional survey of the seven-county area dealing with issues of place was conducted in 1967 by TALUS, the Detroit Regional Transportation and Land Use Study (Lansing and Hendricks, 1967). TALUS was an intergovernmental regional planning agency established in 1965 and eventually morphed into the Southeast Michigan Council of Governments (SEMCOG). 3. Initially, data from 1967 and 1975 metropolitan area surveys were to be compared with the 2001 findings. This idea was abandoned when it was recognised that new informational requirements for the 2001 questionnaire would not replicate questions asked in the earlier surveys. 4. The quality of community life was subsequently changed to quality of urban life, in large part because of the different meaning of community in other world cities where the research was being replicated. 5. For a discussion of the approach to incentives and its impact on selected responses, see Ryu et al. (2006). 6. For a complete list of all indicators used in the metropolitan Istanbul survey, see Tables 9.1 and 9.2 in Türkoğlu et al. (2011). 7. See Marans et al. (2021) for a discussion of participant involvement.

REFERENCES Bauer, R. (ed.) (1966), Social Indicators, Cambridge, MA: MIT Press. Boyer, R. and Savageau, D. (1981), Places Rated Almanac: Your Guide to Finding the Best Places to Live in America, Chicago, IL: Rand McNally & Co. Campbell, A., Converse, P.E. and Rogers, W.L. (1976), The Quality of American Life: Perceptions, Evaluations and Satisfactions, New York: Russell Sage Foundation. Couper, M.O., Clemens, J. and Powers, K. (2002), Detroit Area Study 1952–2001: Celebrating 50 Years, Ann Arbor, MI: University of Michigan. Cummins, R.A. (1996), ‘The domain of life satisfaction: an attempt to order chaos’, Social Indicators Research, 18, 303–32. Ewing, R., Pendall, R. and Chen, D. (2002), Measuring Urban Sprawl and Its Impact, Washington, DC: Smart Growth America. Freedman, R. (1953), ‘The Detroit Area Study: a training and research laboratory in the community’, The American Journal of Sociology, 19, 30–33. Fuller, A. (2018), America’s 100 Best Places to Retire, 6th edition, Houston, TX: Vacation Publications, Inc. Gross, B.M. (ed.) (1967), The Annals of the American Academy of Political and Social Science: Social Intelligence for America’s Future: Exploration in Societal Problems (I and II, Vols 371 and 373), Philadelphia, PA: American Academy of Political and Social Sciences. Hagerty, M.R., Cummins, R.A. and Ferriss, A.L. et al. (2001), ‘Quality of life indexes for national policy: review and agenda for research’, Bulletin of Sociological Methodology, 7, 58–78. Hall, J.F. (1976), ‘Subjective measures of quality of life in Britain 1971 to 1975: some developments and trends’, in E. Thompson (ed.), Social Trends No. 7, London: HMSO. Innes de Neufville, J. (1975), Social Indicators and Public Policy: Interactive Processes of Design and Application, Amsterdam: Elsevier. Kweon, B.-S. and Marans, R.W. (2011), ‘Disaggregating the measure of quality of urban life dimensions across a complex metro region: the case of metro Detroit’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 369–84.

164  Handbook of quality of life research Lansing, J.B. and Hendricks, G. (1967), Living Patterns and Attitudes in the Detroit Region: A Report of TALUS, the Detroit Regional Transportation and Land Use Study, Detroit, MI: Detroit Metropolitan Area Regional Planning Commission. Lowe, C.T., Stimson, R.J. and Chan, S. et al. (2017), ‘Personal and neighbourhood indicators of quality of urban life: a case study of Hong Kong’, Social Indicators Research, 136, 751–73. Marans, R.W., Gerber, E. and Morenoff, J. (2021), ‘Detroit Area Studies (DAS)’, in F. Maggino (ed.), Encyclopedia of Quality of Life and Well-Being Research, 2nd edition, Cham: Springer, pp. 1–6. Marans, R. and Kweon, B.-S. (2011), ‘The quality of life in metro Detroit at the beginning of the millennium’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 163–84. Marans, R.W. and Stimson, R.J. (eds) (2011), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer. McCrea, R., Shyy, T.-K. and Stimson, R.J. (2006), ‘What is the strength of the link between objective and subjective indicators of quality of life?’, Applied Research in Quality of Life, 1, 77–96. McCrea, R., Stimson, R.J. and Western, J. (2005), ‘Testing a moderated model of satisfaction with urban living using data for Brisbane-South East Queensland, Australia’, Social Indicators Research, 72, 121–51. Rodgers, W.L. and Marans, R.W. (1975), Quality of Life in the Detroit Metropolitan Area, 1975, Ann Arbor, MI: Inter-university Consortium for Political and Social Research (distributor). Ryu, E., Couper, M. and Marans, R.W. (2006), ‘Survey incentives: cash versus in-kind; face-to-face versus mail; response rate versus response bias’, International Journal of Public Opinion Research, 18, 89–106. Sassen, S. (2001), The Global City: New York, London, Tokyo, 2nd edition, Princeton, NJ: Princeton University Press. Savageau, D. (2007), Places Rated Almanac, 7th edition, Washington DC: Places Rated Books. Scharf, S. (2021, 14 September), ‘How Money chose the best places to live in 2021’, Money, https://​ money​.com/​best​-places​-to​-live​-methodology​-2021/​ (accessed 25 October 2023). Shek, D.T.L. (2010), ‘Introduction: quality of life of Chinese people in a changing world’, Social Indicators Research, 95, 357–61. Sheldon, E.B. and Moore, W.E. (1968), Indicators of Social Change: Concepts and Measurements, New York: Russell Sage Foundation. Smith, D.M. (1973), The Geography of Social Well-Being in the United States: An Introduction to Territorial Social Indicators, New York: McGraw Hill. Sperling, B. and P. Sander (2006), Best Places to Raise Your Family: The Top 100 Affordable Communities in the US, Hoboken, NJ: Wiley Publishing. Stimson, R.J., Low, C.T. and Chen, S. et al. (2017), ‘The subjective assessment of quality of urban life in Hong Kong’, paper presented at the Western Regional Science Association Annual Conference, Santa Fe, February. Stimson, R.J., McCrea, R. and Western, J. (2011), ‘The Brisbane-South East Queensland region, Australia: subjective assessment of quality of urban life and changes over time’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 185–207. Türkoğlu, H., Bölen, F., Korça Baran, P. and Terzi, F. (2011), ‘Measuring quality of life in Istanbul’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 209–31. United Nations, Department of Economic and Social Affairs, Population Division (2019), World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420), New York: United Nations, https://​population​.un​.org/​wup/​publications/​Files/​WUP2018​-Report​.pdf (accessed 26 April 2023). US Census (2022, 26 May), ‘Fastest-growing cities are still in the West and South’, US Census Bureau, https://​www​.census​.gov/​newsroom/​press​-releases/​2022/​fastest​-growing​-cities​-population​-estimates​ .html (accessed 26 April 2023). Western, J., McCrea, R. and Stimson, R.J. (2007), ‘Quality of life and social inclusion’, International Review of Sociology, 17, 525–37.

11. Pathways from compact city to subjective well-being: evidence from Oslo, Norway Kostas Mouratidis

INTRODUCTION With the rapid urbanisation of the world’s population, enhancing the quality of life (QOL) of people in cities is a critical issue for city planning. The compact city model – a city of short distances, high densities, mixed land uses, efficient public transport services and good access to local facilities (Jenks et al., 2003; Neuman, 2005) – has been employed in numerous cities across the world. This is mainly due to the supposed environmental and economic benefits, including lower transport-generated greenhouse gas emissions, potential for preservation of natural environment, lower pollution, improved productivity, more efficient services, better access to jobs and higher potential for innovation (Ahlfeldt et al., 2018; Meyer, 2013; Organisation for Economic Co-operation and Development [OECD], 2018; United Nations, 2012). Despite environmental and economic benefits, the impacts of compact cities on residents’ QOL have been less clear as research findings appear to be conflicting, complex and context dependent. Understanding how planning compact cities may contribute to QOL is important for present and future urban development around the globe. Knowledge of the pathways between cities and QOL has been expanding in recent years (for example, Marans and Stimson, 2011; Mouratidis, 2018d, 2021; Pfeiffer and Cloutier, 2016; Shekhar et al., 2019). Nevertheless, there is a need to better understand the complex pathways between compact cities and QOL (Kyttä et al., 2016). In particular, in-depth knowledge of compact cities and subjective well-being – the personal evaluation of QOL (Diener et al., 2018) – is lacking. The chapter addresses this need by presenting new empirical evidence of the pathways between the compact city and subjective well-being (SWB). In doing so, two research questions are addressed: (1) How does urban compactness relate to satisfaction with life domains? (2) How does urban compactness relate to SWB? The Oslo, Norway, city case study is presented in the chapter. It expands on previous research that examined four life domains as mediators of QOL using regression analysis (Mouratidis, 2019b). Additional mediators are examined based on a recently developed model (Mouratidis, 2020a) using structural equation modelling, which is better suited for testing models with multiple hypothesised mediating pathways. Knowledge gained from the analysis can be used as theoretical and methodological guidance for further empirical research on cities and SWB. The findings have implications for city planning strategies aimed at improving QOL in compact cities.

165

166  Handbook of quality of life research

THE COMPACT CITY AND SUBJECTIVE WELL-BEING Subjective well-being is the subjective measurement of QOL (Diener, 2009; Diener et al., 2018). Improving SWB is an important goal of public policy (OECD, 2013; Stiglitz et al., 2009; Veenhoven, 2004) and one of the main pillars of social sustainability (Cloutier and Pfeiffer, 2015; Rogers et al., 2012). It can be conceptualised based on three components (OECD, 2013; Sirgy, 2012): ● life satisfaction (that is, contentment with life overall); ● emotional well-being (also called affect or hedonic well-being); and ● eudaimonia (that is, self-actualisation and meaning in life). Studies examining the relationship between the compact city and SWB report differing results. Compact urban development offers access to places and people, while low-density development offers quietness, stronger connection with nature and greater neighbourhood social cohesion (Mouratidis, 2018c). Negative associations between population density and life satisfaction have been reported in studies from Norway (Cramer et al., 2004) and the US (Cao, 2016). A study from Finland found higher evaluations of QOL in central, pedestrian neighbourhoods and higher happiness than in car-oriented neighbourhoods (Ala-Mantila et al., 2018). A study from China and a more recent study from Norway found that SWB is similar in compact and low-density neighbourhoods (Feng et al., 2018; Mouratidis, 2019b), except for anxiety, which was found to be slightly higher in inner-city, dense neighbourhoods of Oslo. Overall, evidence on compactness and SWB remains inconclusive. To fully understand the possible influence of the compact city on SWB, we need to understand the pathways between them. The relationships between cities and quality of life are complex. Therefore, empirical studies need to examine the mediating factors between them based on theoretical and methodological considerations (Kyttä et al., 2016; Marans, 2003; Mouratidis, 2018d, 2021). Potential pathways linking cities to SWB (Mouratidis, 2021) may include the following life domains: ● ● ● ● ● ● ●

travel; leisure; work; social relationships; residential well-being; emotional responses; and health.

All these life domains (that is, domain satisfaction) may contribute to SWB (Diener et al., 2018; Sirgy, 2012), as depicted in Figure 11.1. Further, those life domains could be influenced by urban compactness in distinct ways, and this is why they may represent unique pathways between the compact city and SWB (Marans, 2003; Mouratidis, 2020a). An overview of how the compact city relates to the above domain satisfactions as follows: 1. Travel satisfaction has been found to be higher in compact urban forms as compactness may reduce travel times and enable active travel modes that are more satisfying (Mouratidis et al., 2019). By reducing the distance to destinations, the compact city can be conducive to shorter trip durations, which have positive well-being implications (Chatterjee et al., 2020;

Pathways from compact city to subjective well-being: evidence from Oslo, Norway  167

Source: Adapted from Mouratidis (2020a).

Figure 11.1

2.

3.

4.

5.

The link from urban compactness to subjective well-being via domain satisfactions

Morris and Guerra, 2015). Compact city attributes – including shorter distances, higher densities, land-use mix, local facilities, walkability and efficient public transport services – promote the use of active travel modes such as walking and cycling (de Nazelle et al., 2011; Durand et al., 2011; Næss et al., 2019; Sallis et al., 2016). Leisure satisfaction can be influenced by compactness in indirect ways (Mouratidis, 2019a). Compact urban form is characterised by less green space and lower access to nature compared with low-density development and may negatively contribute to leisure satisfaction. On the other hand, compact urban form is characterised by access to a number of local facilities and services, shorter travel times and increased social interaction, all of which may positively contribute to leisure satisfaction. Job satisfaction has not been sufficiently explored in terms of its relationship with the compact city. The compact city provides job-related benefits including more economic activity and job opportunities (Glaeser, 2011; Glaeser et al., 2001) and shorter commutes, which are positively linked to job satisfaction (Clark et al., 2020; Sun et al., 2021). Social relationships have been examined for their link to the compact city in several ways. One distinction focused on in the literature is social relationships at the local neighbourhood level and social relationships at the overall city level. Social ties with neighbours tend to be less strong in compact neighbourhoods (French et al., 2014; Mazumdar et al., 2018; Mouratidis and Poortinga, 2020). On the other hand, compact city residents may have a larger number of close relationships overall at a city level, a more active social life and increased opportunities to make new social acquaintances compared with residents living in low-density areas (Melis et al., 2015; Mouratidis, 2018a). Residential well-being can be measured by neighbourhood satisfaction and housing satisfaction (but also with city satisfaction for comparisons between cities). Both neighbourhood satisfaction and housing satisfaction largely depend on household needs and preferences. High population density has been linked with lower neighbourhood satisfaction in some studies (Bramley et al., 2009; Cook, 1988). However, recent studies suggest that when common urban problems (for example, noise, inequalities, crime, lack of green space) are relatively limited, and when all the essential compact city characteristics (density, land-use mix, public transport, walkability) are present, the compact city could be

168  Handbook of quality of life research conducive to higher neighbourhood satisfaction than is the case in cities characterised by urban sprawl (Mouratidis, 2018c; Yang, 2008). Housing satisfaction has not been focused on much in terms of its relation to the compact city. Smaller dwellings have been found to be associated with compact city living. Specifically, dwelling size has been found to be a significant positive contributor to housing satisfaction (Wang and Wang, 2016, 2020). Therefore, it might be expected that compact city living will contribute negatively to housing satisfaction. However, a recent study from China found that housing satisfaction (measured as residential satisfaction) did not change after relocation from suburban to urban areas or vice versa (Wang and Wang, 2020). 6. Emotional responses to the compact city seem to be less positive than those to low-density areas. People tend to experience lower momentary happiness in cities than in natural or rural environments (MacKerron and Mourato, 2013) and people who grow up in cities may develop more stress later in life (Lederbogen et al., 2011). Suburban neighbourhoods have been associated with more positive emotional responses compared with denser, inner-city neighbourhoods (Mouratidis, 2019b). These findings altogether suggest a negative link between dense, vibrant urban surroundings and emotional well-being. This could be explained by stressful intense life rhythms, noise, overcrowding, impersonal social interactions in public spaces, fear of crime and loss of connection with nature. 7. Health may be influenced by urban compactness in numerous and diverse ways. Compact cities may positively contribute to health outcomes by enabling physically active travel (for example, walking, biking) and the use of public transport (Stevenson et al., 2016), improving social well-being (Melis et al., 2015; Mouratidis, 2018a) and offering better access to healthcare and increased economic opportunities (Glaeser, 2011; Litman, 2020). On the other hand, compact cities may negatively contribute to health via higher exposure to noise and air pollution, lack of connection with nature and less positive emotional reactions (Litman, 2020; MacKerron and Mourato, 2013; Markevych et al., 2017; Mouratidis, 2019b; Tao et al., 2020). Overall, residents of urban areas have been found to report better health and live longer on average than rural residents (Cosby et al., 2019; Dye, 2008), and compact city residents in the US live longer than residents of sprawling suburbs (Hamidi et al., 2018). Residents of the compact, inner city of Oslo, Norway, reported slightly better overall health than suburbanites (Ihlebæk et al., 2021; Mouratidis, 2019b). The possible links between the built environment and mental health have been investigated by numerous studies but there is still insufficient evidence on causal effects (Núñez-González et al., 2020). Urban life has been linked with increased risk of schizophrenia, stress and anxiety (Gruebner et al., 2017; Lederbogen et al., 2011; Mouratidis, 2019b) but also with reduced risk of depression and dementia (Litman, 2020; Melis et al., 2015). It has been argued that the increased risk for certain mental health problems found in some urban areas could be due to poverty, inequality or better reporting in cities (Gruebner et al., 2017; Litman, 2020).

Pathways from compact city to subjective well-being: evidence from Oslo, Norway  169

THE OSLO CASE STUDY Methodology Data sources Data from a population-based questionnaire survey on QOL as well as geospatial data are used in the Oslo, Norway, case study reported here. The survey was carried out in 2016 in the metropolitan area of Oslo, the capital of Norway. In 2018, the metropolitan area of Oslo had approximately a million and a half inhabitants. Oslo metropolitan area is a suitable case study as it includes neighbourhoods of varying degrees of compactness. Compact neighbourhoods are mostly located around the central business district of Oslo. Those neighbourhoods are characterised by relatively high population densities, apartment blocks, mixed land uses and good access to public transport services. Neighbourhoods that are located further away from the inner city (out of the so-called ‘Ring 3’) are mostly characterised by lower densities, predominantly detached housing and more reliance on private cars. The total sample for the survey is 1339 individuals, aged 19–94 years. Participants were residents of 45 neighbourhoods within the metropolitan area of Oslo. The random sample of households was selected from each neighbourhood. Only one adult member of each household could be selected. The neighbourhoods chosen for the study are located in the inner city, inner suburbs and outer suburbs. A broad range of urban forms are represented in the sample: low-, medium- and high-density urban forms. Neighbourhoods also vary in terms of socio-demographic profile, including both poorer and richer inner-city areas as well as poorer and richer suburbs. More details on the survey, the sample and the neighbourhoods of the study can be found in Mouratidis (2019b, 2020a). Variables Table 11.1 presents the measurement of SWB and domain satisfactions. Subjective well-being and domain satisfactions were measured via the survey based on items developed by the OECD (2013), the European Social Survey (2012) and Mouratidis (2018b). The study uses univariate measures of SWB. These are stable and reliable (Lucas and Brent Donnellan, 2012). Table 11.2 presents descriptive statistics of SWB and domain satisfactions. Individual socio-demographic variables, used in the analysis as control variables, were also captured via the survey and are presented in Table 11.2. Table 11.1 Variables

Measurement of subjective well-being and domain satisfactions Question

Scale

Subjective well-being Life satisfaction Eudaimonia Happiness Anxiety

All things considered, how satisfied are you with your life as a whole ‘Extremely

‘Extremely

nowadays?

satisfied’ (10)

dissatisfied’ (0)

Overall, to what extent do you feel that the things you do in your life ‘Not at all

‘Extremely

are worthwhile?

worthwhile’ (0)

worthwhile’ (10)

Please tell us how much of the time during the past week you felt

‘Very rarely or

‘Very often or

happy

never’ (1)

always’ (5)

Please tell us how much of the time during the past week you felt

‘Very rarely or

‘Very often or

anxious

never’ (1)

always’ (5)

170  Handbook of quality of life research Variables

Question

Scale

How satisfied are you with your dwelling? (Consider only the

‘Very

‘Very satisfied’

interior of your dwelling)

dissatisfied’ (1)

(5)

What are your general feelings about your travel to your main

‘Very negative’

‘Very positive’

occupation? (Consider the time spent and mode(s) of travel)

(1)

(5)

Neighbourhood

How well do you think your local areaa meets your current needs?

‘Extremely

‘Extremely well’

satisfaction

(Consider your local area’s internal – physical and social – and

poorly’ (0)

(10)

Domain satisfactions Housing satisfaction Commute satisfaction

external – accessibility to other areas – characteristics) Neighbourhood

How would you describe your feelings experienced when walking or ‘Very negative’

‘Very positive’

happiness

biking in your local area?a

(1)

(5)

Personal relationships

How satisfied are you with your personal relationships?

‘Extremely

‘Extremely

dissatisfied’ (0)

satisfied’ (10)

How satisfied are you with the time you spend on your favourite

‘Extremely

‘Extremely

leisure activities?

dissatisfied’ (0)

satisfied’ (10)

Health (self-reported)

How would you describe your health in general?

‘Extremely poor’ ‘Extremely

Job satisfaction

How satisfied are you overall with your work/studies?

satisfaction Leisure satisfaction

(0)

good’ (10)

‘Very

‘Very satisfied’

dissatisfied’ (1)

(5)

Note: a Local area was defined in the survey as the area within 15-minute walking distance from the respondent’s dwelling. Sources: The author based on European Social Survey (2012); Mouratidis (2018b); OECD (2013).

Table 11.2 presents descriptive statistics of the variables used to examine urban compactness: distance to city centre and neighbourhood density. These variables were captured with analysis in geographic information systems (GIS) for the residential location of each participant. Table 11.2

Descriptive statistics for all variables

Variables

N

Min/Max

Mean

s.d.

Subjective well-being Life satisfaction

1335

0/10

7.88

(1.71)

Eudaimonia

1324

0/10

7.85

(1.70)

Happiness

1313

1/5

3.67

(0.84)

Anxiety

1319

1/5

2.02

(1.01)

Domain satisfactions Housing satisfaction

1330

1/5

4.27

(0.78)

Commute satisfaction

966

1/5

3.78

(0.90)

Neighbourhood satisfaction

1334

0/10

8.23

(1.83)

Neighbourhood happiness

1317

1/5

4.11

(0.75)

Personal relationships satisfaction

1310

0/10

7.57

(1.91)

Leisure satisfaction

1304

0/10

7.15

(2.10)

Health (self-reported)

1333

0/10

7.72

(1.83)

Job satisfaction

971

1/5

4.10

(0.86)

Urban compactness Distance to city centre (km)

1339

0.7/46.2

10.06

(10.71)

Neighbourhood density (persons/ha within 1 km radius)

1339

1/177

75.27

(54.37)

Socio-demographic variables Age

1339

19/94

50.14

(15.68)

Living with partner/spouse

1324

0/1

0.61

(0.49)

Non-Norwegian

1337

0/1

0.09

(0.28)

Pathways from compact city to subjective well-being: evidence from Oslo, Norway  171 Variables

N

Min/Max

Mean

s.d.

Adjusted household income (1000s NOK)a

1255

35/4330

642.2

(321.08)

Female

1326

0/1

0.54

(0.50)

College degree or higher

1336

0/1

0.79

(0.41)

Household with children

1329

0/1

0.32

(0.47)

Note: a Annual household income divided by the square root of household size. Source: The author.

Distance to city centre assessed the location of each participant’s residence in relation to the city centre. It was calculated in GIS as the distance, in kilometres, from each residence to the city centre based on the pedestrian network. Residential location, measured by distance to city centre, is important for assessing possible impacts of compactness at the urban region scale. Compact cities have higher overall densities and residential locations that are closer to the city centre. Long distances to city centre may reveal implications for suburban living and urban sprawl, while proximity to city centre is related to urban living and the compact city. Examining distance to the city centre within a specific urban region, as done in this study, is also important when examining neighbourhood density. Denser neighbourhoods tend to be located closer to the central business district, especially in monocentric urban regions. Examining neighbourhood density without accounting for distance to the city centre may therefore yield inflated, inaccurate estimates (Næss, 2019). Neighbourhood density was examined in the study as a measure of local neighbourhood compactness. It was measured in GIS as the population density within a 1000 metre (m) radius from each participant’s residence. The population dataset for statistical grids (250 m × 250 m) from Statistics Norway was used for this analysis. Analytical method The main analytical method in the study is structural equation modelling (Byrne, 2016). This method includes path analysis (a structural model) and/or latent variables (a measurement model). Here only path analysis is used since measures of SWB and domain satisfaction are univariate. To investigate the multiple pathways between compactness and SWB, multiple mediator analysis was carried out (Preacher and Hayes, 2008). The responses of the survey are clustered in 45 neighbourhoods. However, no between-cluster variance for the SWB variables of the study was found when socio-demographic variables were accounted for (Mouratidis, 2020b), so a multi-level analysis was not appropriate. Structural equation modelling can process binary and continuous variables. Variables on domain satisfaction and SWB in the study were assessed on 0–10 and 1–5 scales. These variables can be handled as either ordinal or continuous, with little difference in the results between the two approaches (Ferrer-i-Carbonell and Frijters, 2004). It is thus common practice to treat such variables as continuous in structural equation models. Maximum likelihood estimation was used to estimate the coefficients of the structural equation model. Bootstrapping of 1000 replications was employed to estimate significance levels for direct, indirect and total statistical effects. Bootstrapping provides reliable estimates of levels of significance in multiple mediation models and helps address issues of normality in the data (Pek et al., 2018; Preacher and Hayes, 2008). The sample size was reduced in the model results since missing data were deleted as required for bootstrapping. Commute satisfaction and job satisfaction were measured only

172  Handbook of quality of life research for workforce participants and students within the sample, resulting in a substantially reduced sample size (N = 833). The Model The Oslo study explores pathways between urban compactness and SWB based on the model displayed in Figure 11.2. The model examines urban compactness – measured with distance to city centre and neighbourhood density – as exogenous to a previous model on domain satisfactions and SWB developed by Mouratidis (2020a). That model was developed, tested and compared with alternative models. The final model was based on theoretical and methodological considerations and had the best fit for the data.

Source: The author, adapted from Mouratidis (2020a).

Figure 11.2

Model linking urban compactness to domain satisfactions and SWB

In the model the final endogenous variables are the measures of SWB: ● ● ● ●

life satisfaction; happiness; anxiety; and eudaimonia.

As the starting point was to examine compactness, possible predictors of domain satisfactions and SWB considered were distance to city centre and neighbourhood density. Distance to city centre is exogenous to neighbourhood density as explained in the description of the variables above. Socio-demographic variables were included as control variables and were considered exogenous. The variables included in the model were: ● age; ● age squared;

Pathways from compact city to subjective well-being: evidence from Oslo, Norway  173 ● ● ● ● ● ●

gender; cohabitation status; citizenship; level of education; household income; and presence of children in household.

Domain satisfactions were considered as mediators between urban compactness and SWB. The arrangement of domain satisfactions in Figure 11.2 is based on the following theoretical considerations (for more details, see Mouratidis, 2020a). 1. Commute satisfaction is indirectly linked to SWB, mainly through neighbourhood satisfaction, leisure satisfaction and job satisfaction (Clark et al., 2020; De Vos, 2019; De Vos and Witlox, 2017; Ettema et al., 2010; Gao et al., 2017; Mouratidis, 2020a). 2. Neighbourhood satisfaction could be shaped by emotional responses to the neighbourhood one lives in (neighbourhood happiness) (Wang and Wang, 2016). For example, living in an unsafe neighbourhood could induce negative emotions and subsequently lower neighbourhood satisfaction. The neighbourhood one lives in can play a role in life domains, including social relationships and leisure activities (Mazumdar et al., 2018; Mouratidis, 2018a, 2019a). Therefore, neighbourhood satisfaction is indirectly linked to SWB via satisfaction with these life domains. The attributes of the neighbourhood one lives in contribute to neighbourhood satisfaction and this satisfaction may contribute to evaluations of one’s dwelling (Davis and Fine-Davis, 1991). For example, a resident may be dissatisfied living in a very unsafe neighbourhood, and this could make housing conditions appear less satisfactory (Wang and Wang, 2016). 3. Self-reported health is interlinked with commute satisfaction and neighbourhood happiness in the model, and in turn linked to all other domain satisfactions. Health problems might hamper all life domains and domain satisfactions and thus considering self-reported health to precede other domain satisfactions and SWB has been common practice in relevant analyses (Diener, 2009). There could be bi-directional relationships between certain domain satisfactions in the model. These were explored with alternative models in Mouratidis (2020a). An alternative arrangement of domain satisfactions is not expected to substantially influence the conclusions of the present study because the model considers urban compactness to be linked directly to all domain satisfactions and SWB, in addition to indirect links. So, all pathways between urban compactness and SWB through domain satisfactions (those studied here) are captured in the analysis. Results Results of the structural equation model shown in Figure 11.2 are presented in Tables 11.3 and 11.4. The results are presented in two separate tables for reasons of simplicity. Table 11.3 shows the results for domain satisfactions, while Table 11.4 shows results for SWB. The tables present coefficients for exogenous variables that measure compactness – distance to city centre and neighbourhood density – while the coefficients of other predictor variables – socio-demographic variables and domain satisfactions – are not shown to reduce complexity

174  Handbook of quality of life research (see Mouratidis, 2019b, 2020a for these associations). Fit indices of the structural equation model (RMSEA < 0.08; CFI > 0.93) indicate a good fit for the data. Direct, indirect and total effects of compactness on domain satisfactions Table 11.3 presents direct, indirect and total statistical effects of compactness on domain satisfactions. Results show the following: 1. Compactness is positively related to domain satisfactions overall. Residents of neighbourhoods that are closer to the city centre reported higher commute satisfaction, higher neighbourhood satisfaction, higher personal relationships satisfaction and lower neighbourhood happiness (the latter is marginally significant). 2. Neighbourhood density is associated with higher commute satisfaction (marginally significant) and lower neighbourhood happiness. 3. Total statistical effects of distance to city centre and neighbourhood density on self-reported health, housing satisfaction, job satisfaction and leisure satisfaction are non-significant. Direct, indirect and total effects of compactness on subjective well-being Table 11.4 presents direct, indirect and total statistical effects of compactness on SWB. Results on total effects indicate the following: 1. Overall, distance to city centre is not significantly associated with life satisfaction, eudaimonia and happiness. 2. Living closer to the city centre was found to be associated with increased anxiety (marginally significant). 3. Neighbourhood density is not significantly associated with life satisfaction, happiness, anxiety and eudaimonia. Overview of findings The analysis of direct and indirect effects in Table 11.4 suggests that distance to city centre relates to SWB via both direct and indirect pathways. These pathways are contrasting and counterbalance each other. Specifically, distance to city centre is directly linked to higher happiness, higher eudaimonia and lower anxiety, while it is indirectly linked to lower eudaimonia. Indirect associations capture the influence of domain satisfactions presented in Table 11.3. Domain satisfactions are mostly positive for residents living closer to the city centre, therefore proximity to city centre has a positive indirect effect on SWB – (marginally) significant only for eudaimonia. On the other hand, direct effects shown in Table 11.4 suggest that there is a positive link between suburban living and SWB that is not captured by the domain satisfactions included in the model of the study. Urban compactness is therefore partially mediated by the domain satisfactions examined here but there are other pathways that cannot be explained by the model.

CONCLUSION This chapter has provided new evidence on the pathways linking the compact city to SWB. It is one of the first empirical investigations of links between compactness and SWB through multiple dimensions of domain satisfaction. The Oslo case study has thus extended previous

0.116a

Neighbourhood density

 

Neighbourhood density

0.082

–0.191***

0.060a

 

0.139***

–0.191***

–0.079a

0.046

0.021

–0.054

 

–0.015

0.021

–0.039

Health

0.179

0.074

–0.236**

–0.046*

–0.089*

0.119*

–0.147**

Neighbourhood satisfaction

0.188

–0.023

–0.028

–0.002

–0.040

–0.021

0.012

satisfaction

Housing

0.175

–0.038

0.045

0.004

–0.024

–0.043

0.068

satisfaction

Leisure

0.068

0.084

0.048

0.011

0.018

–0.057a

0.030

0.197

–0.019

–0.134**

0.009

–0.044

–0.028

–0.089a

satisfaction

relationships

Job satisfaction Personal

Notes: a p < 0.10; * p < 0.05, ** p < 0.01, ***p < 0.001. All effects are standardised. Number of observations N = 833. Significance levels are calculated with bootstrapping. Bootstrap replications = 1000. Goodness-of-fit measures: χ2/df = 1.501 (p = 0.065); CFI = 0.999; RMSEA = 0.025. Standardised effect of distance to city centre on neighbourhood density = –0.726 (p = 0.003). The model also includes individual socio-demographic characteristics as exogenous variables and domain satisfactions as mediators, as shown in Figure 11.2. Source: The author.

(SMC)

Squared multiple correlation

0.035

0.116a

Neighbourhood density

Summary statistic

–0.152**

Distance to city centre

Total effects

–0.085*

Distance to city centre

Indirect effects

–0.068

Distance to city centre

Direct effects

Neighbourhood happiness

Commute

satisfaction

Domain Satisfactions

 

Structural equation modelling results on how compactness relates to domain satisfactions

 

Table 11.3

Pathways from compact city to subjective well-being: evidence from Oslo, Norway  175

176  Handbook of quality of life research Table 11.4

Structural equation modelling results on how compactness relates to measures of SWB (based on Figure 11.2)

 

Subjective Well-being

 

Life satisfaction

Happiness

Anxiety

Eudaimonia

Distance to city centre

0.060

0.079a

–0.078a

0.073a

Neighbourhood density

–0.039

0.012

0.022

–0.024

Distance to city centre

–0.057

–0.048

0.009

–0.066a

Neighbourhood density

0.012

–0.007

–0.002

0.020

Direct effects

Indirect effects

Total effects Distance to city centre

0.003

0.030

–0.069a

0.008

Neighbourhood density

–0.028

0.006

0.020

–0.004

0.480

0.268

0.165

0.466

Summary statistics Squared multiple correlation (SMC)

Notes: a p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. All effects are standardised. Number of observations N = 833. Significance levels are calculated with bootstrapping. Bootstrap replications = 1000. Goodness-of-fit measures: χ2/df = 1.501 (p = 0.065); CFI = 0.999; RMSEA = 0.025. The model also includes individual socio-demographic characteristics as exogenous variables and domain satisfactions as mediators, as shown in Figure 11.2. Source: The author.

relevant research (Cao, 2016; Feng et al., 2018; Kyttä et al., 2016; Mouratidis, 2019b, 2020b; Okulicz-Kozaryn and Mazelis, 2018). Findings support the suggestion that urban compactness is linked to SWB via pathways represented by life domains. Urban compactness was found to be significantly associated with multiple dimensions of domain satisfaction. The results indicate that urban compactness is generally positively related to domain satisfaction. Inner-city residents had higher commute satisfaction, neighbourhood satisfaction and personal relationships satisfaction, but lower neighbourhood happiness compared with suburban residents. Neighbourhood density was associated with higher commute satisfaction and lower neighbourhood happiness. Distance to city centre and neighbourhood density were not significantly associated with self-reported health, housing satisfaction, job satisfaction and leisure satisfaction. Compactness relates to leisure satisfaction mainly via indirect pathways explored in a previous study (Mouratidis, 2019a). Associations between compactness and both housing and job satisfaction have been underexplored in previous research. The finding that housing satisfaction was similar for inner-city and suburban residents is surprising since dwelling size, a predictor of housing satisfaction (Wang and Wang, 2016), tends to be larger in the suburbs. Job satisfaction was found to be similar for inner-city and suburban residents, although we might have expected that it would be higher for inner-city residents since there are more job opportunities in inner cities (Glaeser, 2011) and commutes are shorter (Mouratidis et al., 2019). Subjective well-being was found to be similar for residents of compact areas and low-density suburbs with the exception of anxiety, which was found to be slightly higher for inner-city residents. Findings indicate that compactness is positively linked to SWB via improved (commute) travel satisfaction, neighbourhood satisfaction and personal relationships satisfaction, while it is negatively linked to SWB via lower neighbourhood happiness. The study’s mediation analysis also suggests that there exist negative pathways from compactness to SWB that were not captured by the structural equation model presented here.

Pathways from compact city to subjective well-being: evidence from Oslo, Norway  177 Potential pathways that were not captured in the model examined in this study should be further investigated in future research to more fully understand how compactness relates to SWB. The study’s findings are based on survey data from one city; subsequent studies could explore the topic in other geographical, socio-economic and cultural contexts. For further advancements in this field, the pathways between the compact city and SWB could be examined using longitudinal data to provide stronger insights into causality as well as with qualitative and mixed-methods studies for a more nuanced understanding.

IMPLICATIONS FOR POLICY The chapter has presented evidence that can be useful for city policy and planning by shedding light on the links between the compact city and SWB. Findings have possible implications for how to improve QOL in compact cities. Practitioners, policymakers and decision-makers can ensure and promote the strengths of the compact city: short distances, short travel times, active social life, accessibility, walkability, cyclability, efficient public transport services and access to the public realm. City policy and planning can further improve quality of life in compact cities by mitigating relevant weaknesses or common urban problems. The Oslo study findings suggest some possible negative implications of the compact city, including lower emotional response to urban compactness and slightly increased resident anxiety. Strategies that could be employed to mitigate the potential weaknesses of the compact city include: ● ● ● ● ● ●

the integration of various forms of urban nature into compact cities; strategies to reduce noise, light and air pollution; pedestrianisation and car restrictions; strategies to limit overcrowding; improvements in safety through environmental design; and support for vulnerable groups in terms of housing and transport.

Compact cities have been under debate for their contribution to quality of life during the COVID-19 pandemic. Some of their characteristics – public transport reliance, small dwellings and lack of private yards – have been challenged and may have become less desirable during the COVID-19 pandemic. On the other hand, other compact city characteristics – access to hospitals and daily-life facilities, walkability and cyclability – have been helpful to mitigate the negative impacts of COVID-19 on urban quality of life. As urban areas accommodate more and more people around the globe, it is important that compact cities are resilient to pandemic events by providing, among others, good access to healthcare, secure and frequent public transport, good conditions for walking and cycling, access to outdoor space (public, communal and private), and sufficient housing standards in terms of size, ventilation and overall quality.

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12. The role of neighbourhoods in quality of life: toward a comprehensive model1 Robert Gifford, Leila Scannell, Christine Kormos, Jessica Rourke and Amanda J. McIntyre

INTRODUCTION With most of the world’s population now living in an urban area, understanding the role of urban neighbourhoods in people’s overall experienced quality of life (QOL) is important. It is well established that human–environment interactions can strongly influence well-being (see Das, 2008; Stokols, 1985), with a cross-cultural study by the World Health Organization suggesting that environmental factors are as strong a predictor of QOL as are other factors (Power et al., 1999), for example life-central factors such as relationships (Bookwala, 2011), work (Drobnič and Präg, 2010), physical and mental health (Meijer et al., 2009) and economic status (Alwin, 1987; Wong, 2011). However, much about the relative importance of various possible influences on people’s overall experienced QOL remains unresolved, despite efforts going back for over half a century (for example, Campbell et al., 1976), including attempts to investigate residential satisfaction (see Marans and Rodgers, 1975). Important questions to ask include: ● How important is one’s neighbourhood? ● Is the physical or the experienced neighbourhood more important? ● Do they both play an important a role? Certainly, neighbourhood-related factors in general influence well-being (for example, Smith et al., 2004), and it is a reasonable proposition that both physical and experiential factors influence residents’ overall QOL. This chapter addresses such questions. It proposes a model including four physical elements of the neighbourhood and seven experiential elements of the neighbourhood. A preliminary study including multiple indicators of each of the elements is developed and tested using community samples in three diverse neighbourhoods of different cities. The results suggest that neighbourhoods have perhaps been underrated as determinants of overall QOL.

QUALITY OF LIFE AND THE NEIGHBOURHOOD: SOME BACKGROUND Overall experienced QOL is a multidimensional construct that represents satisfaction with key aspects of life and the degree to which important values and needs are fulfilled (see Diener, 1995). But overall QOL is not limited to any one domain. It includes many elements, such as one’s sense of freedom, privacy, identity, social justice, status, challenge and many others 182

The role of neighbourhoods in quality of life: toward a comprehensive model  183 (Poortinga et al., 2004). QOL is also influenced by a person’s experiences with family, work, health, interpersonal relations and other key aspects of life (see Moser and Uzzell, 2003). In addition, people’s neighbourhood may also play an important role (Marans and Kweon, 2011; Robert, 1998; Yen and Syme, 1999). But the question is how? It has been shown that both experiential and physical context indicators of QOL help improve the prediction of residential environment quality (Milbrath, 1979). However, which of the two is more influential is much debated. For example, Fried (1982) suggested that most of the variance in residential satisfaction is accounted for by the physical quality of the neighbourhood, a finding supported in a study in Hong Kong in which the objective assessment of residential features (distance to amenities, physical dwelling quality and community facilities) explained a significant amount of the differences in perceived QOL (Ng et al., 2005). Research also shows that experienced aspects of the neighbourhood can predict QOL (Das, 2008), with personal neighbourhood evaluations – such as satisfaction – possibly being more influential than physical factors on outcome measures such as physical health, which is an important part of QOL (Ellaway and Macintyre, 1998; Hadley-Ives et al., 2000; Robert, 1998). A Glasgow study found residents’ perceptions of their neighbourhoods (such as amenities, neighbourliness and satisfaction) to be negatively associated with their health, regardless of the physical features of their neighbourhoods (Sooman and Macintyre, 1995). This chapter presumes that both element types are important, based in part on the meaning-mediated assumption that residents draw their experiences from their physical environment (Stedman, 2003). Based on a review of the literature, an initial model is proposed incorporating both physical and experienced elements of neighbourhood (Figure 12.1).

Source: The authors.

Figure 12.1

The initial model

The Physical Neighbourhood Physical aspects of neighbourhoods undoubtedly have substantial effects on mental and physical health (see Macintyre et al., 1993; Yen and Syme, 1999). For example, children who live in neighbourhoods that are in worse physical condition tend to have more behavioural problems, even after controlling for income and other factors (Gifford and Lacombe, 2006). With the

184  Handbook of quality of life research competing goals of comprehensiveness and parsimony, our literature review has identified four contextual factors that have been hypothesised to affect QOL: ● ● ● ●

appearance (such as well-maintained versus crumbling infrastructure and housing); busyness (such as little versus much vehicle and human traffic); affluence (average income); and amenities (such as services often used by householders).

Evidence pertaining to each of these follows. Appearance Visual features, whether natural (trees, gardens and green spaces) or built (residential and commercial buildings, streets), can facilitate cognitive and emotional functioning and reduce stress (see Kaplan and Kaplan, 1989; Velarde et al., 2007). Thus, neighbourhoods that are either tidy and well-maintained with natural elements or are littered and in poor physical condition with few natural elements may be expected to impact their residents’ QOL in predictably opposite ways. Busyness Stimulation from one’s neighbourhood arises from many sources, such as traffic, noise and other people. These features also influence QOL. For example, traffic negatively impacts the health of residents living in neighbourhoods with major streets or highways (Song et al., 2007). Busy streets can also interfere with socialisation among neighbours by blocking access to each other’s homes and reducing the number of aesthetically pleasing places to relax (Appleyard, 1981). Another obvious effect of busy streets is increased noise, which contributes to residents’ distress and detracts from their well-being (Ouis, 2001). However, stimulation and busyness within a neighbourhood can have positive outcomes. Some residents are comforted by the sounds of traffic and view it as part of city living (Walker and Hiller, 2007; Whyte, 1974). Pedestrian activity can create a sense of liveliness and promote walkability and social interaction (Baum et al., 1978). Thus, the impact of busyness on QOL remains unresolved, and is one reason for its focus in this chapter. Affluence The average wealth of a neighbourhood has been shown to be positively correlated with resident well-being (Brooks-Gunn et al., 1993; Klebanov et al., 1998). Economic factors may also influence neighbourhood satisfaction. Not surprisingly, residents of run-down neighbourhoods experience lower neighbourhood satisfaction (Galster and Hesser, 1981). Amenity distance Services that households usefully patronise and their distribution in a neighbourhood can influence QOL (Field et al., 2004). Accessible amenities provide opportunities for education, employment, leisure and physical activities (Parks et al., 2003; Takano et al., 2002). Well-being and health can also benefit from access to health services (such as clinics and pharmacies) and grocery stores (Barton and Tsourou, 2000; Leather, 1996). On the other hand, proximity to unhealthy options – such as fast-food outlets – has been linked to increased health-related problems, such as obesity (Pearce et al., 2007). Local amenities such as coffee shops are also

The role of neighbourhoods in quality of life: toward a comprehensive model  185 places where social participation occurs, both formally and informally (Cattell, 2001; Dalgard and Tambs, 1997). Thus, again, amenities can have a positive or negative influence on QOL. The Experienced Neighbourhood People experience where they live. The lived aspects of neighbourhoods identified as being important to QOL (see Bonaiuto et al., 2006; Brown et al., 2004) include: ● ● ● ● ● ● ●

neighbourhood satisfaction; residential satisfaction; neighbourhood place attachment; neighbourhood social involvement; neighbourhood social support; satisfaction with the distance to amenities; and perceived opportunities for physical activity.

Neighbourhood satisfaction Satisfaction with one’s neighbourhood is associated with psychological well-being (Carp and Christensen, 1986) and overall life satisfaction (see Campbell et al., 1976; Fried, 1982; Sirgy and Cornwell, 2002). Therefore, neighbourhood satisfaction should be related to overall QOL. Residential satisfaction Satisfaction with one’s own housing satisfaction obviously is a part of QOL (Amérigo and Aragonés, 1997). This is based on one’s perceptions of physical features of the residential dwelling – such as its physical condition, form and amount of space (see Gifford and Lacombe, 2006; Michelson, 1977) – as well as social influences – such as comparisons of one’s dwelling with those of peers (see Michalos, 1994) – along with satisfaction with one’s neighbours (Amérigo and Aragonés, 1997). Residences are primary living environments, and satisfaction with a place where one spends much of one’s most valued time will positively or negatively affect QOL. Housing satisfaction has been proposed as the second-strongest predictor of life satisfaction second only to marital satisfaction (Fried, 1984). Greater satisfaction with housing has been linked to lower levels of psychiatric distress and to higher levels of adaptive functioning among individuals with mental illness (Wright and Kloos, 2007). Furthermore, QOL increases when individuals move to a residence with which they are more satisfied (Kahlmeier et al., 2001). Greater housing satisfaction should then be correlated with greater QOL. Neighbourhood place attachment The cognitive-affective bond that develops between an individual and a meaningful place (see Low and Altman, 1992; Scannell and Gifford, 2010) may be one of the most important sources of well-being (Giuliani, 2003). This notion is supported by studies documenting the negative psychological consequences of displacement. Like the effects of separation from a loved one, separation from an important place may produce feelings of insecurity, sadness and longing (Fullilove, 1996). In a classic study, Marc Fried (1963) found that residents expressed grief and distress after being forced to relocate from their neighbourhood that was slated to be demolished.

186  Handbook of quality of life research Even when relocation is voluntary, individuals may suffer from the effects of a severed place bond. For example, Chinese students in Australia who longed for home had more health problems and lower grades than those who were more attached to their new environment (Hornsey and Gallois, 1998). Place attachment increases well-being because the sense of safety and comfort it offers is conducive to relaxation, problem-solving and self-regulation (Korpela, 1989; Korpela et al., 2001). In sum, the evidence supports the notion that the development of a place bond contributes positively to one’s QOL. Neighbourhood social involvement The experience of participating in satisfying social activities with neighbours and in community activities and events is shown to be associated with better mental and physical health (Baum et al., 2000; Muhajarine et al., 2008). In older adult populations, community involvement and social participation predict positive health-related outcomes (Richard et al., 2009). These findings suggest that involvement should be included in an investigation of the effects of experienced neighbourhood features on QOL. Perceived neighbourhood social support Beyond mere involvement in local activities, QOL increases when residents happily exchange tangible or intangible resources (Cohen and Syme, 1985) that make them feel ‘accepted, loved, or prized by others’ (Brannon et al., 2000, p. 120). Neighbourhood social support may be informal – such as a chat or a quick visit – or more formal – such as belonging to a neighbourhood crime-watch group (Ross and Jang, 2000; Woldoff, 2002). Greater social support is experienced when other residents of one’s community are trusted and when residents are willing to help their neighbours (Buka et al., 2003). The benefits of neighbourhood social support have often been demonstrated. For example, it was the strongest predictor of life satisfaction among adults with an intellectual disability as well as other adults from the community (Bramston et al., 2005). Among children living in communities with high rates of violence and crime, neighbourhood social support was associated with greater well-being, less anxiety and better school performance (Hill and Madhere, 1996). Feeling supported by neighbours should be associated with increased QOL. Satisfaction with distance to amenities Aside from the impact of the mere presence of or distance to amenities within one’s neighbourhood, satisfaction with their accessibility is important (Sirgy and Cornwell, 2002). Given that residents are known to adjust their behaviour to maintain acceptable travel times to and from their destinations (Mokhtarian and Chen, 2004), satisfaction with the distance to local amenities can greatly affect the usability of the residential environment, particularly for pedestrians. Therefore, the more satisfied residents are with the distance to and from amenities, the more likely they will interact with their local environment, including those who live within it. Outcomes from these interactions may contribute to greater QOL. Physical activity in the neighbourhood Movement and activity within one’s neighbourhood can contribute to the development of its meaning and, therefore, the way that it is experienced by the individual. As Seamon (2002) has argued, place is understood through bodily movement. Furthermore, movement is also important for QOL (see Warburton et al., 2006). For example, regular exercise reduces the risk of

The role of neighbourhoods in quality of life: toward a comprehensive model  187 coronary heart disease, stroke, obesity, diabetes, osteoporosis, cancer and other physical health problems, decreases fatigue, depression and anxiety, and benefits cognitive functioning (see Dunn et al., 2001; Stampfer et al., 2000; Thune and Furber, 2001; Wendel-Vos et al., 2004; Weuve et al., 2004). Exercise scientists have devoted much effort to identifying the predictors of physical activity. As part of this endeavour, considerable attention has been devoted to the neighbourhood as a key determinant of physical activity. Therefore, physical activity within one’s personal neighbourhood is worthy of consideration as a predictor of overall QOL.

EMPIRICALLY INVESTIGATING THE ROLE OF NEIGHBOURHOODS IN OVERALL QUALITY OF LIFE Two studies are discussed, the objective of which is to better understand residents’ overall experienced QOL as it relates to their physical and experienced neighbourhoods. The four physical elements and the seven experienced elements discussed above are included in the model framework (refer back to Figure 12.1). These 11 constructs are examined to estimate the relative contribution of each element to perceived overall QOL, and their collective contributions to it. A Preliminary Study A preliminary study was designed to select or create reliable measures of the 11 key constructs to provide a solid psychometric foundation for the main study. An extensive list of items relating to each construct was generated. Pilot interviews were conducted with six community residents focusing on the clarity and understandability of the items. Based on this feedback, an initial 12-scale instrument – four physical and seven experiential constructs, plus the QOL criterion – was devised. The physical neighbourhood Four items relating to neighbourhood contextual characteristics – drawn in part from Bonaiuto et al. (2006) – were used: ● ● ● ●

appearance (such as structural condition of residences and amount of green space); busyness (traffic level and number of pedestrians); affluence (average neighbourhood household income); and amenity accessibility (distances to key amenities from each resident’s block).

Trained research assistants rated the first two neighbourhood characteristics on site. Socio-economic data for affluence were gathered from Statistics Canada census data broken down at the neighbourhood level. Distance to amenities listed by residents was measured using online mapping tools.

188  Handbook of quality of life research The experienced neighbourhood Seven personal features assessed participants’ experiences in their neighbourhoods: 1. A neighbourhood satisfaction scale was generated based on previous research (Bonaiuto et al., 2006). It was assessed based on participants’ ratings of their local parks, aesthetic quality of buildings, traffic, and so on. 2. A residential satisfaction measure was created using items mainly derived from Michelson (1977) to assess satisfaction with various housing aspects, such as noise, amount of space, cost and privacy. 3. Neighbourhood place attachment was measured using items from Brown et al. (2003) that assess residents’ feelings of attachment and pride in their dwelling, yard, block and neighbourhood. 4. A neighbourhood social involvement scale assessed the frequency of residents’ participation in community events, as well as their frequency of engagement in social activities with neighbours. 5. A perceived neighbourhood social support scale was adapted from Schieman and Meersman (2004). Its items were revised so that each referred to social support transactions that occur within the neighbourhood. 6. Satisfaction with the distance to amenities was assessed by asking participants to report their level of satisfaction with the distance to 12 major neighbourhood amenities (grocery, pharmacy, retail complex, bank, restaurant, pub, library, school, park, bus stop and place of worship).2 7. A neighbourhood physical activity scale was modelled after the International Physical Activity Questionnaire (IPAQ; Craig et al., 2003), but adapted to assess physical activity within the neighbourhood. Participants recalled the past seven days and estimated the amount of time that they performed various out-of-home physical activities within their neighbourhood for more than 15 minutes at a time. Activities included walking, bicycling and gardening or yard work. For example, participants answered questions such as: ‘On average, how much time per day do you spend walking in your neighbourhood?’ Quality of life Overall quality of life was measured using a scale adapted from that of Poortinga et al. (2004), which asks participants to rate the importance of 21 aspects of QOL. In the present study, items were reworded to evoke residents’ ratings of their experienced QOL. Some sample items were: ● ‘I experience a variety of pleasant challenges and exciting things’; ● ‘I have the opportunity to do what I want and to be myself’; and ● ‘I have a comfortable and easy daily life’. Demographics The questionnaire collected information on respondents’ age, gender and income, residential history (for example, number of years spent in rural, suburban or urban areas), housing type and total area of the residence. Method The study surveyed a sample of residents (N = 114) from several neighbourhoods in a mid-sized Western Canadian city, randomly selected along postal routes within each neigh-

The role of neighbourhoods in quality of life: toward a comprehensive model  189 bourhood. The participants completed the questionnaire and then drew the boundaries of their neighbourhoods, as they thought of them, on maps that featured their region of the city. Two research assistants went to each participant’s block to assess its contextual features. To achieve an accurate portrayal of changing physical neighbourhood conditions (for example, busyness), each rater assessed certain features twice – once during the day, and once in the evening. Intraclass correlations (ICC) (Shrout and Fleiss, 1979) across raters were computed for the scales and comprise the four contextual indices. All except aesthetic quality of buildings had excellent ICCs, ranging from .83 to 1.0, with a median of .99. Given the strong internal consistencies, values for each item were averaged across raters. The internal consistency of the four physical contextual indices was adequate to excellent: ● ● ● ●

appearance, α = .74; busyness, α = .61; affluence, α = .87; and distance to amenities, α = .94.

The experiential indices also had good to excellent internal consistency: ● ● ● ● ● ●

neighbourhood satisfaction, α = .84; residential satisfaction, α = .84; neighbourhood place attachment, α = .75; neighbourhood social involvement, α = .67; neighbourhood social support, α = .91; and satisfaction with distance to amenities, α = .82.

The neighbourhood physical activity scale consisted of five items. Its internal consistency was not assessed because it is an omnibus, open-ended response scale. The dependent measure of overall QOL had excellent internal consistency, α = .93. Analysis When numerous physical and experiential measures are developed on a theoretical basis, some redundancies may occur. To determine whether the 11-scale preliminary model could reasonably be simplified, principal component analyses (PCAs) with oblimin rotations were conducted to assess the latent variable structure among the physical and, separately, the experiential measures. For the physical indices, the scree plot revealed three factors above the point of inflexion. Taken together, these factors explained 85.6 per cent of the total variance, suggesting that all the indices were well represented by the factors: ● Factor 1 consisted of the affluence and appearance indices, and was named Neighbourhood Physical Condition; ● Factor 2 comprised the distance-to-actual-amenities index, and was named Objective Amenity Distances; and ● Factor 3 represented neighbourhood busyness, and was named Neighbourhood Busyness. For the experienced neighbourhood indices, the scree plot did not display a distinct point of inflexion. A four-factor solution representing the seven initial measures was retained, and

190  Handbook of quality of life research these factors collectively explained 82.9 per cent of the variance in the items. These factors were named: ● ● ● ●

Neighbourhood Place Attachment; Neighbourhood Supportive Involvement; Amenity Distance Satisfaction; and Neighbourhood Physical Activity.

The Main Study The preliminary study served its purpose of developing and testing the psychometric properties of the model’s measures. But it was not without limitations. It was conducted in a relatively affluent city, and while efforts were made to distribute surveys in its less-affluent neighbourhoods, quite high experienced QOL was found, which might limit the generalisability of the findings. Therefore, for the main study, it was decided to choose samples from more diverse neighbourhoods in a different city. The main study had three goals: ● to further establish the reliability of the measures and their factor structure in more diverse neighbourhoods; ● to examine the relations between the elements of the proposed model to determine the power of the constructs to explain overall experienced QOL; and ● to investigate whether the relation of the physical neighbourhood features to overall QOL is mediated by the experienced neighbourhood features – that is, do the physical features of a neighbourhood have a direct, indirect or no effect on overall QOL? Method The indices developed in the preliminary study remained very similar. However, along with the modifications described previously, a few new items were included for psychometric purposes. To reduce problems with outliers, the physical activity scale was changed from an open-ended to closed-ended scale. Furthermore, because of the different financial information available about the new municipalities, the neighbourhood finances index was computed using median (rather than average) household income, average gross rent and the percentage of low-income families in the neighbourhood. The survey participants were recruited from three diverse neighbourhoods in a large Canadian city, the neighbourhoods being chosen deliberately to represent low-income, new middle-class (mostly newer high-rise condos) and traditional middle-class (detached, older single-family dwellings) housing. The returned survey packages resulted in a sample size of N = 163. Predicting overall QOL ICCs were examined to assess the interrater reliability of the physical neighbourhood scales. Two assistants independently coded 15 per cent of the returned questionnaires. The ICCs ranged from .75 to .99 (M = .93). Given the excellent interrater reliability, one rater coded the remaining contextual variables. The elements and structure of the proposed model were then examined. Then PCAs with oblimin rotations were conducted to reduce the complexity of the model, if warranted. The

The role of neighbourhoods in quality of life: toward a comprehensive model  191 PCA performed on the physical indices produced the same three factors as in the preliminary study, explaining 89 per cent of the variance in the items. The PCA performed on the experiential indices resulted in the same four-factor solution as in the preliminary study, explaining 84 per cent of the variance in the items. To assess the contribution of the neighbourhood to overall QOL, a simultaneous regression analysis was conducted with the seven physical and experiential neighbourhood indices as predictors. The result was strongly significant, F(7, 142) = 13.66, p < 0.001. The indices collectively explained about 40 per cent of the variance in QOL (R2 = 0.40; adjusted R2 = 0.37, p < 0.001), which – in Cohen’s (1988) terminology – represents a strong effect size, r = .61. Among the physical neighbourhood factors, the beta weights were: ● Objective Amenity Distances: –.12; ● Neighbourhood Busyness: .17; and ● Neighbourhood Physical Condition: .12. Among the experienced neighbourhood factors, the beta weights were: ● ● ● ●

Neighbourhood Attachment: .50; Neighbourhood Supportive Involvement: .23; Neighbourhood Physical Activity: –.10; and Amenity Distance Satisfaction: –.01.

Mediation analyses The final goal was to investigate whether physical neighbourhood factors are directly, indirectly (mediated) or not related to experienced QOL. The model implicitly asserts that the influence of physical features flows through or is filtered by the experiential factors. The mediational analyses suggested by Baron and Kenny (1986) was used, which demonstrates that the necessary criteria for mediation were met – that is, that the physical and experiential environments are related, and that both predict QOL. First, a hierarchical linear regression analysis was performed in which the set of physical factors was entered in the first step and the set of experiential factors was entered in the second. The linear combination of physical factors significantly predicted QOL [R2 = 0.08, adjusted R2 = 0.06, F(3, 146) = 3.97, p = 0.01], but in the second step, the experiential predictors contributed a much larger additional amount of predicted variance to the model [R2-change = 0.33, F-change (4, 142) = 19.43, p < 0.001]. In sum, these analyses support the notion that the experienced neighbourhood mediates the association between the physical neighbourhood and QOL. The results are depicted in Figure 12.2. Discussion In a diverse set of three urban neighbourhoods, this study has provided strong support for the proposed model, including further evidence of the measures’ reliability and evidence that the neighbourhood elements account for a substantial proportion of overall experienced QOL. The main study generally confirmed the factor structure found in the preliminary study: a three-factor structure for the physical measures (Neighbourhood Physical Condition, Objective Amenity Distances and Neighbourhood Busyness); and a four-factor structure for

192  Handbook of quality of life research

Note: * Significant at the 95% level; **significant at the 99% level. Source: The authors.

Figure 12.2

The final empirical model, after the consolidation of some elements

the experience measures (Neighbourhood Place Attachment, Neighbourhood Supportive Involvement, Amenity Distance Satisfaction and Neighbourhood Physical Activity). The mediation findings demonstrate how the constructed meanings of neighbourhoods are shaped by their physical features; that is, neighbourhood physical condition appears to shape neighbourhood attachment, supportive involvement and neighbourhood physical activity. The study also demonstrates that these constructed meanings in turn strongly predict residents’ overall QOL. The results confirm that experienced elements of a neighbourhood mediate the relation between its physical features and residents’ overall QOL. Figure 12.3 depicts an integrated view of the whole model, demonstrating that considerable variance in overall QOL is attributable to both contextual and personal neighbourhood factors, attesting to their importance for overall QOL.

CONCLUSION The chapter has proposed a modelling approach to investigate the role of physical and experiential elements of a neighbourhood for residents’ overall experienced QOL. Although past research on the nature and impact of those elements has been accumulating, few studies had investigated their combined and unique relation to overall QOL. The research reported in this

The role of neighbourhoods in quality of life: toward a comprehensive model  193

Source: The authors.

Figure 12.3

The personal neighbourhood mediates the contextual neighbourhood

chapter builds on Bonaiuto et al. (2006), who identified certain key contextual and personal neighbourhood factors by exploring how these (and other) factors relate to general experienced QOL, and Sirgy and Cornwell (2002), who showed that satisfaction with different features of the neighbourhood (social, economic and physical) affect different domain satisfactions, which in turn affect life satisfaction. Our proposed multi-element model consists of three physical factors and four experiential factors. In terms of the physical vs experienced theoretical issue, these studies discussed here clearly show both are important. Both types of factors predict QOL, working together (as shown by the regression and mediation analyses) to explain important amounts of variation in residents’ experienced QOL. This, of course, assumes that the model’s links are causal, which field studies like this one cannot claim. It might be surprising that about 40 per cent of the variance in QOL in general is explained by contextual and personal neighbourhood factors, given that numerous other influences on QOL exist, from marriage and work (see Bookwala, 2011; Drobnič and Präg, 2010), to health and poverty (see Meijer et al., 2009; Wong, 2011), to religion and disaster (see Idler et al., 2009; Priebe et al., 2011), to crime levels or war and social change (see Araya et al., 2011; Cheung and Leung, 2010). A model that includes all the above would be quite cumbersome, but would begin to determine how all drivers of QOL play a role. For now, the modelling here confirms that the physical and experienced neighbourhood appear to be very important factors. The research presented in this chapter has four limitations. First, the populations sampled were from one country, and although the study employed a diversified sample from two cities and three highly dissimilar neighbourhoods, efforts are needed to replicate the results elsewhere. Second, the research focused on adults, no children being included. Third, the response rate was low, which may have resulted from the length of the questionnaire, the method of recruitment or from the lower levels of helpfulness often observed in cities, stemming from urban overload (for example, Milgram, 1970). Despite the potential problems posed by a low response rate, it is important to note that other similar studies have also reported low response rates (see Sirgy and Cornwell, 2002). Finally, the studies were conducted in cities with relatively low crime rates, so future research is needed in cities with neighbourhoods where crime rates are more variable.

194  Handbook of quality of life research The studies demonstrate that the neighbourhood – both as a physical setting and an experienced place – is a key component of overall QOL. About 40 per cent of overall experienced QOL can be predicted by these aspects of neighbourhood, which is perhaps surprising when one considers other important parts of people’s lives – such as health, relationships and occupational factors – are presumably strong factors in one’s overall QOL. In fact, one might suggest that the factors included in the present studies are sometimes overlooked in explanations of QOL, and therefore deserve more attention. Hopefully, the modelling effort in this chapter will be visible enough and useful enough to encourage new modelling, something that earlier authors (such as Marans and Kweon, 2011) had lamented as occurring too infrequently.

NOTES 1. The authors gratefully acknowledge the research assistance of Eva Gifford, Angela Liu, Ai Nakahama, Derek Pacheco, Danielle Lawson, Matt Stafford, Ildiko Kovacs, Jaclyn Casler, Amy Green, Rob Hamilton, Coral Candlish-Rutherford, Miko Betanzo and Jeffrey Gardiner. This research was supported by the Social Sciences and Humanities Research Council of Canada. 2. Few respondents listed a place of worship in the preliminary study, so coffee shop was added for the main study.

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The role of neighbourhoods in quality of life: toward a comprehensive model  195 Brown, J., Bowling, A. and Flynn, T. (2004), Models of Quality of Life: A Taxonomy and Systematic Review of the Literature, University of Sheffield, European Forum on Population Ageing Research. Buka, S., Brennan, R.T. and Rich-Edwards, J.W. et al. (2003), ‘Neighborhood support and the birth weight of urban infants’, American Journal of Epidemiology, 157, 1–8. Campbell, A., Converse, P.E. and Rodgers, W.L. (1976), The Quality of American Life: Perceptions, Evaluations, and Satisfactions, New York: Russell Sage Foundation. Carp, F.M. and Christensen, D.L. (1986), ‘Older women living alone: technical environment assessment of psychological well-being’, Research on Aging, 8, 407–25. Cattell, V. (2001), ‘Poor people, poor places, and poor health: the mediating role of social networks and social capital’, Social Science and Medicine, 52, 1501–16. Cheung, C.-K. and Leung, L. (2010), ‘Ways that social change predicts personal quality of life’, Social Indicators Research, 96, 459–77. Cohen, J. (1988), Statistical Power Analysis for the Behavioural Sciences, 2nd edition, Hillsdale, NJ: Erlbaum. Cohen, S. and Syme, S.L. (1985), ‘Issues in the study and application of social support’, in S. Cohen and S. Syme (eds), Social Support and Health, San Francisco, CA: Academic Press, pp. 3–22. Craig, C.L., Marshall, A.L. and Sjostrom, M. et al. (2003), ‘International physical activity questionnaire: 12-country reliability and validity’, Medicine and Science in Sports and Exercise, 35, 1381–95. Dalgard, O.S. and Tambs, K. (1997), ‘Urban environment and mental health: a longitudinal study’, British Journal of Psychiatry, 171, 530–36. Das, D. (2008), ‘Urban quality of life: a case study of Guwahati’, Social Indicators Research, 88, 297–310. Diener, E. (1995), ‘A value-based index for measuring national quality of life’, Social Indicators Research, 36, 107–27. Drobnič, S.B. and Präg, P. (2010), ‘Good job, good life? Working conditions and quality of life in Europe’, Social Indicators Research, 99, 205–25. Dunn, A.L., Trivedi, M.H. and O’Neal, H.A. (2001), ‘Physical activity dose–response effects on outcomes of depression and anxiety’, Medicine and Science in Sports and Exercise, 33(Suppl.), S587–S597. Ellaway, A. and Macintyre, S. (1998), ‘Does housing tenure predict health in the UK because it exposes people to different levels of housing related hazards in the home or in its surroundings?’, Health and Place, 4, 141–50. Field, A., Witten, K., Robinson, E. and Pledger, M. (2004), ‘Who gets to what? Access to community resources in two New Zealand cities’, Urban Policy and Research, 22, 189–205. Fried, M. (1963), ‘Grieving for a lost home’, in L.J. Dulh (ed.), The Urban Condition: People and Policy in the Metropolis, New York: Simon & Schuster, pp. 124–42. Fried, M. (1982), ‘Residential attachment: sources of residential and community satisfaction’, Journal of Social Issues, 38, 107–19. Fried, M. (1984), ‘The structure and significance of community satisfaction’, Population and Environment, 7, 61–86. Fullilove, M.T. (1996), ‘Psychiatric implications of displacement: contributions from the psychology of place’, American Journal of Psychiatry, 153, 1516–23. Galster, G.C. and Hesser, G.W. (1981), ‘Residential satisfaction: compositional and contextual correlates’, Environment and Behavior, 13, 735–58. Gifford, R. and Lacombe, C. (2006), ‘Housing quality and children’s socioemotional health’, Journal of Housing and the Built Environment, 21, 177–89. Giuliani, M.V. (2003), ‘Theory of attachment and place attachment’, in M. Bonnes, T. Lee and M. Bonaiuto (eds), Psychological Theories for Environmental Issues, Aldershot: Ashgate, pp. 137–10. Hadley-Ives, E., Stiffman, A. and Elze, D. et al. (2000), ‘Measuring neighborhood and school environments: perceptual and aggregate approaches’, Journal of Human Behavior in the Social Environment, 3, 1–28. Hill, H.M. and Madhere, S. (1996), ‘Exposure to community violence and African American children: a multidimensional model of risks and resources’, Journal of Community Psychology, 24, 26–43.

196  Handbook of quality of life research Hornsey, M. and Gallois, C. (1998), ‘The impact of interpersonal and intergroup communication accommodation on perceptions of Chinese students in Australia’, Journal of Language and Social Psychology, 17, 323–47. Idler, E.L., McLaughlin, J. and Kasl, S. (2009), ‘Religion and the quality of life in the last year of life’, Journal of Gerontology, 64B, 528–57. Kahlmeier, S., Schindler, C., Grize, L. and Braun-Fahrländer, C. (2001), ‘Perceived environmental housing quality and well-being of movers’, Journal of Epidemiology and Community Health, 44, 708–15. Kaplan, R. and Kaplan, S. (1989), The Experience of Nature: A Psychological Perspective, Cambridge, UK: Cambridge University Press. Klebanov, P.K., Brooks-Gunn, J., McCarton, C. and McCormick, M.C. (1998), ‘The contribution of neighborhood and family income to developmental test scores over the first three years of life’, Child Development, 69, 1420–36. Korpela, K.M. (1989), ‘Place-identity as a product of environmental self-regulation’, Journal of Environmental Psychology, 9, 241–56. Korpela, K.M., Hartig, T., Kaiser, F.G. and Fuhrer, U. (2001), ‘Restorative experience and self-regulation in favorite places’, Environment and Behavior, 33, 572–89. Leather, S. (1996), The Making of Modern Malnutrition: An Overview of Food Poverty in the UK, London: Caroline Walker Trust. Low, S.M. and Altman, I. (1992), ‘Place attachment: a conceptual inquiry’, in I. Altman and S.M. Low (eds), Place Attachment, New York: Plenum Press, pp. 1–2. Macintyre, S., Maciver, S. and Sooman, A. (1993), ‘Area, class, and health: should we be focusing on place or people?’, Journal of Social Policy, 22, 213–34. Marans, R.W. and Kweon, B.-S. (2011), ‘The quality of life in Metro Detroit at the beginning of the millennium’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 163–83. Marans, R.W. and Rodgers, W. (1975), ‘Toward an understanding of community satisfaction’, in A.W. Hawley and V. Rock (eds), Metropolitan America in Contemporary Perspective, New York: Wiley, pp. 299–352. Meijer, C.J., Koeter, M.W.J., Sprangers, M.A.G. and Schene, A.H. (2009), ‘Predictors of general quality of life and the mediating role of health-related quality of life in patients with schizophrenia’, Social Psychiatry and Psychiatric Epidemiology, 44, 361–8. Michalos, A.C. (1994), ‘Goal formation, achievement and their consequences for residential satisfaction and mobility’, in J. Cecora (ed.), Changing Values and Attitudes in Family Households with Rural Peer Groups, Social Networks, and Action Spaces, Bonn: Society for Agricultural Policy Research and Rural Sociology (FAA), pp. 31–40. Michelson, W. (1977), Environmental Choice, Human Behavior and Residential Satisfaction, New York: Oxford University Press. Milbrath, L.W. (1979), ‘Policy-relevant quality of life research, The ANNALS of the American Academy of Political and Social Science, 444, 32–45. Milgram, S. (1970), ‘The experience of living in cities: adaptations to urban overload create characteristic qualities of city life that can be measured’, Science, 167, 1461–8. Mokhtarian, P.L. and Chen, C. (2004), ‘TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets’, Transportation Research, 38, 643–75. Moser, G. and Uzzell, D. (2003), ‘Environmental psychology’, in T. Millon and M.J. Lerner (eds), Comprehensive Handbook of Psychology, Vol. 5: Personality and Social Psychology, Hoboken, NJ: Wiley, pp. 419–45. Muhajarine, N., Labonte, R., Williams, A. and Randall, J. (2008), ‘Person, perception, and place: what matters to health and quality of life’, Social Indicators Research, 85, 53–80. Ng, S.H., Kam, P.K. and Pong, R.W.M. (2005), ‘People living in ageing buildings: their quality of life and sense of belonging’, Journal of Environmental Psychology, 25, 347–60. Ouis, D. (2001), ‘Annoyance from road traffic noise: a review’, Journal of Environmental Psychology, 21, 101–20.

The role of neighbourhoods in quality of life: toward a comprehensive model  197 Parks, S.E., Housemann, R.A. and Brownson, R.C. (2003), ‘Differential correlates of physical activity in urban and rural adults of various socioeconomic backgrounds in the United States’, Journal of Epidemiology and Community Health, 57, 29–36. Pearce, J., Blakely, T., Witten, K. and Bartie, P. (2007), ‘Neighborhood deprivation and access to fast-food retailing: a national study’, American Journal of Preventive Medicine, 32, 375–82. Poortinga, W., Steg, L. and Vlek, C. (2004), ‘Values, environmental concern, and environmental behavior: a study into household energy use’, Environmental and Behavior, 36, 70–93. Power, M., Bullinger, M., Harper, A. and the World Health Organization Quality of Life Group (1999), ‘The World Health Organization WHOQOL-100: tests of the universality of quality of life in 15 different cultural groups worldwide’, Health Psychology, 18, 495–505. Priebe, S., Marchi, F. and Bini, L. et al. (2011), ‘Mental disorders, psychological symptoms and quality of life 8 years after an earthquake: findings from a community sample in Italy’, Social Psychiatry and Psychiatric Epidemiology, 46, 615–21. Richard, L., Gauvin, L., Gosselin, C. and Laforest, S. (2009), ‘Staying connected: neighborhood correlates of social participation among older adults living in an urban environment in Montreal, Quebec’, Health Promotion International, 24, 46–57. Robert, S. (1998), ‘Community-level socioeconomic status effects on adult health’, Journal of Health and Social Behavior, 39, 18–37. Ross, C.E. and Jang, S.J. (2000), ‘Neighborhood disorder, fear, and mistrust: the buffering role of social ties with neighbors’, American Journal of Community Psychology, 28, 401–20. Scannell, L. and Gifford, R. (2010), ‘Defining place attachment: a tripartite organizing framework’, Journal of Environmental Psychology, 30, 1–10. Schieman, S. and Meersman, S. (2004), ‘Neighborhood problems and health among older adults: received and donated social support and the sense of mastery as effect modifiers’, The Journals of Gerontology, 59, S89–S97. Seamon, D. (2002), ‘Physical comminglings: body, habit, and space transformed into place’, The Occupational Therapy Journal of Research, 22, 42S–S51. Shrout, P.E. and Fleiss, J.L. (1979), ‘Intraclass correlations: uses in assessing rater reliability’, Psychological Bulletin, 86, 420–28. Sirgy, M.J. and Cornwell, T. (2002), ‘How neighborhood features affect quality of life’, Social Indicators, 59, 79–114. Smith, A.E., Sim, J., Scharf, T. and Phillipson, C. (2004), ‘Determinants of quality of life amongst older people in deprived neighborhoods’, Ageing and Society, 24, 793–814. Song, Y., Gee, G.C., Fan, Y. and Takeuchi, D.T. (2007), ‘Do physical neighborhood characteristics matter in predicting traffic stress and health outcomes?’, Transportation Research, 10, 164–76. Sooman, A. and Macintyre, S. (1995), ‘Health and perceptions of the local environment in socially contrasting neighborhoods in Glasgow’, Health & Place, 1, 15–26. Stampfer, M.J., Hu, F.B. and Manson, J.E. et al. (2000), ‘Primary prevention of coronary heart disease in women through diet and lifestyle’, The New England Journal of Medicine, 343, 16–22. Stedman, R. (2003), ‘Is it really just social construction? The contribution of the physical environment to sense of place’, Society and Natural Resources, 16, 671–85. Stokols, D. (1985), ‘A congruence analysis of human stress’, Issues in Mental Health Nursing, 7, 35–64. Takano, T., Nakamura, K. and Watanabe, M. (2002), ‘Urban residential environments and senior citizens’ longevity in megacity areas: the importance of walkable green spaces’, Journal of Epidemiology and Community Health, 56, 913–18. Thune, I. and Furber, A.-S. (2001), ‘Physical activity and cancer risk: dose–response and cancer, all sites and site-specific’, Medicine and Science in Sports and Exercise, 33, 530–50. Velarde, M.D., Fry, G. and Tveit, M.S. (2007), ‘Health effects of viewing landscapes: landscape types in environmental psychology’, Urban Forestry and Greening, 6, 199–212. Walker, R.B. and Hiller, J.E. (2007), ‘Places and health: a qualitative study to explore how older women living alone perceive the social and physical dimensions of their neighborhoods’, Social Science and Medicine, 63, 1154–65. Warburton, D.E.R., Nicol, C.W. and Bredin, S.S.D. (2006), ‘Health benefits of physical activity: the evidence’, Canadian Medical Association Journal, 174, 801–909.

198  Handbook of quality of life research Wendel-Vos, G.C.W., Schuit, A.J. and Feskens, E.J. et al. (2004), ‘Physical activity and stroke: a meta-analysis of observational data’, International Journal of Epidemiology, 33, 787–98. Weuve, J., Kang, J.H. and Manson, J.E. et al. (2004), ‘Physical activity, including walking, and cognitive function in older women’, Journal of the American Medical Association, 292, 1454–61. Whyte, W.H. (1974, July), ‘The best street life in the world: why schmoozing, smooching, noshing, ogling are getting better all the time’, New York Magazine, 27–33. Woldoff, R.A. (2002), ‘The effects of local stressors on neighborhood attachment’, Social Forces, 81, 87–116. Wong, H. (2011), ‘Quality of life of poor people living in remote areas in Hong Kong’, Social Indicators Research, 100, 435–50. Wright, P.A. and Kloos, B. (2007), ‘Housing environment and mental health outcomes: a levels of analysis perspective’, Journal of Environmental Psychology, 27, 79–89. Yen, I.H. and Syme, S.L. (1999), ‘The social environment and health: a discussion of the epidemiologic literature’, Annual Review of Public Health, 20, 287–308.

13. Exploring quality of life in new towns: an overview1 Robert W. Marans and Noah J. Webster

INTRODUCTION The planning and building of new human settlements has occurred throughout the history of civilisation. Archaeological sites around the world offer early evidence, as do the remnants of old cities throughout Europe and Asia. Yet, it was not until the end of the nineteenth century when the impact of these settlements on the quality of life (QOL) of their inhabitants was specifically noted. This idea was central to Ebenezer Howard’s garden city movement, which proposed the building of new towns throughout the UK (Howard, 1898). He viewed new garden cities or new towns as a solution to the growth pressures on rapidly expanding industrial cities in the UK while enhancing the lives of new community residents. The garden city movement took hold during the twentieth century and eventually spread to other countries throughout Europe, the US and elsewhere. Over the years, the garden cities movement grew and eventually became the UK Town and Country Planning Association (TCPA), an organisation advocating sound planning for new and established towns and cities throughout the UK. In 1989, TCPA selected the theme ‘British towns and QOL’ for its annual conference. While conference papers covered topics such as accessibility, shopping and commerce, conservation, neighbourhoods and culture, none directly addressed the QOL of the residents living in towns or cities. In part, that omission reflected the paucity of research on this topic within urban settings, particularly new towns. Nonetheless, the concluding statement in the proceedings said: ‘the conference reinforced the argument that the QOL in British towns can only be improved through effective planning, community participation, and sensitive development, which is what the TCPA exists to promote’ (Hall, 1989, p. 13.4). While interest in new towns has had its ups and downs over the past century, there has been a surge in new town development during the early part of the twenty-first century, particularly in the rapidly developing parts of Latin America, Asia and Africa (see Peiser and Forsyth, 2021). This interest is likely to increase globally as growth pressures on large cities intensify, as the climate crisis becomes more pronounced and as governments address the exigencies associated with resettlement away from climatically vulnerable places. This chapter begins with a review of past research covering QOL in new towns and other planned communities. It then outlines a programme of new town research that urbanists and social researchers might embark on in the years ahead, taking account of a world impacted by climate change, global pandemics and war. As with past new town research, there are opportunities to learn from new studies offering lessons for other forms of housing and community development. The chapter concludes with a discussion of a research programme on new town development in China that began with a pilot study aimed at understanding the role of new urban developments including new towns in the QOL experience of all Chinese citizens. 199

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EARLY STUDIES RELATED TO QOL IN NEW TOWNS UK Studies For the most part, early new town studies conducted in the UK did not explicitly ask about people’s QOL, their overall satisfaction with their lives or the role that new town living contributed to individual health and well-being. Rather, studies focused on satisfaction with the community and factors people liked and disliked about living in the new town. A series of empirical studies in post-war British new towns focused on resident satisfaction and concluded that most participants were content with life in the new town. Positive comments gleaned from interviews highlighted: ● pride in homeownership and improvements in housing quality (Sykes and Livingstone, 1970; Sykes et al., 1967; Willmott, 1962, 1967); ● a higher class of people and related higher levels of consumption (Willmott, 1964); and ● clean and healthy surroundings (Sykes and Livingstone, 1970; Sykes et al., 1967). At the same time, residents complained about the lack of entertainment, public transport and other urban amenities (Sykes et al., 1967; Willmott, 1962, 1967). However, one study found that community facilities – when they did exist – were unsuccessful in either creating social networks or a sense of place (Willmott, 1967). It was subsequently suggested that the satisfaction expressed with the British new communities were more likely to reflect improvements in one’s personal life rather than attributes of the new town (Hillman, 1975). Another study conducted in the early new town of Hemel Hempstead showed high levels of community satisfaction particularly among older residents and those representing a higher social class (Bardo, 1977). However, a later survey in the same community found that levels of satisfaction had changed. The authors attribute this in part to regulations limiting the size of the community and restrictions on new housing construction. Such restrictions did not allow for changes in family composition over time, prompting greater dissatisfaction and a desire to move (Bardo and Bardo, 1983). US Studies An early attempt to test the hypothesis that new town living results in a higher QOL was made in the late 1960s in the US as part of a University of Michigan (U-M) study focusing on travel patterns among new town residents and people living in nearby less planned communities (Lansing et al., 1970). As part of the interviews with residents in both types of communities, the researchers asked: ‘In general, how satisfying do you find the ways you’re spending your life these days? Would you call it completely satisfying, pretty satisfying, not very satisfying, or not at all satisfying?’ Findings comparing the new towns and the paired conventional residential environments showed little variation in levels of satisfaction. The authors concluded that QOL is a far more complex phenomenon than could be captured in a single question. A few years later, Zehner and his University of North Carolina (UNC) colleagues further explored QOL in US new towns in their major study of US new communities (Zehner, 1977; see also Burby and Weiss, 1976). The study included interviews with residents of 17 new communities including Reston, VA and Columbia, MD, and interviews with residents in

Exploring quality of life in new towns: an overview  201 a paired set of less planned traditional residential developments. Repeating a question used in a US survey by Campbell et al. (1976) (‘How satisfied are you with your life as a whole these days?’) the researchers found that: (1) there were no differences in overall life satisfaction between the residents in the new communities and those in the less planned communities; and (2) the study respondents expressed somewhat higher levels of satisfaction with their lives than the Campbell et al. (1976) national sample of participants controlling for income. At the same time, respondents in the new communities expressed greater satisfaction than their counterparts in the traditional communities with community liveability and with two important life domains – use of leisure time and personal health; that is, new community residents expressed greater satisfaction with provision of recreation and healthcare facilities (Campbell et al., 1976, p. 54). Residents in the new communities who were least satisfied with their lives were unmarried persons, renters, apartment dwellers and those with below-average family incomes. Building on the conclusion of the U-M study, the UNC team explored QOL in additional ways. Survey respondents in both the new communities and the traditional communities were asked to express in their own words what was important to their lives. Not surprisingly, there were few differences in responses dealing with economic security, family life and health. However, there were significant differences between the two groups – new community residents were more likely to mention their engagement in leisure and recreational activities and the quality and accessibility of community facilities and services – two attributes of new towns.2 Finally, the impact of moving to both traditional and new communities on the quality of the residents’ lives was examined, with no differences found. Two-thirds of the residents from both types of communities said their lives had improved as a result of the move (Burby and Weiss, 1976, p. 387). Yet, when asked in what ways had their QOL improved, new town residents were most likely to mention that the move resulted in: ● ● ● ●

more conveniently located schools, doctors and other services; an improvement in health and medical facilities; more recreational opportunities with superior facilities, better shopping; and more attractive neighbourhoods.

Among the new community residents, married persons and those under 50 years of age were most likely to say their lives had improved as a result of the move. For low- and moderate-income families, blacks and the elderly, the new communities offered an improved living environment. Furthermore, women and older respondents were most likely to say they were ‘completely satisfied’ with life as a whole. Similar findings have also been reported for new arrivals to new towns and other new communities in Hong Kong (Vasoo, 1988), Australia (Houghton, 1976) and the Netherlands (Zhou, 2012). Studies in Other Western Countries New community and new town studies examining quality of community life were conducted in other countries with mixed results. While the availability of new housing and job opportunities were in many cases the primary attractions for residents, the planning concept and features of the new towns also contributed to positive feelings about the community as a place to live.

202  Handbook of quality of life research For example, an examination of community satisfaction in two working-class new communities revealed conflicting findings. In the self-contained new town development of Kwinana in Western Australia, residents expressed a high level of satisfaction (Houghton, 1976). On the other hand, Skärholmen – a new Swedish satellite community consisting of high-rise, high-density dwellings and supporting shopping facilities – was disliked by some residents. They expressed dissatisfaction with the overall design, which was viewed as ‘anti-human’, saying it lacked housing–job links, there was a prevalence of vandalism in the shopping centre, and that there was limited space and recreational opportunities for children (Gordon and Molin, 1972). In his review of the new town literature, Hillman (1975) indicates that, despite the criticism of Skärholmen by outsiders and some residents, others living there expressed satisfaction with the community. More recent studies in the new towns of Almere in the Netherlands (Zhou, 2012), Springfield Lakes in Australia (Rosenblatt et al., 2009) and Canberra – the planned capital of Australia (Nakanishi et al., 2013) – suggested high levels of community liveability, satisfaction and overall QOL. The Canberra study examined QOL in seven districts of the new city that were built at different stages of its evolution and according to different planning principles. Two districts consisting of older neighbourhoods were built according to the early garden city principles. Another four districts or neighbourhoods built in the 1960s and 1970s followed a more conventional new town model. Still another district built a few decades later used a new urbanism form of development. Using a QOL index consisting of five satisfaction domains, the study showed that the early garden city districts had a relatively high QOL, as did the new urbanism neighbourhood. Similar index scores were found in two districts representing the 1960s’ new town movement. However, two others built during this period had relatively low QOL index scores. In one of the weak districts, limited access to healthcare and social services and a prevalence of drug use were highlighted; while in the other, having a low QOL score, safety, housing affordability and access to healthcare and social services were important factors contributing to the lower QOL (ibid., p. 80). Studies in Asia Since the 1980s, several government-sponsored new communities have been built throughout Asia (Rowe, 2021; Yeh, 2021). A number have prompted researchers to examine living conditions and their impact on resident well-being (Eng, 1996; Foo, 2000; Lee and Yip, 2006; Omar, 2009; Vasoo, 1988; Zhou, 2012). Although these communities have been labelled as new towns, many – including those in Singapore and Hong Kong – were planned as satellite or large residential environments adjacent to older, established urban centres. Accordingly, they have yet to accommodate the necessities of urban life, such as entertainment and employment opportunities. Nonetheless, they do offer insights into how new town residents representing different cultures respond to their new living situations. In general, residential satisfaction is associated with the provision of high-quality community amenities. Most residents – especially those from low-income households – responded positively to their communities, citing improvements in services and facilities compared to where they previously lived. In cases where there were expressions of dissatisfaction – that is, Zhou’s (2012) study in Tongzhou, China – urban facilities were not available, and consequently, residents complained that the town lacked an urban vitality. Zhou (2012) concluded

Exploring quality of life in new towns: an overview  203 that urban amenities necessary to support housing should be in place in order to satisfy residents and improve their lives. In another study assessing the QOL in 13 Malaysian state-sponsored new towns, Omar (2009) identifies attributes of each community that were rated on a satisfaction scale by residents. The 22 attributes are then ranked from those that are most satisfying (that is, religious centres, electrical supply, telephone service, primary service, etc.) to attributes that are least satisfying (that is, commercial services for higher-order goods, taxi services, entertainment centres, children’s playgrounds, etc.). Finally, Omar assigned satisfaction scores to each new town and rank-ordered them from those having the highest QOL to those having the poorest QOL. Community satisfaction and those attributes contributing to it are equated with QOL, a misnomer that is prevalent in many of the studies examined.

LESSONS FROM THE LITERATURE Several conclusions can be drawn from the literature about the QOL in new towns and other large-scale residential developments: 1. Compared to life in conventional urban communities, residing in a new town is unlikely to have a major effect on one’s QOL. It is doubtful that new town living directly contributes to the financial well-being, marriage and family life, living standards, social life, or health and physical well-being of the individuals and households that live there. However, given the location of new towns with regard to employment centres, good transportation and proximity to nature and facilities (including medical and healthcare, recreation and leisure, and shopping) the QOL of new town residents is likely to be enhanced, all else being equal. For instance, there may be major health benefits accrued to children and older adults who live in close proximity to recreational and healthcare facilities and programmes. While such facilities are sometimes associated with conventional residential developments, they are more likely to be an integral part of new town design and development, creating a self-sufficient living environment. 2. People’s feelings about living in new towns often reflect their recognition that they live in a well-designed, planned community with an abundance of services and amenities. Positive comments were prevalent about living in a self-contained, mixed-use setting that is relatively new, particularly in new communities in the US but also among new town residents in other countries. 3. Health and other individual benefits that contribute to QOL may not be realised during the early stage of new town development when planned facilities and services are not yet fully operational or available. Much of the research dealing with community satisfaction suggests that a lack of facilities, particularly during the early phases of the community’s growth, diminishes community liveability. 4. Opportunities for social engagement and community participation in new towns vary depending on the nature of the physical layout of the community, the auspices under which the community is built and governed, and the characteristics of the residents themselves. Active social engagement is most likely to be found in places with homogeneous populations or among people with a strong sense of community (that is, community spirit).

204  Handbook of quality of life research Community cohesion is established through common interests and shared values rather than physical proximity. 5. Finally, resident satisfaction with the individual dwelling and the neighbourhood in new towns in the US is comparable to housing and neighbourhood satisfaction in conventional communities. Irrespective of country, the research on satisfaction with the residential environment indicates the primacy of dwelling over neighbourhood and community. The saying that ‘a man’s home is his castle’ is applicable in all cultures. Future Research Directions For the most part, new town research focusing on the residents’ lives were conducted during the early stages of new town development, whether in the UK, in the US or elsewhere. Considering that new town and new urbanism developments have expanded during the past few decades, particularly in countries other than the US, there are opportunities to learn more about the impacts of new community living on the residents’ health and well-being. Several directions for future research should be considered, each of which could incorporate elements of the others. Replication of the North Carolina research The seminal work in the US in the mid-1970s was reported as part an evaluation of the federally sponsored Urban Growth and New Communities Act passed by the US Congress in 1970 (Burby and Weiss, 1976). Some of the established new communities were reaching maturity while many were in their infancy and experiencing growing pains. Although there have been intermittent attempts to survey residents of some new towns since the University of North Carolina study was completed, such comprehensive studies of new town residents have not been made in nearly a half-century, either in the US or elsewhere. In addition to its scope, a strength of the North Carolina work was its research design and specifically the pairing of each planned new community, including new towns with nearby conventional residential developments of the same vintage. Indeed, the notion of comparative design and analysis from a research perspective gives greater validity when interpreting findings, particularly with respect to people’s assessments of their lives and the places they live. Although a contemporary replication of North Carolina work would be useful to academics, its potential value is in understanding more about the QOL of current residents and their responses to a more mature and ageing physical environment and social setting. Such research could determine whether the earlier conclusions from research conducted a half-century ago still hold while informing contemporary policymakers operating in several realms (that is, social welfare, public health, education, recreation, etc.), as well as planners and developers of new residential environments including new towns. Following new towns and new town residents over time The review of new town research indicates that taking a longitudinal approach is rare (see Bardo and Bardo, 1983). It would thus seem appropriate to re-examine the earlier new towns to determine how life has changed for long-term residents and how responses to new town living for each demographic cohort differ from comparable cohorts covered in the earlier studies. Similarly, longitudinal studies of residents living in new towns and emerging patterns

Exploring quality of life in new towns: an overview  205 of residential development (that is, new urbanism communities) should be undertaken. Further efforts to test assumptions regarding the effects of place on QOL are needed. Focus on youth and older adult populations Consideration should also be given to examining the health and well-being of individuals living in new towns for an extended period. Focus on people at both ends of the age spectrum would be of particular value. Long-time residents could be asked to reflect retrospectively on the following questions: ● How did growing up in a new town influence their childhood and teen years? ● How did place and behavioural patterns at an earlier time contribute to their current state of well-being? ● Are residents more appreciative of nature and living in a planned residential environment than people who grew up in more conventional communities? ● Do people’s early childhood experiences influence their QOL in terms of long-term friendships and current social networks, career paths and patterns of leisure? ● Has growing up in a relatively diverse socio-economic and ethnic setting had a bearing on their political and social outlook, including their views about different racial and ethnic groups? Longitudinal studies could also be conducted to track a sample of residents to assess lifestyle changes, housing histories and people’s perceptions and evaluations of the socio-physical setting. Motivating factors for moving away from their original new towns would shed light on the inability of the new town to accommodate the changing needs of individuals or the family unit. Older people living in new towns might be asked: (1) how have their lives changed over time and (2) to what extent has the socio-physical environment contributed to or hindered successful ageing? Successful ageing has been described as having three key components: ● a low risk of disease and disease-related disabilities; ● maintenance of high mental and physical functioning; and ● continued engagement with life including relations with others and productive activity – either paid or volunteered (Rowe and Kahn, 1997, 1998). These components are related to overall well-being and QOL, which can be influenced by the physical environment including its socio-economic make-up and its human-made and natural features. Expectations of moving to new towns Elsewhere in the US, prospects of mass migration and the building of new towns for populations displaced by sea-level rise, hurricanes, forest fires and extreme heat resulting from climate change have recently been discussed (Godschalk, 2021; Khanna, 2021). Building on a National Research Council report dealing with coastal risks to the East Coast and Gulf Coast of the US, Godschalk suggests building a series of new towns inland to accommodate vulnerable coastal populations. While he recognises that the idea is radical, ambitious and complex in its implementation, he offers a compelling case while outlining clear advantages to this adaptive approach to sea-level rise. Among the advantages is the ‘involvement of publics and

206  Handbook of quality of life research stakeholders to ensure the goals, objectives, and procedures of relocation and new town development are widely understood and that the wisdom and insights of the citizens and leaders are incorporated into adaption plans’ (Godschalk, 2021, pp. 403–4). This statement raises a series of questions that can be addressed through QOL research focusing on prospective new town residents. For example: ● What are the expectations of those households to be relocated in terms of lifestyle changes that may affect their QOL? ● To what extent do they see the move to a new planned community influencing the mental and physical health of family members? ● In what ways do they anticipate that the relocation will improve the lives of household members? While these types of questions may inform the planning and development process for new towns, they also suggest the need for post-relocation research to inform future new town planning. Exploring physical attributes contributing to community satisfaction and health The conceptual models proposed by Campbell et al. (1976) and Marans and Rodgers (1975) suggested how perceptions and evaluations of the socio-physical environmental attributes (that is, recreational facilities, schools, shopping, landscaping, etc.) could collectively contribute to the understanding of QOL and particularly community satisfaction. The models also suggest that the perception and evaluation of each attribute vary depending on the characteristics of the attribute. Longitudinal studies focusing on the role of new towns in contributing to the QOL of their residents would create opportunities to test and modify the model. Ideally, such studies would focus on residents prior to their moving into the new town and periodically after residency is established. At each period, measures would be made of their perceptions and assessments of various attributes in their surroundings, and the objective measures of the attributes themselves. An early effort to measure both people’s feelings and the objective reality in the context of new towns and paired conventional communities was reported as part of the North Carolina study (Burby and Weiss, 1976; Zehner, 1977). In addition to the comprehensive survey of residents, researchers inventoried commercial, recreational, healthcare and transportation facilities within each community. For the most part, new communities had many amenities, particularly recreational and leisure facilities. The objective measures provide one indicator of community liveability. Residents’ perceptions also matter (Zehner, 1977, p. 54). Quantity, however, is an insufficient indicator of resident satisfaction. Other facility attributes would need to be taken into account as measures for resident assessments in subsequent new town studies. Further research will also be needed to determine aspects of the community and its neighbourhoods that create a sense of community. Similarly, consideration should be given to examining associations between the physical attributes of new towns and the physical health of its residents.

Exploring quality of life in new towns: an overview  207

A NEW PROGRAMME FOR QOL RESEARCH ON NEW TOWNS IN CHINA Because of China’s extensive programme of new town development and its urbanisation plan – including the relocation of populations from rural to urban areas – there are opportunities to pursue QOL research incorporating some of the above suggestions. In this last section of the chapter, we present an overview of recent new town development in China and the need for a long-term programme of research to understand QOL effects on impacted populations. A discussion of China’s urbanisation plan and related resettlement programme is first discussed followed by a review of a pilot study designed to explore migrants’ QOL to inform the development of a long-term (that is, longitudinal) programme of QOL research. Background Rapid urbanisation has been a key to sustained economic growth in China. However, urbanisation has also brought challenges to the natural environment and population health. In 2014, China’s central government launched the National New-type Urbanization Plan. The plan advocates a new ‘people-centred’ and ‘pro-environment’ approach to coordinate urbanisation and migration. The plan anticipates relocating 100 million people living in rural areas to newly developed urban communities (Tang et al., 2016; Wilmsen, 2017; Xu et al., 2016). The Shaanxi Provincial Government’s (SPG) ten-year Qinba Mountain Area (QMA) resettlement programme falls under this larger national plan. The QMA programme intends to move 2.4 million residents from impoverished and ecologically fragile rural areas to new settlements in urban peripheries. The initiative aims to accelerate urbanisation, restore the natural environment, alleviate poverty and improve overall QOL in the region. Promotion of migrants’ health and well-being as a priority of the resettlement programme resulted in an increase in the number of studies on this topic (for example, Wang et al., 2013; Xi, 2016; Xue et al., 2015). However, these studies provided contradictory conclusions and offered limited guidance for policy and planning. Specifically, inconsistences in results on health-related topics reflected limitations in the research designs and the scope of the investigations, as noted below. Characteristics of the programme investigated in the studies may be summarised as follows: 1. First, studies of the programme’s health effects have primarily focused on the psychological well-being of the resettled population with a particular focus on reactions to socio-economic change (that is, Hwang et al., 2007; Qin et al., 2010; Xi, 2016; Wang et al., 2013). People’s adaptation and reactions to changes in the natural and built environment, however, were rarely considered. These specific aspects of environmental change are likely to impact every aspect of life, including people’s physical activity patterns, diet, social interactions and exposure to pollutants/contaminants. Consequently, the implications of environmental change on physical health as well as psychological well-being as sub-domains of QOL need to be better understood before planners and policymakers can effectively address migrants’ QOL holistically. 2. Second, a holistic assessment of QOL outcomes among the residents impacted by the resettlement programme’s was missing. Definitions of QOL and corresponding measurement scales varied between studies, which often led to conflicting conclusions. Additionally,

208  Handbook of quality of life research spillover effects of resettlement programmes on the QOL of non-resettled populations such as extant urban residents and to-be-resettled rural residents were ignored in previous research. 3. Third, regarding research design issues, the majority of studies only included assessments immediately after the resettlement. This cross-sectional study design only captured a snapshot of the circumstances and experiences of the migrants. The long-term effects of rapid urban expansion, redistribution of the labour force and continuous environmental transformation on the region and its population remain unclear. Overview of a New Research Initiative In 2016, the first author of this chapter was asked to discuss QOL in the context of new towns for a conference to be held at Harvard (Peiser and Forsyth, 2021). As part of that effort, it was decided to address future research needs for contemporary new towns and those currently being developed throughout the world. The talk and the eventual chapter (see Marans and Xu, 2021) sparked interest among officials within the Chinese Academy of Engineering (CAE) and SPG. The SPG was working with the Institute on Urban Planning at the Xian University of Architecture and Technology (XUAT) in planning new residential developments for migrants in the Shaanxi province’s QMA). Interest in the draft chapter led to conversations between the CAE, XUAT and researchers at U-M, including the authors of this chapter. The conversations considered the development of a programme of research to understand factors linked to QOL among populations impacted by the resettlement programme in the QMA. Specifically, guidance was sought on the use of social science research methodologies to conduct the research. This resulted in a pilot study in the QMA to be led by XUAT that included a sequential exploratory mixed methods approach (Fetters et al., 2013) beginning with focus groups and followed by a survey. The U-M team assisted in several ways, including the analysis of the focus group data. They also provided guidance on survey development, the training of survey interviewers from XUAT and analysis of survey data. The pilot study had three primary objectives to: ● identify specific aspects of the environment (for example, built, natural, socio-economic) most likely to impact QOL of the migrant population; ● develop, test, implement and evaluate survey protocols for systematic survey data collection; and ● analyse and interpret baseline pilot data and report findings to local government officials and planners. Methodology The pilot study used mixed methods, specifically an exploratory sequential design, which included a qualitative phase (focus groups) followed by a quantitative phase (survey) (Fetters et al., 2013).

Exploring quality of life in new towns: an overview  209 Focus groups In 2017, researchers from XUAT conducted five focus groups in three resettlement communities. The focus groups ranged in size from 10 to 14 participants, who were aged between 27 and 75 years old. Focus group questions were intended to learn about migrants’ QOL concerns before and after moving, their overall resettlement experiences and their expectations for the future. Participants were asked questions about their general satisfaction with their new community as well as domain-specific questions (that is, satisfaction) with the built and natural environments, lifestyle, health, family, social environment, work and transportation. Focus group discussions were recorded, transcribed and translated from Mandarin to English. Analysis followed a grounded theory approach that started with open coding to identify themes. Next, axial coding (that is, linking specific passages of text to themes) was conducted. Pilot survey Based on results from the focus groups, a survey was developed. Prior to survey administration, the U-M team worked with XUAT researchers to finalise the survey questionnaire and train faculty and staff in survey administration. In June 2018, the survey was administered in person by XUAT interviewers with 100 residents living in a typical QMA resettlement community. At the time, the community contained more than 6000 dwellings for resettled households along with industry, schools, hospitals, commercial facilities and transportation centres. The first phase of development was completed in 2015 with the first group of residents having moved in during 2016. Analysis of the data included descriptive statistics (frequencies, means and standard deviations) to determine the extent of variability of all survey questions. This was followed by bivariate (correlations) and multivariate (regression) analyses to test hypotheses and identify important concepts to be addressed in future research. Results Outcomes of the focus groups Multiple themes emerged from the focus group data. Some themes were more common than others, which helped provide salient factors in resettlement communities related to QOL. The findings are summarised below: 1. When participants were asked about their general satisfaction with their new community, the most commonly raised themes included aspects of the built environment, followed by family and then mobility/transportation. 2. When discussing the built environment, participants specifically talked about three different levels – their housing unit/apartment, the building where it was located and the surrounding neighbourhood. Specific issues related to the housing unit included the amount of physical space, the layout, amount of natural light, the temperature and sanitary conditions. In terms of the building, participants talked about the quality and safety of the construction, security and stairs. 3. When discussing the neighbourhood and community, participants talked about security, open spaces for gathering, green spaces, roads and parking, access to facilities and amenities (that is, shopping, schools, healthcare), the distance between buildings, waste management, walkability and nearby employment opportunities.

210  Handbook of quality of life research 4. When discussing family, participants talked about household structure and composition, grandchildren and the well-being of older family members. 5. Last, participants talked about convenience of transportation options in their new community. The survey outcomes There were several key findings from analysis of the survey data. 1. First, migrants’ overall QOL was most strongly associated with perceived quality of the built environment in the new community. Specifically, respondents’ assessment of the community environment, including the surrounding area and amenities provided at the township level, was most significant in contributing to QOL. 2. Second, migrants reported benefiting most from improvement in access to transportation and education resources for children compared to their prior living environment. 3. Third, migrants reported that their levels of physical activity had declined, and time spent being sedentary increased since moving to the resettlement community. This suggested additional interventions were needed in the new communities to encourage and facilitate physical activity and promote long-term positive health. 4. Fourth, stress and experiencing depressive symptoms were prevalent in the resettlement community. These mental health issues were largely associated with an increased cost of living in the resettlement community and reduced household income. Summary of the Findings In summary, results from both the qualitative and quantitative analyses suggested the following: 1. First, resettled households experienced improvements in their QOL after moving to the new community. 2. Second, the salience of the built environment for QOL was observed at multiple levels. 3. Third, the concept of a resettlement migration community is ambiguous. This may be due to the fact that the existing stock of new resettlement communities in the QMA varied greatly with regard to location, size, infrastructure, land use, housing type and provided amenities. The pilot study observed three major community types: residential-only urban settlement, large-scale new town development in suburbs, and residential-only rural settlement. Proposed Long-term Programme of QOL Research The pilot study in China discussed above was viewed as the first step in a long-term programme of research aimed at understanding the effects of environmental change on QOL of the impacted migrant populations, which includes: ● migrants who have moved from rural areas to new urban developments; ● the extant rural population, some of whom may eventually become migrants; and ● the urban populations living in established cities and towns near the new developments.

Exploring quality of life in new towns: an overview  211 In this work, environmental change was conceptualised to be multifaceted, including the built environment (for example, housing, regional setting, transportation) and natural environmental aspects (for example, air quality, temperature, water), as well as the socio-economic environment (for example, employment opportunities, social justice, culture, social integration, safety). The programme of research was intended to produce data that informs public policy and planning in the Shaanxi Province and elsewhere in China about the benefits and consequences of the resettlement programme. To address research limitations noted earlier, a long-term QOL research programme in the QMA was proposed. That programme would: ● move beyond the often-examined clinical health-related outcomes and include an investigation of the associations between these outcomes and multifaceted aspects of environmental change; ● monitor the ongoing environmental changes and assess both their short- and long-term impacts on QOL among the impacted populations; ● assess the sustainability of the overall resettlement programme; and ● provide essential evaluative feedback to urban planners and government officials that can be used to adjust the programme in the future. A critical next step in the development of this long-term research programme would be creating resettlement community typologies based on the following community characteristics: ● ● ● ● ● ● ● ●

development status (competed, ongoing, planned); number of building units currently built (and/or planned); number of individual housing units; size of the units; occupancy status; demographics of current population living in the communities; design features (for example, number of floors of building); neighbourhood and community resources (for example, outdoor gather space(s), community centre, exercise equipment, etc.); and ● location of the community relative to other existing cities and townships (for example, rural area, urban periphery). These typologies could then be used to develop a sampling frame and ensure that sufficiently sized samples from each community type are invited to participate in repeated surveys assessing and measuring holistic QOL. To date, plans for moving forward with this comprehensive programme of QOL research in China have not been realised largely because of the emergence of the global pandemic in early 2020. The future of the programme remains uncertain as to when and whether it will resume.

OVERVIEW This chapter began with a review of research covering the QOL of residents in new towns and other large planned residential developments. Recognising that much of that research occurred early in the life of these places, it offered suggestions for future programmes of

212  Handbook of quality of life research research, including the need to understand the effects of person and place characteristics on the well-being and QOL of residents. Finally, the chapter discussed plans for a comprehensive research programme of QOL research in China, including a pilot study leading to what was hoped to be a long-term programme of research designed to understand the impact of people moving from rural to new urban developments including new towns.

NOTES 1.

The authors acknowledge the work of Dr Ying Xu, a postdoctoral research fellow at the University of Michigan from 2016 to 2019. Her efforts at (1) identifying early quality of life studies in new towns (see Marans and Xu, 2021); (2) assisting in developing a new town research programme for China including the pilot study discussed in this chapter; and (3) training colleagues at Xian University of Architecture and Technology in best practices to implement the pilot study, are gratefully appreciated. 2. New community residents were also more likely than residents in conventional communities to mention social relations as an important factor contributing to their QOL.

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Exploring quality of life in new towns: an overview  213 Marans, R.W. and Rodgers, W. (1975), ‘Toward an understanding of community satisfaction’, in A. Hawley and V. Rock (eds), Metropolitan America in Contemporary Perspective, New York: Halsted Press, pp. 299–352. Marans, R.W. and Xu, Y. (2021), ‘Quality of life in new towns: what do we know and what do we need to know?’, in R. Peiser and A. Forsyth (eds), New Towns for the Twenty-First Century: A Guide to Planned Communities Worldwide, Philadelphia, PA: University of Pennsylvania Press, pp. 43–56. Nakanishi, H., Sinclair, H. and Lintern, J. (2013), ‘Measuring QOL: an integrated evaluation of built environment’, in S. Geertman, J. Stillwell and F. Toppen (eds), Proceedings of the 13th International Conference on Computers in Urban Planning and Urban Management, https://​cupum2013​.geo​.uu​.nl/​ download/​usb/​contents/​pdf/​shortpapers/​70​_Nakanishi​.pdf. Omar, D.B. (2009), ‘Assessing residents’ QOL in Malaysian new towns’, Asian Social Science, 5, 1911–2017. Peiser, R. and Forsyth, A. (eds) (2021), New Towns for the Twenty-First Century: A Guide to Planned Communities Worldwide, Philadelphia, PA: University of Pennsylvania Press. Qin, J., Yuan, C.-H. and Liu, B.-F. (2010), ‘Health status and its related factors among Three Gorge migrants in Shandong Province’, China’s Public Health, 9, 1138–9. Rosenblatt, T., Cheshire, L. and Lawrence, G. (2009), ‘Social interaction and sense of community in a master planned community’, Housing, Theory and Society, 26, 122–42. Rowe, J. and Kahn, R. (1997), ‘Successful aging’, The Gerontologist, 37, 433–40. Rowe, J. and Kahn, R. (1998), Successful Aging, New York: Pantheon/Random House. Rowe, P.G. (2021), ‘New towns in East and Southeast Asia’, in R. Peiser and A. Forsyth (eds), New Towns for the Twenty-First Century: A Guide to Planned Communities Worldwide, Philadelphia, PA: University of Pennsylvania Press, pp. 119–51. Sykes, A.J.M. and Livingstone, J.M. (1970), East Kilbride 70, An Economic and Social Survey, Glasgow: University of Strathclyde. Sykes, A.J.M., Livingstone, J.M. and Green, M. (1967), Cumbernauld 67: A Household Survey and Report, Glasgow: University of Strathclyde. Tang, S., Hao, P. and Huang, X. (2016), ‘Land conversion and urban settlement intentions of the rural population in China: a case study of suburban Nanjing’, Habitat International, 51, 149–58. Town and Country Planning Association (TCPA) (1989), Proceedings of the TCPA Annual Conference on British Towns and Quality of Life, London: TCPA. Vasoo, S. (1988), ‘The development of new towns in Hong Kong and Singapore: some social consequences’, International Social Work, 31, 115–33. Wang, Z., Ma, J. and Liu, L. et al. (2013), ‘Quality of life of the ecological migrants in the Ningxia Province’, Modern Preventive Medicine, 40, 1279–81. Willmott, P. (1962), ‘Housing density and town design in a new town: a pilot study at Stevenage’, The Town Planning Review, 33, 115–27. Willmott, P. (1964), ‘East Kilbride and Stevenage: some characteristics of a Scottish and an English new town’, Town Planning Review, 34, 307–16. Willmott, P. (1967), ‘Social research and new communities’, Journal of the American Planning Association, 33, 387–97. Wilmsen, B. (2017), ‘Damming China’s rivers to expand its cities: the urban livelihoods of rural people displaced by the Three Gorges Dam’, Urban Geography, 39, 345–66. Xi, J. (2016), ‘Types of integration and depressive symptoms: a latent class analysis on the resettled population for the Three Gorges dam project, China’, Social Science and Medicine, 157, 78–86. Xu, M., He, C., Liu, Z. and Dou, Y. (2016), ‘How did urban land expand in China between 1992 and 2015? A multi-scale landscape analysis’, PLOS ONE, 11, Article e0154839. Xue, L., Kerstetter, D. and Buzinde, C.N. (2015), ‘Residents’ experiences with tourism development and resettlement in Luoyang, China’, Tourism Management, 46, 444–53. Yeh, A.G.O. (2021), ‘Successes and failures of new towns in Hong Kong’, in R. Peiser and A. Forsyth (eds), New Towns for the Twenty-First Century: A Guide to Planned Communities Worldwide, Philadelphia, PA: University of Pennsylvania Press, pp. 182–99. Zehner, R.B. (1977), Indicators of the QOL in New Communities, Cambridge, MA: Ballinger. Zhou, J. (2012), Urban Vitality in Dutch and Chinese New Towns. A Comparative Study Between Almere and Tongzhou, Scotts Valley, CA: CreateSpace Independent Publishing Platform.

14. The influence of urban layout on perceived residential quality in a Costa Rican suburb Helga von-Breymann and Esteban Montenegro-Montenegro

BACKGROUND Urbanisation in Latin America and the Caribbean grew rapidly during the second half of the twentieth century and is continuing. This transformation, accompanied by low-density urban sprawl, has fostered increased social inequities and poverty, fragmentation of urban spaces, residential segregation and other pathologies associated with rapid urban development. It has also impacted the liveability of cities, creating socio-spatial and environmental deficiencies (Inostroza et al., 2013; Schneider and Woodcock, 2008). Examples include difficulties in accessing urban amenities, inadequate and inefficient public services and infrastructure, and the loss of vitality in public spaces, all of which influence the quality of life (QOL). In Costa Rica, the building of gated communities (‘closed neighbourhoods’) – especially in and around San José, the capital city – exacerbates those inequalities. These enclaves represent a ‘type of urbanism that reinterprets the meaning and form of community’ (Irazábal, 2006, p. 75) and have changed the layout of cities, how their public spaces are used and how diverse segments of the population interact with one another (Caldeira, 2000). There is a scarcity of public open areas in established residential neighbourhoods and difficulties in accessing urban amenities. Studies have shown the importance of locational characteristics as indicators of neighbourhood quality (Connerly and Marans, 1988). The characteristics of immediate surroundings of residential areas and the broader urban environment influence people’s well-being (Brereton et al., 2008; Krellenberg et al., 2014). However, in Latin America, few studies have explicitly examined the impact of location and quality of public services on people’s satisfaction with their residential environment (Basolo and Strong, 2002). Accessibility to urban facilities – including public spaces, services and commerce – are key elements in what is considered a good location, since those assets are essential to satisfying people’s daily needs. Close proximity to urban or town centres with these elements is critical to achieving a good residential environment for their inhabitants. For several decades, understanding the importance of environmental features for people’s QOL has been a major topic of research in urban studies and planning (Marans and Stimson, 2011). Furthermore, different aspects of urban settings have been shown to impact people’s lifestyles and social interactions (Hillier, 2004; Janoschka, 2002; Matsuoka and Kaplan, 2008). Research has determined possible relationships between nature, public spaces and other urban facilities, with a higher QOL for individuals in the urban context, as well as identifying the significant psychological and social benefits to human societies (Brereton et al., 2008; Carrus et al., 2013; Chiesura, 2004; Delavari-Edalat and Abdi, 2010; Hartig et al., 2003; Ríos-Rodríguez et al., 2021). 214

The influence of urban layout on perceived residential quality: Costa Rica  215 In Central America, few studies have investigated the importance of accessibility to urban amenities and services, and the effects of accessibility on people’s living conditions. However, some have addressed the problem of increasing construction of gated communities and ‘closed neighbourhoods’ and its impacts on habitability (Barrantes, 2019; Handal and Irazábal, 2022). This chapter examines the effects of urban layout on resident’s perceptions of their neighbourhoods in Flores, a small urban area located about 15 kilometres north of San José in Costa Rica. The study seeks to understand how living in gated communities, low-density and fragmented areas of suburbs affects people’s perceptions of their residential environment. The chapter also considers how housing density and proximity to town centres with their public spaces influence perceptions of residential quality. To achieve these aims, different residential characteristics and housing conditions were grouped together as ‘housing developments’ (A) and ‘non-housing developments’ (B). In addition to analysing the physical characteristics of these two housing groups, a questionnaire was administered to residents to measure their perceptions of their own residential environmental quality. The chapter first discusses perceived residential environmental quality indicators. It then describes the residential environment where data were collected and the methodology used. The importance of proximity to town centre and public spaces and the impact of housing density within different housing groups are discussed. Finally, the implications of urban design of new urban areas for QOL is addressed.

PERCEIVED RESIDENTIAL ENVIRONMENT QUALITY INDICATORS In evaluating the quality of urban life (QOUL), it is important to understand differences between objective characteristics of the built environment and the subjective evaluations people make about living within the environment. Some studies have evaluated QOL based solely on objectively quantifiable data. Those studies identify measurable variables, such as geographic or physical aspects of the environment. Other studies rely on perceptual data that often do not relate to the objective characteristics of place. The latter deal with measures such as people’s observations or perceptions of the environment (Fornara et al., 2010). How people process their residential environment mentally has also been operationalised through different indicators of perceived residential quality (Amérigo, 1995). That has been a successful strategy to evaluate residential satisfaction and QOL. The perceived residential environment quality indicators (PREQI) is a set of measures designed to focus on multiple perceptions of the environmental quality of residential areas. According to Fornara et al. (2010), the four macro-evaluative dimensions of the residential environment this instrument measures are: the spatial, the human, the functional and the contextual dimensions: ● ● ● ●

the spatial aspect relates to architectural and urban planning variables; the human concerns people and social relationships; the functional has to do with facilities and services; and the contextual are those concerning environmental health, maintenance and pace of life.

216  Handbook of quality of life research The PREQI have been applied in different countries and have demonstrated strong validity (Mao et al., 2015; Sam et al., 2012; von-Breymann and Montenegro-Montenegro, 2019). Cross-cultural validation of such instruments has helped strengthen new research in this area. The Costa Rica study is intended to fill a void within the literature on this topic in Latin American countries to better understand the importance of urban layout and access to urban facilities in assessing QOL. Local governments in Latin America often carry out studies of residential environments. Yet those studies, along with objective evaluations of the built environment by technicians, urban planners or politicians, frequently do not reflect the entire reality of QOL. Instruments such as the PREQI therefore have an important role to play in urban planning practice. They can help provide a different perspective on people’s necessities and yearnings. Likewise, they can reinforce and humanise urban planning and design procedures, resulting in the creation of environments that are liveable and rewarding.

THE CONTEXT AND STUDY AREA Context In Costa Rica, as in other Latin American countries, the economic crisis of the 1980s affected rural areas, triggering massive migration to urban regions (Montoya, 2009). As a result, private developers took advantage of the economic reforms of the 1990s and weakened governmental control of urban development. As in other regions, residential developments gained interest as marketable products (Janoschka, 2002), and gated communities became one of the most extended and consumed ‘urban products’ in Costa Rica. Urban growth in Costa Rica has increased by more than 3 per cent annually since 1970. Between 1984 and 2000, it reached 4.6 per cent (Comisión Económica para América Latina y el Caribe [CEPAL], 2016). That growth was characterised by suburban expansion, which transformed much of the rural landscape into one containing detached houses and gated communities. However, regardless of existing urban plans, rapid urbanisation was not well regulated. In 2014, three-quarters of the country’s population lived in urban areas (United Nations, 2015) and the density in these areas was just 26 inhabitants per hectare, the lowest in Latin America. Yet, the biggest problem was that urbanised land had traditionally been used inefficiently. Only a few people were able to enjoy infrastructure and services within each hectare (Banco Interamericano de Desarrollo [BID], 2016; Shlomo et al., 2012). The Study Area A small canton in the province of Heredia called Flores was selected for study. For decades, Flores was characterised by coffee plantations. Flores is a low-density suburban canton with a population of over 20 000 (Instituto Nacional de Estadística y Censos [INEC], 2012), distributed in three districts, each of which has its own centre with a plaza and church. Several urban facilities and shops surround these centres. This represents the traditional urban layout inherited from the Laws of the Indies, which were used by the Spanish Crown in Latin American colonies in creating new towns (De Terán, 1999; Quesada, 2007). These ‘town

The influence of urban layout on perceived residential quality: Costa Rica  217 centres’ typically contain most of the urban services and commerce in an area and cater to the needs of the surrounding residents. Flores canton was selected because of its residential development pattern consisting of: (A) relatively new housing complexes including gated communities; and (B) individual dwellings developed independently. The first development pattern (A) refers to clusters of planned residential buildings and includes groups of single-family homes in a gated or semi-gated community, multifamily apartment blocks and low-income housing projects. These developments are planned and follow certain regulatory rules in terms of the number of green areas, communal services and infrastructure. The second residential development pattern (B) consists of spread-out forms of single-family homes located outside a planned cluster of residential buildings. These are individual dwellings that were built in areas not requiring specific regulations. Figure 14.1 shows the location of the town centre, parks and other urban facilities in Flores. It also maps out a general distribution of the residential areas within the limits of the canton.

Source: The authors.

Figure 14.1

Flores canton map

218  Handbook of quality of life research Methodology and Analysis The sample A probability sample survey collected information from 227 women and 164 men over the age of 18 who had lived in their dwelling for at least three months. The mean age of respondents was 43 years old and ranged from 18 to 95. Sixty per cent were married or living with a partner. Participants lived in the district for 25 years on average, and nearly a third (29 per cent) participated in an association or group in their community. The self-administered questionnaire asked participants about different aspects of their residential area. Using geographical information systems (GIS), the location of the participants’ residency was geocoded, as was the location of green areas, recreational and cultural facilities, churches and community centres. The distance between each participant’s residence and the nearest town centre was also measured using GIS. The PREQI instrument The study used a modified version of the PREQI instrument (von-Breymann and Montenegro-Montenegro, 2019). During development and validation of the modified version, the term ‘neighbourhood’ used in several questions was misunderstood. This was attributed to cultural interpretation of the concept in the context of a peripheral residential area in Latin America. Consequently, the term ‘district’ replaced ‘neighbourhood’ and was used in the questionnaire. The modified PREQI measured ten different factors with a total of 55 items presented on a five-point Likert scale instead of using the original seven-point Likert response options used by Fornara and colleagues (2010). The five-point scale ranges from ‘strongly disagree’ to ‘strongly agree’. Table 14.1 lists the item means and standard deviations (SD) that compose 17 ‘parcels’. A ‘parcel’ was defined as an aggregated observed indicator – the sum or mean of two or more observed items explained by a latent factor (Little et al., 2002). ‘Parcels’ allow the estimation of a more parsimonious model and are frequently used when the analysis focuses on structural parameters, such as latent means, latent covariances and regression paths (Little, 2013). Given that the PREQI has a large number of items, this strategy was used to fit a more parsimonious model, where the goal was to evaluate regression paths between groups. Structural equation modelling Structural equation modelling (SEM) was used to examine the PREQI factors listed in Table 14.1, modelled as latent constructs. Confirmatory factorial analysis First, a two-group confirmatory factorial analysis (CFA) model was established that included all 55 PREQI items. For the sake of model parsimony, parcelled indicators for all latent factors were created (see von-Breymann and Montenegro-Montenegro, 2019). As shown in Table 14.1, the factors modelled from the PREQI (see Figure 14.2) were: ● architecture and urban space; ● mobility, transport and connectivity; ● green areas;

The influence of urban layout on perceived residential quality: Costa Rica  219 Table 14.1

Modified PREQI factor and parcel means and standard deviations

Factors, Parcels and Questionnaire Items

Mean

SD

Buildings are beautiful in this district (N = 390)

3.65

0.93

It is pleasant to see this district (N = 390)

3.91

0.91

Buildings have unpleasant colours in this district (N = 388)

3.04

0.85

Architecture and urban space factor  Building aesthetics parcel (three items):

 Urban form parcel (three items): Buildings are too close together in this district (N = 390)

2.93

1.06

The volume of buildings is too big in this district (N = 387)

3.35

1.00

Buildings are too tall in this district (N = 390)

3.88

0.83

Parked cars impede walking in this district (N = 393)

2.88

1.30

There is a sufficient number of parking spaces in this district (N = 390)

2.48

1.16

It is easy to cycle around in this district (N = 392)

3.08

1.26

The city centre can be easily reached from this district (N = 388)

3.58

1.18

The district is well connected with important parts of the city (N = 390)

4.00

0.83

The district is too cut off from the rest of the city (N = 391)

3.95

0.89

Mobility, transport and connectivity factor  Internal functionality parcel (three items):

 External connections parcel (three items):

 Transportation services parcel (three items): In this district, public transport provides good connections with the rest of the city (N = 394)

3.60

1.16

In this district, the frequency of public transport is adequate for residents’ needs (N = 393)

3.27

1.25

Bus and train stops are well distributed around the district (N = 394)

3.40

1.07

There are green areas for relaxing in this district (N = 391)

2.82

1.25

There are enough green areas in this district (N = 393)

2.58

1.18

Green areas factor  GA1 parcel (two items):

 GA2 parcel (two items): Going to a park means travelling to other parts of the city (N = 388)

2.88

1.27

In this district, green areas are in good condition (N = 391)

2.97

1.15

You can meet bad people in this district (N = 391)

2.82

1.08

Acts of vandalism happen in this district (N = 389)

2.99

1.09

Here at night, there is the risk of dangerous encounters (N = 391)

2.71

1.08

Socio-relational features factor  Security parcel (three items):

 Discretion parcel (three items): People gossip too much in this district (N = 385)

2.70

1.06

In this district you feel watched (N = 390)

2.99

1.09

In this district people are not intrusive (N = 391)

3.06

1.03

In this district it is difficult to make friends with people (N = 387)

3.49

0.90

In this district it is easy to get to know people (N = 388)

3.41

0.96

In this district people tend to be isolated (N = 389)

3.25

0.98

 Sociability parcel (three items):

Social provisions factor  School services parcel (three items): This district has good school facilities (N = 389)

4.02

0.85

Schools can be easily reached on foot in this district (N = 389)

4.12

0.76

Schools are generally good in this district (N = 390)

3.86

0.88

3.38

0.95

 Social care services parcel (three items): Social services are inadequate in this district (N = 388)

220  Handbook of quality of life research Factors, Parcels and Questionnaire Items

Mean

SD

Elderly care services are lacking in this district (N = 390)

2.71

1.14

The local health service in this district is inadequate for residents’ needs (N = 390)

3.06

1.18

You can do various sports in this district (N = 394)

3.26

1.14

This district is well equipped with sports grounds (N = 392)

2.61

1.12

There are areas where you can do outdoor sports in this district (N = 394)

3.34

1.11

Recreational services factor  Sport services parcel (three items):

 Socio-cultural activities parcel (three items): Entertainment activities for residents are lacking in this district (N = 387)

2.69

1.08

In the evening, this district offers various attractions (N = 393)

2.12

0.95

This neighbourhood is not well equipped to host cultural events (N = 389)

2.94

1.20

Commercial services factor  CS1 parcel (two items): There are all kinds of stores in this district (N = 393)

3.35

1.16

Anything can be found in the district’s stores (N = 388)

3.01

1.18

This district is well served with stores (N = 393)

3.17

1.15

Stores are not well distributed in this district (N = 391)

3.00

1.08

There is a calm atmosphere in this district (N = 394)

3.84

0.89

This district is still liveable if compared with the chaos of other areas (N = 393)

4.15

0.77

Living in this district is quite distressing (N = 393)

3.93

0.86

The air is clean in this district (N = 393)

3.51

0.97

This district is generally not polluted (N = 393)

3.25

1.02

 CS2 parcel (two items):

Pace of life factor

Environmental health factor  EH1 parcel (two items):

 EH2 parcel (two items): This is a noiseless district (N = 393)

3.21

1.08

Residents’ health is threatened by pollution in this district (N = 389)

3.48

1.05

Streets are regularly cleaned in this district (N = 394)

3.16

1.12

Road signs are well kept in this district (N = 393)

3.30

1.03

Residents show care for their district (N = 394)

3.30

1.02

There are too many potholes in the district’s streets (N = 394)

3.20

1.08

Upkeep factor  U1 parcel (two items):

 U2 parcel (two items):

Source: The authors.

● ● ● ● ● ● ●

socio-relational features; social provisions; recreational services; commercial services; pace of life; environmental health; and upkeep.

Figure 14.2 shows the factor loadings and residual variances from the configural model. Correlations between latent factors are not presented for the sake of illustration simplicity. After creating the ‘parcels’, the research then evaluated the assumption of invariance between the two groups: residents living inside housing developments; and residents living

The influence of urban layout on perceived residential quality: Costa Rica  221

Note: The correlations between latent factors are not presented for illustration simplicity. Source: The authors.

Figure 14.2

Configural model between groups

in a single independent dwelling outside of a housing development. The assumption of invariance was tested by fitting three nested models (Cheung and Rensvold, 2002; Little, 1997, 2013; Meade et al., 2008). 1. The first model evaluated the factorial structure by housing condition and did not have any relevant parameter constraints. 2. The second model imposed equality constraints on factor loadings (Little, 1997; Little and Card, 2013). The model aimed to determine whether loading equivalence can be assumed in both groups. 3. The third model evaluated the equality of intercepts between groups by constraining the intercepts between groups to be equivalent. The nested models were evaluated and compared using the difference in Comparative Fit Index (CFI). A difference larger than 0.01 is considered large and attributed to the lack of invariance (Cheung and Rensvold, 2002). Parameters such as latent means, latent variances

222  Handbook of quality of life research and latent covariances were evaluated between groups using a likelihood ratio test (LRT). The aim was to examine the effect of housing conditions on these parameters. The effects of distance from town centres and housing density on the latent factors were evaluated. The relationship between these two variables was also tested with the latent factors being moderated by the type of residential condition (inside/outside housing developments). This moderation was assessed by constraining regression paths between groups. These constraints created a series of nested models that were evaluated using LRT. The SEM models were identified using the fixed factor method. However, the research utilised the effects coding method to enable us to report means for the latent factors (Little et al., 2006). Model estimation was done using maximum likelihood, and missing data were handled with full information maximum likelihood (FIML). All analyses were performed with the package lavaan (Rosseel, 2012) created for R programming language (R Core Team, 2020). Invariance test The PREQI model held the assumptions of strong and weak invariance. When these assumptions are met, it is appropriate to examine latent parameters. As seen in Table 14.2, there was no significant difference in latent variances (Δχ² (20) = 29.79, p = 0.07), latent means (Δχ² (10) = 6.66, p = 0.75) or latent correlations (Δχ² (45) = 47.24, p = 0.38) between those living inside and outside housing developments. It was therefore concluded that the measurement model is equivalent between the two residential conditions even though some small differences were identified. Table 14.2

Latent means Residents Inside Housing

Residents Outside Housing

Developments

Developments

M (SD)

M (SD)

Architecture and urban space

3.47 (0.35)

3.47 (0.40)

Mobility, transportation and connectivity

2.63 (0.30)

2.73 (0.33)

Green areas

2.91 (0.79)

2.76 (0.75)

Socio-relational features

3.04 (0.36)

3.07 (0.37)

Recreational services

2.82 (0.44)

2.83 (0.52)

Commercial services

3.14 (0.80)

3.13 (0.78)

Social provisions

3.43 (0.47)

3.56 (0.41)

Pace of life

3.98 (0.57)

3.97 (0.64)

Environmental health

3.46 (0.72)

3.32 (0.64)

Upkeep

3.13 (0.70)

3.28 (0.68)

Factors

Source: The authors.

Findings The latent factors Table 14.3 presents the correlations between all latent factors examined. The lower triangle of the correlation matrix presents correlations for residents living inside housing developments, while the upper triangle of the correlation matrix presents correlations for those living outside the developments.

Urban Space

0.64***

0.46***

0.30*

0.56***

0.62***

0.51***

0.42***

0.30**

0.82***

0.59***

0.69***

1

0.36***

0.64***

0.79***

0.40***

0.67***

0.41***

0.67***

0.93***

1

0.45***

0.32**

0.68***

0.45***

0.68***

0.74***

0.89***

1

0.79***

0.67***

0.86***

0.96***

0.47***

0.38***

0.29*

0.59***

1

0.78***

0.44***

0.87***

0.53***

0.62***

0.25*

0.24*

0.21

1

0.63***

0.57***

0.34***

0.33***

0.59***

0.33***

Services

0.62***

Commercial

Features

Services

Socio-relational Social Provision Recreational

0.42***

0.71***

1

0.33***

0.42***

0.70***

0.67***

0.37***

0.42***

0.58***

Pace of Life

0.53***

1

0.64***

0.18*

0.56***

0.48***

0.42***

0.57***

0.09

0.55***

Health

Environmental

1

0.60***

0.61***

0.41***

0.59***

0.78***

0.66***

0.59***

0.55***

0.60***

Upkeep

Note: * p < 0.05, ** p < 0.01, *** p < 0.001. Matrix lower triangle = correlations for residents inside housing developments; matrix upper triangle = correlations for residents outside housing developments. Source: The authors.

Upkeep

0.57***

0.52***

health

0.85***

Environmental

0.39**

Pace of life

services

Commercial

0.8***

0.79***

services

0.98***

Recreational

0.45***

Social provision 0.78***

features

0.59***

1

0.63***

0.64***

0.49***

Socio-relational 0.87***

Green areas

and connectivity

transportation

Mobility,

urban space

Connectivity

and

Transportation

Green Areas

Correlation matrix of latent factors by housing condition

Architecture and Mobility,

Architecture and 1

 

Table 14.3

The influence of urban layout on perceived residential quality: Costa Rica  223

224  Handbook of quality of life research As expected, most of the latent factors showed significant correlations for both groups. However, the correlation between commercial services and pace of life was not significant for residents living inside the housing developments (r = 0.21, p = 0.08). Similarly, the correlation between environmental health and mobility, transportation and connectivity was not significant for those living outside the developments (r = 0.09, p = 0.36). Regression estimates Table 14.4 shows the regression estimates by group (residents inside and residents outside housing developments). The table also indicates which regression coefficients were significantly moderated by housing condition. The results showed that: ● overall, most of the significant effects pertained to participants living outside housing developments; ● distance from town centre explained more variance in latent factor outcomes among those living outside housing developments compared to those inside; ● in contrast, there was a significant effect of housing density on seven constructs among those living inside housing developments, compared to only three constructs among those living outside. Table 14.4

Effects of distance to town centre and housing density on PREQI latent factors by housing condition

  PREQI latent factor

Residents Inside Housing

Residents Outside Housing

Developments

Developments

Distance from

Housing density

town centre

Distance from

Housing density

town centre

Architecture and Urban Space

–0.01a

–0.44***a

–0.39***a

0.04a

Mobility, Transportation and Connectivity

0.05a

–0.11a

–0.4***a

0.19**a

Green Areas

0.02a

–0.22*

–0.29***a

–0.18***

Socio-relational Features

0.01

–0.25*

–0.04

–0.11

Recreational Services

–0.09

0.02

–0.33***

–0.02

Environmental Health

–0.08a

–0.38*a

–0.14***a

–0.27*a

Upkeep

0.03a

–0.34***a

–0.33***a

–0.06a

Commercial Services

0.03a

–0.07a

–0.18***a

0.05a

Pace of Life

–0.03

–0.23***

–0.07a

Social Provisions

0.19a

–0.34***a

0.02a

a

–0.33*** –0.27*a

a

a

Note: * p < 0.05, ** p < 0.01, *** p < 0.001. a Estimate is statistically different between groups. Source: The authors.

The following relationships were found: ● the relationship between distance to town centre and architecture and urban space was significantly moderated by housing condition (Δχ2 (1) = 7.34, p < 0.01); ● likewise, the relationship between housing density and architecture and urban space was statistically different between groups (Δχ2 (1) = 9.04, p < 0.001); ● the relationship between mobility, transportation and connectivity and distance to town centre was also different between groups (Δχ2 (1) = 9.85, p < 0.001) along with housing density (Δχ2 (1) = 4.48, p = 0.03);

The influence of urban layout on perceived residential quality: Costa Rica  225 ● in the case of green areas, only distance from town centre showed a significant difference between groups (Δχ2 (2) = 8.51, p = 0.01); ● likewise, the relationship of environmental health, upkeep, commercial services, pace of life and social provisions with distance from town centre was significantly different; and ● a similar pattern was observed between these variables and housing density. These findings indicate that the relationship between the aforementioned latent factors and the variables of distance to town centre and housing density depends on housing condition: ● the results generally showed that among residents living inside housing developments, greater housing density is associated with more negative perceptions of residential environmental quality; ● in contrast, among those living outside housing developments the association is not statistically significant; and ● the results also showed that longer distances to town centres is significantly associated with negative perceptions of residential environmental quality among residents living outside housing developments, while there was no relationship for those living inside the housing developments.

DISCUSSION AND IMPLICATIONS In Costa Rica, as in other Latin American countries, urbanisation is increasing, and new housing developments are a dominant part of the urban landscape. The impact of the design or physical layout of these developments on their residents should be of great interest to urban planners and others involved in local and national land policy. Differentiating between groups of residents who live inside and those who live outside housing developments allowed the study to explore the impact of residential conditions on people’s perceptions of their residential environment. The results show that living inside or outside these developments influences residents’ perceptions in different ways with regard to aspects of the physical-spatial characteristics of the environment. Key findings showed that: 1. Higher housing density negatively affects people living inside housing developments, since their views of their residential environment are low. 2. However, living further away from the town centre, where nearly all urban facilities are located, has no effect on their opinions. This is most likely because of socio-economic conditions and the availability of transportation in most of these housing typologies. 3. Living within residential developments (which are, in many cases, similar to residential ‘islands’ within the urban layout) ensures the presence of nearby green and recreational areas, and greater control of cleanliness and security within the development. 4. On the other hand, for people living outside the housing developments, longer distances to the town centre negatively affect their views about residential quality, but higher housing density does not. For those who live outside these developments, they live further from urban centres which results in less accessibility to public green and recreational areas, as well as other urban services. Also, in areas with lower residential densities, there are

226  Handbook of quality of life research fewer urban amenities and services, adversely affecting their perception of their residential environment. Aligned with other studies (de Pablos and Sánchez, 2003; Francis et al., 2012; Hillier, 2004), the study demonstrated that design or layout of the urban area and accessibility to different urban facilities are essential aspects of achieving positive feelings towards one’s residential environment. However, extensive low-density urban patterns are pushing people away from town centres, decreasing their accessibility to diverse services and the opportunities that urban centres offer local residents. Critics of modern urban patterns find valid proof in these results that show how QOUL is relatively low for people who live within poorly integrated residential areas. That is, they experience more urban segregation and social disintegration. In agreement with Brereton et al. (2008), studies like this are relevant to understanding the vital role of the spatial dimensions in residents’ well-being and have critical implications for public policy. Finally, the study reported here is not without limitations. The multidimensional aspects of the urban environment make it challenging to identify the full array of factors that are the most relevant. More detailed information about the characteristics of housing developments is needed to determine the different aspects of this particular housing typology and ‘way of living’ more accurately. These in turn may influence the QOL of residents in developments as well as those living outside their borders. Doing so could help planners better understand what improvements are needed in urban and residential settings. Furthermore, the study was conducted in only one place in Costa Rica. Replication of this research is needed elsewhere to validate study findings in other parts of Costa Rica and in other Latin America countries where new residential environments are being built. However, the results clearly show that the urban layout and residential conditions are crucial aspects of a high-quality residential environment.

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The influence of urban layout on perceived residential quality: Costa Rica  227 Connerly, C. and Marans, R.W. (1988), ‘Neighborhood quality: a description and analysis of indicators’, in E. Huttman and W. van Vliet (eds), Handbook of Housing and the Built Environment in the United States, New York: Greenwood Press, pp. 37–61. Delavari-Edalat, F. and Abdi, M.R. (2010), ‘Human–environment interactions based on biophilia values in an urban context: case study’, Journal of Urban Planning and Development, 136, 162–8. de Pablos, J.C. and Sánchez, L. (2003), ‘Estilos de vida y revitalización del espacio urbano’, Papers: Revista de Sociologia, 71, 11–31. De Terán, F. (1999), ‘El urbanismo europeo en América y el uso de la cuadrícula. Cerdá y la ciudad cuadricular’, Ciudad y Territori. Estudios Territoriales, XXXI, 21–40. Francis, J., Giles-Corti, B., Wood, L. and Knuiman, M. (2012), ‘Creating sense of community: the role of public space’, Journal of Environmental Psychology, 32, 401–9. Fornara, F., Bonaiuto, M. and Bonnes, M. (2010), ‘Cross-validation of abbreviated perceived residential environment quality (PREQ) and neighborhood attachment (NA) indicators’, Environment and Behavior, 4, 171–96. Handal, C. and Irazábal, C. (2022), ‘Gating Tegucigalpa, Honduras: the paradoxical effects of “Safer Barrios”’, Journal of Urban Affairs, 44, 59–79. Hartig, T., Evans, G.W. and Jamner, L.D. et al. (2003), ‘Tracking restoration in natural and urban field settings’, Journal of Environmental Psychology, 23, 109–23. Hillier, B. (2004), ‘Can streets be made safe?’, Urban Design International, 9, 31–45. Inostroza, L., Baur, R. and Csaplovics, E. (2013), ‘Urban sprawl and fragmentation in Latin America: a dynamic quantification and characterization of spatial patterns’, Journal of Environmental Management, 115, 87–97. Instituto Nacional de Estadística y Censos (INEC) (2012), IX Censo Nacional de Población y V de Vivienda: Resultados Generales, Costa Rica: INEC. Irazábal, C. (2006), ‘Localizing urban design traditions: gated and edge cities in Curitiba’, Journal of Urban Design, 11, 73–96. Janoschka, M. (2002), ‘El nuevo modelo de la ciudad latinoamericana: fragmentación y privatización’, Revista EURE – Revista De Estudios Urbano Regionales, 28, 11–29. Krellenberg, K., Welz, J. and Reyes-Päcke, S. (2014), ‘Urban green areas and their potential for social interaction – a case study of a socio-economically mixed neighbourhood in Santiago de Chile’, Habitat International, 44, 11–21. Little, T.D. (1997), ‘Mean and covariance structures (MACS) – analyses of cross-cultural data: practical and theoretical issues’, Multivariate Behavioral Research, 32, 53–76. Little, T.D. (2013), Longitudinal Structural Equation Modeling, New York: The Guilford Press. Little, T.D. and Card, N.A. (2013), Longitudinal Structural Equation Modeling, New York: The Guilford Press. Little, T.D., Cunningham, W.A., Shahar, G. and Widaman, K.F. (2002), ‘To parcel or not to parcel: exploring the question, weighing the merits’, Structural Equation Modeling, 9, 151–73. Little, T.D., Slegers, D.W. and Card, N.A. (2006), ‘A non-arbitrary method of identifying and scaling latent variables in SEM and MACS models’, Structural Equation Modeling, 13, 59–72. Mao, Y., Fornara, F. and Manca, S. et al. (2015), ‘Perceived residential environment quality indicators and neighborhood attachment: a confirmation study on a Chinese sample in Chongqing’, PsyCh Journal, 4, 123–37. Marans, R.W. and Stimson, R.J. (2011), ‘An overview of quality of urban life’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 1–29. Matsuoka, R.H. and Kaplan, R. (2008), ‘People needs in the urban landscape: analysis of Landscape and Urban Planning contributions’, Landscape and Urban Planning, 84, 7–19. Meade, A.W., Johnson, E.C. and Braddy, P.W. (2008), ‘Power and sensitivity of alternative fit indices in tests of measurement invariance’, Journal of Applied Psychology, 93, 568–92. Montoya, J.W. (2009), ‘Globalización, dependencia y urbanización: la transformación reciente de la red de ciudades de América Latina’, Revista de Geografía Norte Grande, 44, 5–27. Quesada, F. (2007), ‘La modernización entre cafetales: San José, Costa Rica, 1880–1930’, doctoral thesis, Instituto Renvall.

228  Handbook of quality of life research R Core Team (2020), ‘R: a language and environment for statistical computing’, Vienna: R Foundation for Statistical Computing, https://​www​.r​-project​.org/​(accessed 17 December 2021). Ríos-Rodríguez, M.L., Rosales, C. and Lorenzo, M. et al. (2021), ‘Influence of perceived environmental quality on the perceived restorativeness of public spaces’, Frontiers in Psychology, 12, Article 644763. Rosseel, Y. (2012), ‘lavaan: an R package for structural equation modeling’, Journal of Statistical Software, 48, 1–36. Sam, N., Bayram, N. and Bilgel, N. (2012), ‘The perception of residential environment quality and neighbourhood attachment in a metropolitan city: a study on Bursa, Turkey’, eCanadian Journal of Humanities and Social Sciences, 1, 22–39. Schneider, A. and Woodcock, C.E. (2008), ‘Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information’, Urban Studies, 45, 659–92. Shlomo A., Parent, J., Civco, D.L. and Blei, A.M. (2012), Atlas of Urban Expansion, Cambridge, MA: Lincoln Institute of Land Policy. United Nations (2015), Division World Urbanization Prospects: The 2014 Revision, New York: Department of Economic and Social Affairs/Population. von-Breymann, H. and Montenegro-Montenegro, E. (2019), ‘Validation of a scale to measure perceived residential environment quality in a Latin American setting/Validación de una escala para medir la percepción de la calidad del entorno residencial en un contexto latinoamericano’, PsyEcology, 10, 217–56.

PART IV QUALITY OF LIFE IN SMALL TOWNS, RURAL AREAS AND MIGRATION COMMUNITIES

15. Quality of life in small towns: a mid-American case study Rodrigo F. Cantarero and James J. Potter

INTRODUCTION There are pros and cons of living in rural small towns that affect people’s quality of life (QOL). They can have positive attributes – such as safety, tranquillity and a friendly environment – in contrast with some of disamenities found in cities (such as pollution, crime, traffic congestion, high costs of living). But there can be negatives relating to industrial evolution and out-migration, leading to population decline, ageing and a diminution of services and employment opportunities, which has been relatively widespread across small town America. This chapter reviews QOL in small towns in the US, highlighting how in-migration and its considerable impacts on small towns affects residents’ well-being and QOL outcomes. Numerous small towns in the Midwest US have experienced relatively large and mainly culturally different in-migration attracted by industries such as meat processing. A small rural town case study in Nebraska explores the question of whether QOL factors vary in composition and importance depending on status as a newly arrived (NA) or a long-term (LT) resident of the community. NA residents are seen to be satisfied with their incomes and their neighbourhood and community environment, which strongly influence their QOL, while for LT residents, there is a significantly weaker effect of job availability, overall community stress and a person’s gender. Lessons learned from the case study are discussed, along with suggestions for QOL research.

THE NATURE OF SMALL TOWNS As the lowest level of the central place hierarchy (Stafford, 1963), in the pre-industrial revolution era, small towns emerged as places of production of craft goods, exchange and export of agricultural goods (Jousseaume and Talandier, 2016; Mahoney, 2003; Stafford, 1963), and were service centres for the agricultural environment (Jousseaume and Talandier, 2016). The coming of the Industrial Revolution saw the rapid growth of cities, with the out-migration of farm labourers to larger urban places where manufacturing industries were being established (Knox and Mayer, 2013). The advent of the ‘Fordist’ economy (assembly line, scale and agglomeration economies, technology) (ibid.) called into question the basic economic function of small towns (Jousseaume and Talandier, 2016), which initiated their general decline. In the 1970s, a process of counter-urbanisation started, particularly in towns within commuting distance of a city. This movement from cities back to small towns brought lifestyle changes, including what has been called rural gentrification (Dahms, 1998; Li et al., 2019). The future of small towns was further affected by the late twentieth century by globalisation and changes in the division of labour (Dahms, 1998; Knox and Mayer, 2013), and the emer230

Quality of life in small towns: a mid-American case study  231 gence of the knowledge economy away from industrial production, with flexible modes of production and transnational capital (Dahms, 1998). The nature of these trends in small town development varies greatly across the world and within countries (see Fertner et al., 2015; Jousseaume and Talandier, 2016; Kwiatek-Soltys and Mainet, 2014; Li et al, 2019; Vaishar et al., 2016). There is no standard definition of what constitutes a ‘small town’ with its marked cultural connotations, only a common sense of categorisation (Servillo et al., 2014). Population size for what defines a town or small town urban area can range, for example, from 200 people in Denmark to 100 000 in China. The US has no official definition, but the United States Department of Agriculture (USDA) National Center for Education Statistics uses 2500 to 25 000 people, and its Rural–Urban Continuum Codes use 2000–2500 (Economic Research Service, USDA, 2020). Some small towns have done well while others have experienced demographic and economic decay with the out-migration of educated young people, a decline in local businesses and a loss of character and sense of place (Knox and Mayer, 2013). The winners tend to be those that retained their commercial, industrial and service functions (Jousseaume and Talandier, 2016), or were able to adapt to changing functional and economic circumstances (Fertner et al., 2015; Li et al., 2019), with some even attracting in-migration. The Pros and Cons of Small Towns In the context of investigating QOL, small town attraction resides in lifestyle preferences and their immaterial endowment – safe, tranquil and friendly (social capital) environment – in contrast with some of disamenities found in cities that have contributed to the counter-migration movement, which influences perceived QOL (Dahms, 1998; Li et al., 2019). Some small towns with a pleasant climate or attractive scenic and recreational amenities have become recreational or retirement communities. Despite differences among small towns (Fertner et al., 2015; Knox and Mayer, 2013; Westlund and Kobayashi, 2013), in general over many decades, a large number of small towns in the more economically developed countries have been in decline, associated with depopulation and loss of economic vitality and services (Besser et al., 2005; Li et al., 2019; McAreavey and Argent, 2018; Recker, 2013). This is the case across much of rural US, particularly around the Corn Belt, Mississippi Delta and the Great Plains states (Hansen, 2003; Johnson, 2003; Recker, 2013; Whitener and Parker, 2007). Not surprisingly, studies have found that QOL in larger urban areas is perceived as being higher than in small towns (Knox and Mayer, 2013; McCrea et al., 2011; Shucksmith et al., 2009). However, some found overall QOL in small towns to be more positive than in larger urban centres (Bell, 1992; Campanera and Higgins, 2011; Campbell et al., 1976; Davis and Fine-Davis, 1991; Dillman and Tremblay, 1977; Marans et al., 1980; Oppong et al., 1988; Prezza and Costantini, 1998; Richmond et al., 2000). Yet others have found no or little difference between them (Beesley, 1997; Best et al., 2000; Mookherjee, 1992; Shucksmith et al., 2009), suggesting that in wealthier countries, rural living standards are sufficiently high (Knox and Mayer, 2013; Requena, 2016). The conflicting results might be due to locational variables that may affect QOL (Kwiatek-Soltys and Mainet, 2014; Millward and Spinney, 2013), and an element of self-selection through migration (Walker and Li, 2007), with people choosing to reside in

232  Handbook of quality of life research small towns that satisfy those QOL attributes that are important to them (McCrea et al., 2014). That is the case especially for some small towns near larger cities with their easy access to those cities’ services, jobs and amenities (Casini et al., 2021; Fertner et al., 2015; Vaishar et al., 2016). There are disadvantages in rural areas and small towns, especially in job opportunities, services and housing choice (Dillman and Tremblay, 1977; Marans et al., 1980; Peters, 2017; Richmond et al., 2000) – all key determinants of QOL – which translates into an exodus of youth and early adult populations from small towns, especially for the more isolated ones (Llorent-Bedmar et al., 2021). Nonetheless, studies (for example, see Auh and Cook, 2009; Casini et al., 2021; Dillman and Tremblay, 1977; Gattino et al., 2013; Idris et al., 2016; Kwiatek-Soltys and Mainet, 2014; Richmond et al., 2000; Sørensen, 2016) show that rural/small town residents persistently evaluate their overall QOL positively (if sometimes below urban residents), with the positive contributors to well-being and QOL being: ● the more intangible aspects and immaterial endowments and of the environment of the small towns (more relaxed, tranquil, less pressured way of life, lower expectations); ● psychological factors (satisfaction with personal life, friendly, community attachment, safety); and ● social relationships. All are factors that seem to compensate for the deficiencies of small towns or are given greater weight in the perceived evaluation of QOL in them (Dahms, 1998, Li et al., 2019). Social relations and interpersonal aspects of a community seem to play a key role in small town QOL (Crowe, 2010; Filkins et al., 2000; Goudy, 1977; Van der Horst and Coffé, 2012; Whitham, 2007), helping to fill the gap in services, increase community capacity and promote social cohesion, which in turn increases community attachment and satisfaction (Auh and Cook, 2009; Bernard, 2015; Filkins et al., 2000; Sullivan et al., 2014).

SMALL TOWN GROWTH The general decline of rural areas and small towns in terms of population, economy and services has not been universal. For example, in Denmark, small towns have generally fared better (Fertner et al., 2015; Knox and Mayer, 2013; Jousseaume and Talandier, 2016). And the counter-migration movement in the US (Henderson, 2018) has been fuelled by: ● the improved infrastructure in communications, enabling distance learning and labour market teleworking (Saez Soro et al., 2007 in Llorent-Bedmar et al., 2021); ● improved transportation accessibility of rural areas to the services, jobs and amenities of the cities; and ● the physical, residential and social amenities of small town environments that contrast with the disamenities of larger cities. In the US, the flow of migration directed towards the so-called new immigration destinations (NIDs) has stimulated growth in some long-declining small towns, resulting in a net population gain in 2016–17, although smaller towns in aggregate continued to decline (Henderson, 2018).

Quality of life in small towns: a mid-American case study  233 This in-migration movement to small towns that previously had little in-migration experience (Fonseca, 2008; McAreavey and Argent, 2018) comes mainly from the US Southwest, and especially from Mexico and Central America, with the in-movers seeking employment in agro-industrial plants – particularly in the meat-processing industry – as many plants have relocated from cities to small towns (Potter et al., 2005). The Hispanic-Latino population grew by 43 per cent from 2000 to 2010, while the US total population increased by 9.7 per cent (Colby and Ortman, 2014; Humes et al., 2011). That growth was even more significant in many parts of the US, with portions of the Midwest showing over 100 per cent growth. Impacts The economic services structure and population growth impacts (in terms of size, speed and diversity) on small rural towns can be considerable (Barcus and Simmons, 2013), affecting their QOL (Besser et al., 2008; Dillman and Tremblay, 1977; Looker, 2014), especially for small towns characterised by a high degree of industry specialisation such as those specialising in the meat-processing industry (Dalla et al., 2002), which is labour-intensive and utilises mainly unskilled labour. The work is unpleasant, demanding and low paid, which most local residents avoid. Thus, the positions are filled by the recently arrived labourers. The dramatic increase of the Latino population in some Midwest small towns is illustrated in Nebraska, where 82 of its 93 counties experienced growth in their Hispanic population, which was more than 100 per cent between 2000 and 2010 in 22 counties (Pew Research Center, 2022), compared to 5 per cent for the state’s Latino population. Nebraska’s Latino population component rose from 5.5 per cent in 2000 to 9.2 per cent in 2010 and was projected to increase to 12.5 per cent by 2020 (Center for Public Affairs Research [CPAR], 2013; Ennis et al., 2011). This rapid change in small town racial, ethnic and cultural diversity can be a shock to community life for existing residents, generating large amounts of stress and often fear over the perceived cultural and social transformation taking place in their communities (Popke, 2011), all of which affects perceptions of QOL. Miraftab (2014) suggests that ‘we need to systematically examine small towns and non-traditional destinations to recognise the specificities and generalisabilities that surface in terms of immigrants’ experiences in these emerging transnational spaces’ (cited in McAreavey and Argent, 2018, p. 148) and the impact on the local residents. Recent literature on US small town growth has focused on this labour in-migration to small towns, including those with a meat-processing industry base (Aponte, 1999; Benjamin-Alvarado et al., 2009; Bodvarsson and Van den Berg, 2003; Carr et al., 2012; Dalla et al., 2002; Fink, 1998; Gouveia and Stull, 1997; Henness, 2002; Potter et al., 2005, 2010; Ramos et al., 2017; Thuy and Durrenberger, 1998). The rapid growth in the number of non-natives can have a destabilisation effect, with the new population being blamed for increase in crime (Crowley and Lichter, 2009, 2017; Ferraro, 2016; Sampson, 2008) and lower wages (Jensen, 2006; Marrow, 2009). However, the increase in population does rejuvenate and reinvigorate the community and local economy (Carr et al., 2012; Crowley and Knepper, 2019; Lichter and Johnson, 2006; Trabalzi and Sandoval, 2010), injecting funds into businesses and the housing market, expanding the tax base and services. But there can also be new and additional costs with increased demand on housing, local services and education (Chapa et al., 2004).

234  Handbook of quality of life research Social and cultural issues are apparent, with social dilemmas arising from conflict with the historical and established social pattern of small town societies. The idealist agrarian vision of a society of small landowners denying class division persists to shape the modern image of rural life (Fink, 1998). But social and cultural differences can create a separation between the recent arrivals and the long-time residents, a product of unpreparedness (Chapa et al., 2004). A rapid expansion of foreign-born in the labour force within dominantly Anglo communities takes the latter by surprise (they have limited resources and little experience with this type of change) (Koball et al., 2008). Neither meat-processing plants nor communities fully anticipated the effects of this shift nor planned for the successful integration of the new workers and their families into the community (North Central Regional Center for Rural Development, 1999). Accompanied by ‘white flight’, this has led to high levels of residential segregation of newcomers and a lack of integration or spatial assimilation into the community, with its associated lesser access to community resources (Hall and Stringfield, 2014; Lichter et al., 2016) that is compounded by the legal status of the Latino in-movers (Parrado and Kandel, 2008), contributing to an ‘us and them’ attitude by established residents to protect their local ‘culture’ (Milbourne and Webb, 2017). Community satisfaction has been linked to social ties (Filkins et al., 2000), which exert a positive effect on well-being (Theodori, 2001; Van der Horst and Coffé, 2012). The sense of community, often connected with life satisfaction (Ramos et al., 2017), can be threatened by diversity (Townley et al., 2011). On the positive side, it is also important to be cognisant of cultural norms in shaping understanding of deprivation and feeling of well-being by mitigating the experience of poverty and positively influencing an individual’s perception of well-being (Milbourne and Webb, 2017). Many impacts and responses discussed in the literature focus on the services within the communities that become strained with the new arrivals. Social and cultural differences inhibit societal inclusion and can complicate access to basic services. Education, healthcare and welfare service providers have had to incorporate language and/or multicultural training for staff, hire new bilingual staff or contract with translators (Dalla et al., 2002). That in turn inflates community concerns that new arrivals are compromising the quality of their services, as well as the quality of schools and education (ibid.). In summary, the literature (Aponte, 1999; Bodvarsson and Van den Berg, 2003; Dalla et al., 2002; Fink, 1998; Gouveia and Stull, 1997; Henness, 2002) identifies a series of issues for NA labourers and the communities to which they migrate. Following the arrival of the processing plants, communities were often caught in a reactionary mode to new arrival issues, experiencing additional burden from the growing populations (Henness, 2002). As the population of new arrivals increases, it is necessary for the community to recognise the changing demographic situation and take action to accommodate and adjust to the quickly diversifying community. Some studies have explicitly explored community residents’ individual perceptions of, and responses to, this rapid in-migration to small towns (Dalla and Christensen, 2005; Dalla et al., 2004; Grey and Woodrick, 2002; Hernández-León and Zúñiga, 2000; Ramos et al., 2017). Dalla et al. (2004) explored perceptions of LT community residents concerning community change related to in-migration; however, the views of the NA community residents were not explicitly examined. Dalla and Christensen (2005) and Ramos et al. (2020) examined the community perceptions of the NA in-migrant residents in rural communities.

Quality of life in small towns: a mid-American case study  235 While these studies have provided information about the feelings and opinions of individual community residents, a more objective and generalised measure of community residents’ perceptions concerning community changes is needed to lay a foundation for addressing the stresses created. Currently, there is a dearth of empirical quantitative research exploring both NA and LT small town community residents’ perceptions of their communities following massive in-migration, most studies using qualitative methods (for example, Broadway and Stull, 2006; Cooper 1997; Dalla and Christensen, 2005; Grey and Woodrick, 2002; Stull et al., 1995). Studies tend not to compare the perceptions of NA and LT community residents regarding the QOL in their community, although Dalla et al. (2002) suggest that LT community residents and immigrant newcomers are more alike than different. But there might be significant differences in perceptions as well. The chapter now addresses the effects of in-migration to small towns on residential satisfaction and QOL of both NA residents and LT residents through a small town case study.

A SMALL TOWN CASE STUDY Background Crete is a small town in Saine County, Nebraska, with a population of 5200 in 2015. It is one of the communities affected by a sudden increase in diversity through the in-migration of labourers to work in the meat-processing industry. Those jobs tend to not be desirable for many members of the Anglo population (Grey and Woodrick, 2002), so meat-processing plants have hired non-Anglo workers (ibid.) who were largely Hispanic or Latino, many with families. Thus, Crete experienced a large population increase over two decades (23.7 per cent between 1990 and 2000, and 16.2 per cent between 2000 and 2010). Crete’s Hispanic population grew from just 40 in 1990, to 814 in 2000, and to 2484 in 2010 (US Census Bureau, 2015). Incoming workers also came from Vietnam, South Korea, Laos, Croatia, Serbia and Iraq. The workers were attracted to Crete by: ● employment opportunities at Farmland, a pork processing plant; ● the resettlement of refugees through organisations located in nearby Lincoln, the capital of Nebraska; and ● word-of-mouth invitation by Crete residents to their friends, families and acquaintances (chain migration). Methodology The sample The authors conducted a survey in Crete to assess the QOL and housing concerns of the LT residents – defined as those having lived in town for 15 years or more – and NA residents (defined as those having lived in town less than five years). Participants were 19 years of age or older. Both English-speaking and bilingual Spanish speaking interviewers were used for the data collection. The survey sample design involved dividing the town into two sets of census blocks: one containing blocks that the census identified as containing five or more racial/ethnic minority

236  Handbook of quality of life research household members (to locate areas where new arrivals tended to reside); the other containing all other census blocks. A simple random sample (SRS) of these two sets of Census 2000 blocks for the city of Crete was undertaken. All chosen households were contacted and randomly assigned to an eligible group (NA and LT), giving a total of 180 respondents split evenly between both groups. The questionnaire used a five-point Likert scale to determine a resident’s level of concern regarding housing and QOL issues. Respondents were specifically asked if a particular issue affected their QOL – for example, satisfaction with various aspects of their residence, neighbourhood, services and sources of stress such as job, housing, discrimination and language. Analytic procedures The initial procedural approach involved creating composite indexes (mean of a set of topically related questions) in four areas (domains) related to the physical environment. Those domains were: ● ● ● ●

residential satisfaction; neighbourhood satisfaction; satisfaction with city services; and safety.

These indexes all had a Cronbach’s alpha > 0.7, except for safety with an alpha > 0.65. They were combined with: (1) health, level of stress (influential explanatory variables identified in the literature); and (2) age and income level (control variables). They were then regressed against level of satisfaction with overall QOL perception, undertaken separately for LT and NA residents. Only health proved to be statistically significant in one of the groups. Lack of statistical significance of the regression model could have been due to a combination of the relatively small sample size and the restricted variability of some of the variables, as well as the presence of some bias towards positive satisfaction on the dependent variable, which is not uncommon (Cummins et al., 2012). Normalising the dependent variable did not change the results. However, it did suggest that the composite indexing of individual variables in the domain areas might be masking the influence of some of the individual variables within the index. Thus, we proceeded to unbundle the indexes and explore their individual relationship to the overall QOL variable using correlation analysis (separately for the LT and NA groups). In keeping with the exploratory nature of the study, as a final step a hierarchical regression model was run, controlling initially for age, income and gender, then followed by variables that had medium to high correlation (r > 0.3; Cohen et al., 2003) with overall QOL. Results Levels of satisfaction Most residents were satisfied with their overall QOL – 66.2 per cent for LT and 59.7 per cent for NA residents – while 9.3 per cent of LT and 15.1 per cent of NA residents were dissatisfied. Males were more satisfied with their QOL than were females. Overall levels of stress were not high for either group, with 24.7 per cent of LT and 23.2 per cent of NA residents expressing

Quality of life in small towns: a mid-American case study  237 being stressed. Individual variable contributors to overall levels of stress (correlation > 0.3) varied by group: ● for LT residents it was life in town is stressful (r = 0.61), their level of income (r = 0.52), the inability to communicate (r = 0.36) and the struggle for a better house (r = 0.41); ● while for NA residents the job is a source of stress (r = 0.36), and tension with neighbours (r = 0.31). But apparently stress was not related to social/cultural differences, with both groups having a low score on this, especially the NA residents. Correlation analysis The correlation matrix for each residential group of overall QOL satisfaction vs all the individual variables in the survey identified a number of individual variables that exhibited moderate (r = 0.30–0.49) to high (r = 0.5 or more) correlation (Cohen et al., 2003) (see Table 15.1): ● LT residents exhibited a stronger correlation (medium to high) than NA residents (only medium, not high); ● LT and NA residents coincided on the relationship between stress and their income, life in town and level of satisfaction with their income; ● among the LT residents, the factors associated with overall QOL satisfaction revolved around income/employment and stressors; and ● among the NA residents, in addition to income/employment and stressors, their living environment (housing and neighbourhood), and health were added (note that although the NA residents were younger on average, they also were more likely to not have health insurance). Table 15.1

Correlation of individual factors with overall quality of life satisfaction by resident type

Long-term Residents

Correlation

Newly Arrived Residents

Correlation

Level of income is stressful

–0.554

Health

0.466

Life in town is stressful

–0.515

Satisfaction with income

0.346

Inability to communicate

–0.520

Life in town is stressful

–0.358

Job is source of stress

–0.501

Level of income is stressful

–0.345

Satisfaction with income

–0.501

Economic conditions of community

0.338

Overall level of stress

–0.439

Would recommend neighbourhood

0.310

Availability of employment

0.426

Satisfaction with mortgage

0.322

Town beneficial for family

0.428

Residence outdoor area

0.304

Source: The authors.

Partial correlations (controlling for gender) of QOL domains (for example, residential satisfaction index) with overall perception of QOL showed very little effect (not controlling for gender). However, some differences were detected in the correlation between male and female individual variables with overall QOL, with females having more individual variables that correlated positively with overall QOL.

238  Handbook of quality of life research Hierarchical regression analysis Controlling for age, income and gender, the hierarchical regression for the NA population identified as significant contributors to overall QOL perception (in order of importance based on beta values): ● level of income stress (p = 0.008); ● life in town being stressful (p = 0.046); and ● neighbourhood satisfaction (p = 0.009). Controlling for age, income and gender for the LT residents proved to be statistically significant explanatory variables (see Table 15.2): ● life in town being stressful (p = 0.003); ● the availability of employment in town (p = 0.007); and ● gender (male) (p = 0.024). Table 15.2

Regression model

Type of Residents

B

Beta

Sig.

R2

New arrival

 

 

 

0.620

Satisfaction with level of income

0.253

0.353

0.008

 

Life in town is very stressful

–0.296

–0.338

0.014

 

I would recommend my neighbourhood

0.230

0.310

0.009

 

Long term

 

 

 

0.281

Life in town is very stressful

–0.240

–0.324

0.003

 

Availability of employment

0.224

0.295

0.007

 

Gender

–0.526

–0.237

0.029

 

Source: The authors.

Summary In summary, NA residents seem to regard their satisfaction with their income, and their neighbourhood and community environment, as being significantly strong influential factors in determining their perception of overall satisfaction with their QOL, while LT residents had a significantly weaker effect of job availability, overall community stress and the person’s gender.

LESSONS LEARNED AND FUTURE QOL RESEARCH The Crete case study confirms that small towns can provide a satisfactory environment for its population in terms of low overall levels of stress and perceived QOL. Access to a larger urban area (proximity of the city of Lincoln) was a probable contributor. Factors affecting QOL varied somewhat – but not totally – by resident type (LT or NA). This was not unexpected as the relative influence of the independent variables reflected differences in the standard of comparison about QOL factors and life experiences (Marans and Rodgers, 1975; Marans and Stimson, 2011) and thus difference in relevance (Land et al., 2012). In-migration is an important contributor to stemming population decline and enhancing

Quality of life in small towns: a mid-American case study  239 economic stability and growth, bringing both opportunities and challenges. Resolving the negative issues requires understanding the residents’ perception – both LT and NA – of factors affecting their QOL to enable community leaders to plan and design community programmes that will enhance and enrich QOL. What is in store for the future? Technology has made it easier for some small town residents to commute to larger urban centres and to interact electronically, which has increased job flexibility and access to services (for example, health, distance learning). Combined with a move towards a knowledge-based and global economy, this has increased mobility based on lifestyle preferences (Dahms, 1998) attracting in-migration to some small towns offering positive QOL attributes. That could accelerate the growth of small towns in a post-COVID-19 era (Belanche et al., 2021), although it might be premature to determine the prevalence and permanent effects from the pandemic. Some communities have been taking action to better prepare the communities and their LT and NA populations, the latter being attracted by job opportunities offered by industries such as meat processing. For instance, in Lexington, Nebraska, community leaders have developed a community impact study team. Others have assembled multicultural forums to answer questions and concerns while covering emerging issues. Some of the literature suggests that communities are moving towards integration and inclusion as opposed to isolation or marginalisation. This is illustrated by policy steps taken to better prepare small town communities in Nebraska for in-migrants, including suggestions by the state’s lieutenant governor that in response to poor working conditions for labourers and their employers, processing plants should establish a community liaison programme (Henness, 2002) within their organisation to help orient the employees and the community as a whole. Such approaches might be taken up more widely to help enhance QOL outcomes in small towns.

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16. Subjective community well-being and resilience in a rural region experiencing rapid change Rod McCrea, Rosemary Leonard and Andrea Walton

INTRODUCTION Subjective community well-being, quality of life (QOL) or liveability studies (hereafter referred to as community well-being within a rural context) often focus on satisfaction with various aspects or dimensions of local community life, along with the importance of each of them for overall community well-being (for example, Lovejoy et al., 2010; Messer and Dillman, 2011; Moeinaddini et al., 2020). However, the level and importance of these dimensions are rarely examined together in a systematic way. By level, we mean how favourably a particular dimension of community life is perceived or rated (for example, level of satisfaction). Some studies may focus on comparing differences in levels of community well-being – for example, between urban and rural areas (McCrea et al., 2011; Messer and Dillman, 2011). Others may focus on comparing what is most important for predicting community well-being and QOL in urban and rural areas (Jansen, 2020; McCrea, Shyy et al., 2014). Other studies consider both levels of community well-being and the relative importance of various dimensions in equal measure; yet they are reported separately and not combined in any way (for example, Crider et al., 1991; Ibrahim and Chung, 2003; McCrea et al., 2019; Messer and Dillman, 2011). The chapter argues that it is important for researchers, planners and communities to consider both the level and importance of various dimensions of community well-being and QOL in combination. These dimensions include social, economic and environmental aspects. Considering both the level of satisfaction in a domain and its importance in a multiplicative way has been found to be important in predicting individual life satisfaction (Hsieh, 2003). However, it is also important to investigate how they combine to contribute to overall community well-being. For example, some dimensions might be rated as low or unsatisfactory, but they may not be that important for overall community well-being. These may be thought of as ‘squeaky wheels’, which may need to be oiled but which may be less important than other aspects in the overall scheme of things. Another example is that satisfaction with important dimensions still need to be maintained, especially during times of rapid change. Therefore, both the level and importance of various aspects of community well-being need to be considered in combined or multiplicative ways when examining their contributions to overall community well-being. The other way that the level and importance of community well-being and QOL dimensions may be related is over time. That is, the importance and level of these dimensions may not be independent. It is not clear from the literature if or how much the level and importance of various aspects of community well-being are related to each other. The relationships are likely to become more evident in communities undergoing rapid change. For example, if 244

Subjective community well-being and resilience in a rural region  245 a dimension of community well-being becomes unsatisfactory as a result of change, then does it also become more important? Conversely, does a dimension of community well-being and resilience become less important in the minds of residents when they feel satisfied with it? If the level and importance of dimensions are not interrelated, then the relative importance of various dimensions of community well-being and resilience may be quite stable over time. If not, their relative importance is likely to change. We define community well-being as a state where important aspects of community are evaluated at some point in time. In contrast, community resilience is defined as processes of ‘responding to change with a view to enhancing community well-being over time’ (McCrea, Walton et al., 2014, p. 271). The chapter makes two contributions to the community well-being literature: first, it examines the multiplicative contribution of various dimensions to overall community well-being and it also includes an analysis of community resilience (defined above) which has been found to be a strong predictor of future community well-being; and second, it examines whether and how the level and importance of community well-being and resilience dimensions are related to each other. It does this by measuring the levels and importance of community well-being and resilience dimensions over time in the context of a rural region in Queensland, Australia that has been undergoing rapid change over a six-year period. It builds on previous research by the authors, who examined changes in the level and importance of community well-being and resilience (McCrea et al., 2019); however, the chapter looks at changes in community well-being and resilience in a more integrated way over a longer time period.

STUDY CONTEXT The research discussed here was conducted in the Western Downs region in the southern part of the State of Queensland in Australia from 2014 to 2018. The Western Downs is approximately 40 000 km2 and functions as a local government area in a rural region, approximately 300 km west of the state’s capital. The region contains approximately 25 000 residents and the main economic activity is agriculture, forestry and mining. Agriculture includes grain and cotton with some irrigated cropping. It also includes broadacre farming for sheep and cattle grazing. Mining-related activities include gas exploration and production, coal mines and a power station. The region experienced rapid change during this period, making it ideal for investigating changes in the levels and importance of various dimensions of community well-being and QOL and community resilience over time. Development of the unconventional gas industry began in 2010 with the construction of extensive gas-related infrastructure, and distribution pipelines to extract, process and distribute the gas to coastal regions for liquification and ultimately export as liquified natural gas (LNG), one the world’s largest onshore gas developments at the time. The research collected survey data in 2014, 2016 and 2018, which allowed us to monitor social impacts associated with coal seam gas (CSG) development as the industry progressed through various phases of its industry life cycle. In 2014, the industry was in its construction phase, building extensive infrastructure for gas extraction and distribution. Local residents reported this construction phase as a ‘tsunami’ of change (Walton et al., 2013). In 2016, economic activity in the region slowed considerably as the industry transitioned from its construction to operations phase (Luke and Emmanouil, 2019; Measham et al., 2019). In 2018,

246  Handbook of quality of life research the industry was in its operations phase, with over 1000 wells producing CSG in the Surat Basin and new gas fields opening up in the region. Thus, the region experienced rapid changes between 2014 and 2018.

CONCEPTUAL FRAMEWORK FOR COMMUNITY WELL-BEING AND COMMUNITY RESILIENCE DIMENSIONS A range of social, economic, environmental and political dimensions have been found to contribute to overall community well-being in previous research (for example, Christakopoulou et al., 2001; Forjaz et al., 2011; Onyx and Bullen, 2000; Sirgy et al., 2010). This research employs a conceptual framework based on McCrea et al. (2016), developed from previous research by the authors in the study area (Leonard et al., 2016; McCrea, Walton et al., 2014), as shown in Figure 16.1.

Source: McCrea et al. (2016).

Figure 16.1

Community dimensions and their relationship to community well-being, community resilience and future well-being

Often, it is not clear how community well-being and resilience differ from each other because they draw on similar community resources. For example, social capital, represented by the community spirit, cohesion and trust dimensions, contributes to both community well-being and resilience (for example, Adger, 2003; Aldrich and Meyer, 2015; Berkes and Ross, 2013; Besser et al., 2008; Helliwell and Putnam, 2004; Onyx and Bullen, 2000; Western et al., 2005). However, our conceptual framework clearly distinguishes between community well-being and community resilience, defining community well-being as a state, and community resilience as processes. Using these definitions, community well-being and resilience do not necessarily move in unison. Rapid change may affect community resources, which may then impact on various aspects of community well-being, while at the same time triggering community-resilient responses to those changes (Norris et al., 2008). As a result, it is possible for overall community well-being to fall while at the same time the community is enacting resilient responses, especially in the context of natural disasters (see Cutter et al., 2008). Conversely, resilient responses can also build social and other community capitals and subsequent future community well-being (Besser et al., 2008). Thus, the model also proposes that future community well-being is a function of current community well-being in combination with how well a community responds to change over time.

Subjective community well-being and resilience in a rural region  247 Research Questions and Hypotheses Given the limited research on how the level and importance of community well-being and resilience dimensions are related to each other, and their relative stability over time, our research questions are broad and our hypotheses exploratory. We ask four basic research questions and generate some associated hypotheses. These are summarised in Table 16.1. Table 16.1

Research questions and associated hypotheses

Research Questions

Associated Hypotheses

RQ1: How stable are satisfaction levels of overall community

The levels of community well-being and resilience dimensions

well-being and resilience during times of rapid change?

will change significantly over time

RQ2: How stable are the importance of various dimensions of

The importance of these dimensions will be more stable over

community well-being and resilience in times of rapid change?

time than the levels of community well-being and resilience dimensions

RQ3: How do the satisfaction level and importance of

Dimensions of community well-being and resilience will become

dimensions of community well-being and resilience relate to

more important to residents if they drop to unsatisfactory levels.

each other over time?

Conversely, they will become less important when residents are very satisfied with them

RQ4: How much do various dimensions contribute to community Social dimensions will contribute more to overall community well-being and resilience in a rural region, considering both the

well-being and resilience in rural contexts than economic and

level and importance of these dimensions?

environmental dimensions

Source: The authors.

RQ1: How stable are satisfaction levels of overall community well-being and resilience during times of rapid change? It is hypothesised that the levels of the dimensions of community well-being and resilience would change significantly during a time of widespread and rapid onshore gas development in the region. However, the direction of change for each dimension is not hypothesised as there was a range of views about whether onshore gas development would be good for community well-being and resilience in the region (Walton et al., 2013). This hypothesis is more about the stability of community well-being, resilience and their underlying dimensions over time. RQ2: How stable are the importance of various dimensions of community well-being and resilience in times of rapid change? We hypothesis that these might change over time, but that they will be more stable than changes in levels of community well-being during rapid change. That is, while some dimensions might become more or less important over time, the basic order of importance of various community well-being and resilience dimensions will change more slowly than satisfaction with these dimensions. This is partly because people tend to move and stay in places that they find satisfactory on dimensions important to them (McCrea, Shyy et al., 2014), and the relative importance of the various dimensions are likely to reflect underlying values that tend to be stable.

248  Handbook of quality of life research RQ3: How do the satisfaction level and importance of dimensions of community well-being and resilience relate to each other over time? As mentioned, it is not clear from the literature how the levels and importance of various dimensions of community well-being and QOL and community resilience are related to each other over a time of rapid change. However, we hypothesise that dimensions of community well-being and resilience become more important to residents if dimensions drop to unsatisfactory levels because they then become more salient. Conversely, we hypothesise that when community ratings of the dimensions are high or very satisfactory, they may become less important in the minds of residents because they are not top of mind and somewhat taken for granted. RQ4: How much do various dimensions contribute to community well-being and resilience in a rural region, considering both the level and importance of these dimensions? In Queensland, social aspects in rural regions are often rated more highly than metropolitan areas, while the latter often rate more highly in services and facilities (McCrea et al., 2011). Both social aspects and services and facilities are both prima facie important in rural regions; however, we hypothesise that social aspects would contribute more to overall community well-being by being rated more highly. Economic prosperity was also expected to be important to overall community well-being, although economic opportunities in regional areas are often more limited than in urban areas, and so the economic opportunities dimension was hypothesised to contribute less to overall community well-being than social aspects and services and facilities. Social aspects were also hypothesised to contribute to community resilience to the extent that they reflected social capital and community efficacy (Brown and Westaway, 2011; Leonard et al., 2016; Walton et al., 2017). Environmental management and employment opportunities were also expected to be important for community resilience in the context of onshore gas development in a rural region (Walton et al., 2013). However, their contribution was hypothesised to be less, given that they may not be rated as highly as social aspects of rural living. Methods The methods used in collecting the data for this chapter have been published more fully elsewhere (McCrea et al., 2019; Walton and McCrea, 2018), and so are only briefly outlined here. The sample A survey was conducted in February to April in 2014, 2016 and 2018. It included approximately 400 residents aged 18 or older in each year. The survey data was collected via computer-assisted telephone interviewing (CATI) from a list of randomly selected landline and mobile numbers. The minimum response rate was 26 per cent in 2014 and 2018, and 45 per cent in 2016 when survey respondents from 2014 were re-surveyed. Quotas were also used to ensure representative sampling on age and gender. Residents from out of town were oversampled to ensure sufficient numbers were generated for comparisons of residents from in and out of town (not included in this chapter). The data was also weighted to

Subjective community well-being and resilience in a rural region  249 Table 16.2

Profile of weighted sample

 

Weighted Survey Sample (%)

 

2014

2016

2018

ABS Population Census (%) 2016

Male

50.8

50.8

50.8

50.9

Employed

67.0

67.5

68.9

62.4

18–34

27.4

27.3

27.5

27.4

35–54

35.4

35.7

35.4

35.5

55+

37.2

37.0

37.2

37.1

Location in town

51.7

52.4

50.9

65.2

Source: The authors.

ensure the statistics reflected the population characteristics, as per the 2016 Australian Bureau of Statistics (ABS) population census (ABS, 2018) (Table 16.2). Measures The same dimensions were measured across all three years. These measures had good internal consistency across the years and are described briefly in Table 16.3. Table 16.3

Measures of community well-being, resilience and their underlying dimensions

Measure (items; alpha 2014; 2016; 2018)

Example Items

Dependent variables Overall community well-being

Community was suitable for young children, teenagers and for seniors; local area

(five items; alpha = 0.86; 0.85; 0.82)

offered a good QOL overall, and they were happy living in their local area

Expected future well-being

Their local area would offer a good QOL, and they would be happy to be living in their

(two items; alpha = 0.86; 0.92; 0.85)

local area in three years’ time

Overall community resilience

How their local community was responding to CSG development in terms of planning

(18 items; alpha = 0.94; 0.95; 0.93)

for the future, leadership, accessing information, supporting volunteers, trust in CSG companies and state government

Underlying dimensions Personal safety

It was safe for various activities at night (e.g., being alone at home and walking

(four items; alpha = 0.77; 0.83; 0.86)

outside), as well as how safe they felt living in the area overall

Services and facilities

Satisfaction with local schools, sports and leisure facilities, food and other shopping,

(nine items; alpha = 0.87; 0.90; 0.86)

medical and health services, and community support services

Built environment

Satisfaction with cleanliness in their town, parks and gardens, and satisfaction with the

(three items; alpha = 0.82; 0.89; 0.85)

general appearance of their town

Environmental loading

Satisfaction with the condition, safety and amount of traffic on the roads; satisfaction

(seven items; alpha = 0.85; 0.82; 0.81)

with the level of dust, noise, and overall satisfaction with the general environment

Community participation and social

Regularly helped out a local group as a volunteer; active member of a local organisation

interaction

or club; regularly participated in community activities; regularly visited someone’s

(eight items; alpha = 0.85; 0.85; 0.87)

home

Community spirit, cohesion and trust

People can rely upon one another for help, have friendly relationships, can work

(eight items; alpha = 0.89; 0.91; 0.90)

together if there is a serious problem; their local community was welcoming of newcomers, and people of different cultures; and trust people in their local area

250  Handbook of quality of life research Measure (items; alpha 2014; 2016; 2018)

Example Items

Environmental management

Satisfaction of management of the natural environment for underground water for the

(four items; alpha = 0.85; 0.88: 0.84)

future, nature reserves for the future, and sustainability of local farming land for the future

Economic opportunities

Good local job opportunities; local businesses had done well out of CSG development;

(three items; alpha = 0.84; 0.86; 0.84)

overall satisfaction with employment and business opportunities in local area

Source: The authors.

Statistical tests and modelling Omnibus F-tests were used to test for any significant differences in levels of community well-being and resilience over time, with follow-up t-tests used to test for significant differences between particular years. Multi-group path analysis and omnibus Wald chi-squared tests were used to test for significant differences in the importance of each dimension in predicting overall community well-being and resilience, with beta confidence intervals used to test for significant difference between particular years in importance. All analyses were undertaken using the Stata statistical software package, version 15.

RESULTS AND DISCUSSION Levels of Community Well-being and Resilience Over Time (RQ1) Table 16.4 shows levels of the community well-being and QOL dimensions over time. This was used to test the stability of these dimensions over time in the context of regional change (RQ1). Overall community well-being remained robust over time, while perceptions of overall community resilience declined significantly. As such, community resilience appears less stable than community well-being. Table 16.4

Changes in satisfaction levels of community well-being dimensions over time

 

2014

2016

2018

 

Economic opportunities

3.09

2.23

2.53

*

Environmental loading

2.86

3.22

3.14

*

Environmental management

2.75

2.94

2.98

*

Services and facilities

3.32

3.41

3.27

 

Community participation and social interaction

3.25

3.31

3.25

 

Personal safety

3.90

3.82

3.82

 

Built environment

3.54

3.58

3.62

 

Community spirit, cohesion and trust

3.70

3.66

3.65

 

Overall community well-being

3.82

3.82

3.75

 

Expected future community well-being

3.63

3.65

3.78

 

Overall community resilience

2.99

2.94

2.79

*

Note: * Significant change in means over time (p < 0.05). Source: The authors.

Three of the eight dimensions of community well-being and resilience changed significantly over the six-year period (Figure 16.2). They were economic opportunities, which decreased

Subjective community well-being and resilience in a rural region  251 significantly in 2016; environmental loading, which improved over time; and environmental management, also improving over time. Since the level of more than half the dimensions did not change significantly across these years, they were seen as being reasonably stable over time in the context of rapid regional change.

Source: Walton and McCrea (2018).

Figure 16.2

Dimensions significantly changing in levels over time

Importance of Community Well-being and Resilience Dimensions Over Time (RQ2) Path analysis was used to test the empirical model shown in Figure 16.1 and Table 16.5 shows the importance of each dimension, as reflected in the beta values. The proportion of variation explained for overall community well-being, resilience and expected future community well-being are shown by the R-squared values. The importance of various dimensions was very stable, as hypothesised for RQ2. The only dimension to change significantly over time was the importance of economic opportunities for community resilience, which become significantly more important in 2016. Although only included for completeness of the model, it is interesting to note that perceptions of overall community resilience became significantly less important over time in predicting expectations about future community well-being. Residents’ expectations relied more on their current community well-being in 2018. The community may not have viewed overall community well-being as being as affected by onshore gas development than initially thought since community well-being was relatively stable over time (see Table 16.4) and perhaps the measure of community resilience as responding to CSG development became less relevant over time as it became less of an issue. However, perceived levels of community resilience also declined over time, which suggests that residents became less optimistic that their community was able to respond effectively to changes from onshore gas development – for example, there was the significant drop in employment opportunities in 2016.

252  Handbook of quality of life research Table 16.5

Changes in importance of community functioning and well-being dimensions over time

 

2014

 

Beta

p-value

Beta

2016 p-value

Beta

2018 p-value

 

 Signif. change

0.37

0.000

0.26

0.000

0.43

0.000

 

Overall community well-being: Community spirit, cohesion and trust

 

Services and facilities

0.24

0.000

0.37

0.000

0.19

0.000

 

Community participation and social interaction

0.18

0.000

0.16

0.000

0.15

0.001

 

Environmental loading

0.12

0.019

0.02

0.742

0.13

0.005

 

Personal safety

0.09

0.019

0.14

0.000

0.08

0.072

 

Built environment

0.09

0.054

0.09

0.086

0.07

0.114

 

Economic opportunities

0.01

0.848

0.06

0.152

0.06

0.187

 

Environmental management

–0.01

0.840

0.02

0.609

–0.02

0.602

 

R-squared

0.55

 

0.60

 

0.58

 

 

0.40

0.000

0.35

0.000

0.40

0.000

 

Overall community resilience: Environmental management

 

Community spirit, cohesion and trust

0.22

0.000

0.23

0.000

0.18

0.001

 

Economic opportunities

0.14

0.000

0.31

0.000

0.08

0.093

*

Built environment

0.13

0.003

0.06

0.242

–0.01

0.830

 

Environmental loading

0.11

0.019

0.08

0.124

0.15

0.013

 

Services and facilities

0.10

0.025

0.03

0.516

0.18

0.000

 

Community participation and social interaction

0.04

0.187

0.04

0.292

–0.02

0.691

 

Personal safety

–0.03

0.325

–0.03

0.429

–0.04

0.455

 

R-squared

0.62

 

0.64

 

0.50

 

 

0.52

0.000

0.51

0.000

0.66

0.000

*

Expected future community well-being: Overall community well-being

 

Overall community resilience

0.34

0.000

0.30

0.000

0.12

0.003

*

R-squared

0.56

 

0.52

 

0.52

 

 

Note: Betas as measures of importance; * significant change in betas over time (p < 0.05); R-squared = the proportion of variance in the dependent varible explained. Source: The authors.

Relating Levels and Importance of Community Well-being and Resilience Dimensions Over Time (RQ3) The relatedness of levels and importance of the dimensions was examined in two ways. First, with regard to RQ3, we examined how they were related over time – that is, whether the importance of a dimension changed when the satisfaction level of that dimension changed significantly (Table 16.6). While six dimensions changed significantly between 2014 and 2016 in level of satisfaction, only the economic opportunities dimension changed significantly in importance. Between 2016 and 2018, only two dimensions changed significantly in level; however, once again, only the economic opportunities dimension changed significantly in importance. We can say that the levels of satisfaction and importance of community well-being and resilience dimensions do not necessarily move together. There was also some support for the hypothesis that dimensions become more important if they fall to unsatisfactory levels in the case of economic opportunities. It may be that dimensions of community well-being and resilience need to drop to quite low levels before they increase in relative importance, as

Subjective community well-being and resilience in a rural region  253 Table 16.6

Changes in both levels and importance of community well-being and resilience dimensions over time

 

Change 2014–16

Change 2016–18

 

Level

Importance

Level

Importance

Community spirit, cohesion and trust

–0.04

–0.11

–0.01

0.17

Services and facilities

0.09

0.13

–0.14

–0.18

Community participation and social interaction

0.06

–0.03

–0.06

–0.01

Environmental loading

0.36*

–0.10

–0.07

0.12

Personal safety

–0.08

0.05

0.00

–0.06

Built environment

0.04

0.00

0.04

–0.02

Economic opportunities

–0.86*

0.05

0.30*

–0.01

Environmental management

0.19*

0.03

0.04

–0.05

Environmental management

0.19*

–0.05

0.04

0.05

Community spirit, cohesion and trust

–0.04

0.02

–0.01

–0.05

Economic opportunities

–0.86*

0.17*

0.30*

–0.23*

Built environment

0.04

–0.07

0.04

–0.07

Environmental loading

0.36*

–0.03

–0.07

0.08

Services and facilities

0.09

–0.07

–0.14

0.15

Community participation and social interaction

0.06

–0.01

–0.06

–0.06

Personal safety

–0.08

0.00

0.00

–0.01

Overall community well-being

0.00

–0.01

–0.07

0.15

Overall community resilience

0.03

–0.04

0.13

–0.18

Overall community well-being:

Overall community resilience:

Expected future community well-being:

Note: Level = change in level between years; importance = change in importance between years; italic number with * indicates a significant change between years (p < 0.05). Source: The authors.

was the case with economic opportunities. None of the other dimensions fell much below the mid-point of ‘3’ on the five-point scale at any time over the six-year period. Combined Contributions of the Satisfaction Level and Importance of Dimensions to Overall Community Well-being and Resilience (RQ4) With regard to RQ4, we examined the multiplicative contributions of each of the dimensions to overall community well-being and resilience. Multiplicative or weighted contributions of each dimension to overall community well-being and resilience were calculated as the product of their level and importance. Table 16.7 shows that community spirit, cohesion and trust was the highest contributor to overall community well-being in both 2014 and 2018, while services and facilities was higher in 2016. Both were ranked first or second in each year, both ahead of community participation and social interaction, which was ranked third in most years. This supports our hypothesis that social aspects of community well-being would be more important in contributing to overall community well-being in this rural region, given that two out of three of the top contributing dimensions related to social aspects of community life. As hypothesised, economic opportunities contributed less than social aspects and services and facilities to community well-being. In fact, it contributed surprisingly little. This may be because this measure was forward-looking in referring to ‘opportunities’. In particular, it

254  Handbook of quality of life research Table 16.7

Weighted contributions of dimensions to community well-being and resilience

 

Weighted Contributions to Community Well-being and Resilience

 

2014

2016

2018

Community spirit, cohesion and trust

10.36

0.82

10.46

Services and facilities

0.82

10.27

0.75

Community participation and social interaction

0.58

0.53

0.54

Environmental loading

0.31

0.14

0.27

Personal safety

0.36

0.56

0.38

Built environment

0.27

0.18

0.30

Economic opportunities

0.02

0.19

0.14

Environmental management

–0.02

0.06

–0.03

Environmental management

10.06

0.98

10.20

Community spirit, cohesion and trust

0.79

0.96

0.64

Built environment

0.48

0.16

0.03

Economic opportunities

0.40

0.68

0.20

Environmental loading

0.34

0.25

0.52

Services and facilities

0.33

0.10

0.62

Community participation and social interaction

0.14

0.11

–0.01

Personal safety

–0.12

–0.04

–0.07

Overall community well-being

20.02

10.93

20.31

Overall community resilience

10.23

10.14

0.61

Overall community well-being:

Overall community resilience:

Expected future community well-being:

Source: The authors.

referred to economic opportunities from onshore gas development, which were often less than expected, especially after the construction phase (Measham et al., 2019). Table 16.4 shows how economic opportunities declined significantly in 2016 after the construction phase. Consistent with research showing that social capital is important for both community well-being and resilience (Berkes and Ross, 2013; Besser et al., 2008; Cox, 1998), social dimensions relating to social capital and community efficacy were hypothesised to contribute most strongly to overall community resilience (that is, community spirit, cohesion and trust). However, the results showed that, in this rural region, environmental management contributed most strongly to community resilience. This reflects how important environmental management is seen in this highly productive irrigated farming region in combination with perceptions of the farming, underground water and natural environments being well managed. The combination makes managing the environment an important focus for community resilience in this region. Interestingly, overall community resilience contributed less over time to expectations about future community well-being. Contributions to expected future well-being were largely accounted for by current overall community well-being. As discussed above, this may reflect a perception that community resilience to onshore gas development was not as important for future community well-being as originally expected, as well as declining confidence that local communities could respond effectively to changes resulting from onshore gas development.

Subjective community well-being and resilience in a rural region  255

CONCLUSION The research reported here explored both the satisfaction level and importance of various subjective dimensions of community well-being (or community QOL) and resilience, interrelationships between them, and how combining both is a better estimate of each dimension’s contributions to overall community well-being and resilience. Overall community well-being was found to be quite stable over time, while perceptions of overall community resilience in the context of rapid and widespread onshore gas development declined over time. Expected future community well-being was based more on current community well-being rather than how effectively the community perceived it was responding to onshore gas development. This suggests that rural community well-being is quite robust and that even in a place experiencing major changes due to onshore gas, it is not ‘all about gas’. Similarly, the importance of various dimensions of community well-being and resilience were quite stable over time, suggesting that it is important to monitor levels of community well-being and resilience more regularly than their relative importance. The exception to this was economic opportunities, which dropped significantly and sharply in 2016 with a corresponding increase in its relative importance for community resilience. Its importance then declined again when economic opportunities increased significantly in 2018. This dynamic was not observed for other dimensions. However, we surmise that dimensions of community well-being and resilience need to drop sharply to unsatisfactory levels before such a dynamic is invoked. Social aspects of community well-being were found to contribute most to overall community well-being in this rural region when considering both their importance and their favourable ratings. For overall community resilience, it was environmental management that contributed most in this productive farming region. This method of combining or multiplying the satisfaction level and importance of each dimension was put forth as a new and useful way measuring the contribution of each dimension to overall community well-being and resilience.

REFERENCES Adger, W.N. (2003), ‘Social capital, collective action, and adaptation to climate change’, Economic Geography, 79, 387–404. Aldrich, D.P. and Meyer, M.A. (2015), ‘Social capital and community resilience’, American Behavioral Scientist, 59, 254–69. Australian Bureau of Statistics (ABS) (2018), Australian National Accounts: State Accounts, 2017–18 (5220.0), www​.abs​.gov​.au. Berkes, F. and Ross, H. (2013), ‘Community resilience: toward an integrated approach’, Society and Natural Resources, 26, 5–20. Besser, T.L., Recker, N. and Agnitsch, K. (2008), ‘The impact of economic shocks on quality of life and social capital in small towns’, Rural Sociology, 73, 580–604. Brown, K. and Westaway, E. (2011), ‘Agency, capacity, and resilience to environmental change: lessons from human development, well-being, and disasters’, Annual Review of Environment and Resources, 36, 321–42. Christakopoulou, S., Dawson, J. and Gari, A. (2001), ‘The community well-being questionnaire: theoretical context and initial assessment of its reliability and validity’, Social Indicators Research, 56, 321–51. Cox, E. (1998), ‘Measuring social capital as part of progress and well-being’, in R. Eckersley (ed.), Measuring Progress: Is Life Getting Better?, Canberra: CSIRO, pp. 157–67.

256  Handbook of quality of life research Crider, D.M., Willits, F.K. and Kanagy, C.L. (1991), ‘Rurality and well-being during the middle years of life’, Social Indicators Research, 24, 253–68. Cutter, S.L., Barnes, L. and Berry, M. et al. (2008), ‘A place-based model for understanding community resilience to natural disasters’, Global Environmental Change: Human and Policy Dimensions, 18, 598–606. Forjaz, M.J., Prieto-Flores, M.E. and Ayala, A. et al. (2011), ‘Measurement properties of the Community Wellbeing Index in older adults’, Quality of Life Research, 20, 733–43. Helliwell, J.F. and Putnam, R.D. (2004), ‘The social context of well-being’, Philosophical Transactions of the Royal Society B – Biological Sciences, 359, 1435–46. Hsieh, C.M. (2003), ‘Counting importance: the case of life satisfaction and relative domain importance’, Social Indicators Research, 61, 227–40. Ibrahim, M.F. and Chung, S.W. (2003), ‘Quality of life of residents living near industrial estates in Singapore’, Social Indicators Research, 61, 203–25. Jansen, S.J. (2020), ‘Urban, suburban or rural? Understanding preferences for the residential environment’, Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 13, 213–35. Leonard, R., McCrea, R. and Walton, A. (2016), ‘Perceptions of community responses to the unconventional gas industry: the importance of community agency’, Journal of Rural Studies, 48, 11–21. Lovejoy, K., Handy, S. and Mokhtarian, P. (2010), ‘Neighborhood satisfaction in suburban versus traditional environments: an evaluation of contributing characteristics in eight California neighborhoods’, Landscape and Urban Planning, 97, 37–48. Luke, H. and Emmanouil, N. (2019), ‘“All dressed up with nowhere to go”: navigating the coal seam gas boom in the Western Downs region of Queensland’, The Extractive Industries and Society, 6, 1350–61. McCrea, R., Shyy, T.-K. and Stimson, R. (2014), ‘Satisfied residents in different types of local areas: measuring what’s most important’, Social Indicators Research, 118, 87–101. McCrea, R., Walton, A. and Leonard, R. (2014), ‘A conceptual framework for investigating community wellbeing and resilience’, Rural Society, 23, 270–82. McCrea, R., Walton, A. and Leonard, R. (2016), ‘Developing a model of community wellbeing and resilience in response to change’, Social Indicators Research, 29, 195–214. McCrea, R., Walton, A. and Leonard, R. (2019), ‘Rural communities and unconventional gas development: what’s important for maintaining subjective community wellbeing and resilience over time?’, Journal of Rural Studies, 68, 87–99. McCrea, R., Western, M. and Shyy, T.K. (2011), ‘Subjective quality of life in Queensland: comparing metropolitan, regional and rural areas’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 295–313. Measham, T.G., Walton, A., Graham, P. and Fleming-Muñoz, D.A. (2019), ‘Living with resource booms and busts: employment scenarios and resilience to unconventional gas cyclical effects in Australia’, Energy Research and Social Science, 56, Article 101221. Messer, B. and Dillman, D. (2011), ‘Comparing urban and rural quality of life in the state of Washington’, in R.W. Marans and R.J. Stillmans (ed.), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 315–43. Moeinaddini, M., Asadi-Shekari, Z. and Aghaabbasi, M. et al. (2020), ‘Applying non-parametric models to explore urban life satisfaction in European cities’, Cities, 105, Article 102851. Norris, F.H., Stevens, S.P. and Pfefferbaum, B. et al. (2008), ‘Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness’, American Journal of Community Psychology, 4, 127–5. Onyx, J. and Bullen, P. (2000), ‘Measuring social capital in five communities’, The Journal of Applied Behavioral Science, 36, 23–42. Sirgy, M.J., Widgery, R.N., Lee, D.J. and Yu, G.B. (2010), ‘Developing a measure of community well-being based on perceptions of impact in various life domains’, Social Indicators Research, 96, 295–311. Walton, A. and McCrea, R. (2018), Trends in Community Wellbeing and Local Attitudes to Coal Seam Gas Development – 2014 – 2016 – 2018: Western Downs and Eastern Maranoa Regions, Queensland,

Subjective community well-being and resilience in a rural region  257 Survey Report, https://​gisera​.csiro​.au/​wp​-content/​uploads/​2018/​12/​GISERA​-Social​-10​-Final​-Report​ .pdf (accessed 8 January 2024). Walton, A., McCrea, R., Leonard, R. and Williams, R. (2013), ‘Resilience in a changing community landscape of coal seam gas: Chinchilla in Southern Queensland’, Journal of Economic and Social Policy (Special Edition: The Economic and Social Policy Implications of Coal Seam Gas Mining (CSG) in Australia), 15, 1–23. Walton, A., Williams, R. and Leonard, R. (2017), ‘Community perspectives of coal seam gas development during two phases of industry activity: construction and post-construction’, Rural Society, 26, 85–101. Western, J., Stimson, R., Baum, S. and Van Gellecum, Y. (2005), ‘Measuring community strength and social capital’, Regional Studies, 39, 1095–109.

17. Quality of life, amenities and recent migration across the largest US metropolitan areas Gordon F. Mulligan

INTRODUCTION This chapter seeks to clarify the effects of differences in quality of life (QOL) – measured by the geographic variation in natural and human-created amenities – on the recent migration patterns seen in American metropolitan areas. The notion is that through migration, people may enhance their well-being by making spatial substitutes between wages and amenities. Thus, in the US, West Coast cities like Los Angeles have attracted more migrants than East Coast cities like Baltimore or interior cities like St. Louis. Such a study is important because migration appears to have become under-appreciated, at least in the several handbooks devoted to QOL that have appeared since 2015 (see D’Ambrosio, 2018; Glatzer et al., 2015). Specifically, the chapter examines the migration flows between the nation’s largest 25 metropolitan areas during the period 2012–18. Log-linear regressions provide estimates of the two amenity effects during overlapping time periods. A gravity-type formulation makes migration estimates during 2012–18 using origin and destination employment sizes and the distances between places. Extensions include the effects of natural and human amenities; the effects of other attributes, including wage levels and unemployment rates, are also addressed. The main intent is to show how US households have sought to enhance their well-being by migrating, often substituting higher wages for superior amenities. The results indicate that the movement of people among the nation’s largest metropolitan areas continues to be driven by QOL considerations.

THE CONTEXT The terms spatial behaviour and population mobility are widely used to describe the different types of human movement that occur in virtually all societies (Golledge and Stimson, 1997; Plane and Rogerson, 1994). The most prominent of these moves involve a permanent (or semi-permanent) change in residence over a longer distance, with those moves together constituting what is referred to as migration. In contrast, those residential moves that are over a very short distance and take place within the same region constitute the special case of mobility (Clark and Moore, 1978). A large literature – addressing both domestic and international moves – examines the features of migration streams that exhibit known origins and destinations. Those people entering a region from an outside destination, for either a voluntary or involuntary reason, are called in-migrants, while those leaving the region from an inside origin are called out-migrants. These origin–destination flows are directional and so, as Rogers (1990) suggests, studies can 258

QOL, amenities and migration across the largest US metropolitan areas  259 focus on either the one-way flows, the sums of those flows (gross migration) or the differences in those flows (net migration). Migration occurs at different geographic scales and is selective, exhibiting considerable variety in both frequencies and durations, depending upon the age, education and genders of the individuals involved, the sizes of their various families, and the information and resources that are available to those people (Clark and Lisowski, 2020; Morrison and Clark, 2016; Schaeffer, 2017). Migration becomes increasingly important, relatively, once regions or nations have passed into the later stages of their demographic transition (De Jong and Gardner, 1981; Rees et al., 2017). A region’s demographic and economic processes become very tightly intertwined, so many migration studies are plagued by endogeneity problems. Over three distinct parts, the discussion in this chapter provides: ● an overview of the migration literature most relevant to the field of regional development, especially in the US; ● a method for calculating standardised amenity scores in the very largest US metropolitan areas; and ● a means for estimating the effects of natural and human amenities on US inter-metropolitan migration during recent times. Different versions of distance-decay models are constructed to disclose the importance of amenities and other factors in the migration among the nation’s 25 largest metropolitan areas. In each case, there are three key underlying determinants of this movement: ● size of the origin region (place); ● size of the destination region (place); and ● distance between the origin region and destination region. Early Contributions to the Study of Migration The early literature on migration was informed by various disciplines: ● demographers introduced cohort-survival models to disaggregate migrant populations (Keyfitz and Caswell, 2005; Rogers, 1968); ● economists demonstrated that people often trade off high wages for valued amenities (Greenwood, 1975; Greenwood and Hunt, 2003); ● geographers revealed that the locations of amenities vary throughout space (Downs and Stea, 1973; Ullman, 1956); and ● regional scientists paid special attention to how distance attenuates human interaction between locations (Alonso, 1978; Isard, 1960). Notable attempts were made to integrate some of these factors into more complex models (Isserman, 1986; Plaut, 1986), while other studies clarified how the choices and fortunes of people depend upon different aspects of their life courses (Easterlin, 1980; Rossi, 1955). Important and underappreciated advances were also made in conceptualising migration as an investment decision (Sjaastad, 1962).

260  Handbook of quality of life research Recent Contributions The recent migration literature has focused on several key themes, indicating that QOL will continue to exhibit visible spatial shifts in the future. Moreover, many of these shifts will have public policy implications that are not entirely understood. Some of the more important themes include the following: 1. First, building on the ideas of Rosen (1974) and Roback (1982), studies have continued to demonstrate how local labour and property markets affect the QOL decisions made by the residents of both large and small cities (Carruthers and Mundy, 2006; Orrenius and Zavodny, 2018; Partridge et al., 2010). One recent study has even used a general-equilibrium framework to estimate the willingness-to-pay of households and firms that locate in various places (Albouy, 2008; Albouy et al., 2013). Cross-national differences appear to persist in how amenities are valued, even when similar nations like Canada and the US are compared. These various studies strongly endorse the idea that QOL differences lead to regional differences in employment growth and population change. 2. Second, extending the adjustment models popularised by Carlino and Mills (1987) and Clark and Murphy (1996), attempts have been made to adapt this ‘dynamic’ model to different times and places. Recently, Mulligan et al. (2020) demonstrated that, in the metropolitan US, ‘people followed jobs’ prior to the year 2000 but, since that time, a reversal transpired so that ‘jobs followed people’ (Greenwood et al., 1987). In short, the balance of influence between population and employment in the nation’s metropolitan labour markets gradually shifted as households became older and wealthier, while firms, especially those involved in manufacturing, became smaller and more labour intensive. The decisions of people to migrate has clearly become affected by the heterogeneous positioning of both natural and human amenities, and these decisions have subsequently affected the growth seen in US metropolitan employment. Obviously, too, the geography of QOL has changed over time and this has also shifted the regional preferences of US households (Beyers and Lindahl, 1995). 3. Third, a lot of interest has recently focused on the key role played by highly educated households and highly skilled workers in regional development. While this research often claims to address all types of human capital, most recent studies have focused solely on the behaviour of the college educated (Faggian et al., 2019; Venhorst, 2017; Whisler et al., 2008). These people, mainly confined to the earliest stages of the life cycle, seem to be especially attracted to areas that are rich in natural or human amenities. Moreover, there is evidence that the residential choices (or adopted lifestyles) made by creative and entrepreneurial people eventually confer substantial benefits on (already) amenity-rich regions, especially through growth in the artistic and high-tech sectors and the rise seen in property values (Florida et al., 2008). In fact, this topic has recharged interest in the long-standing debate on the merits of ‘person-versus-place’ policies for assisting troubled or lagging regions (Austin et al., 2018; Partridge et al., 2009). 4. Fourth, in attempts to enhance their QOL or well-being, households continue to reposition themselves spatially according to their real preferences and perceived threats. In the past decade or so, it often seems that environmental disamenities have come to outweigh pecuniary advantages as both households and firms have departed from dense, congested

QOL, amenities and migration across the largest US metropolitan areas  261 metropolitan centres for the lower densities of micropolitan centres or the remoteness of rural areas (DeVol and Crew, 2019; Mulligan and Vias, 2006). This geographic and social sorting continues unabated – both within and across the USA’s largest metropolitan centres – and it has become a topic that requires more study by social scientists with different skills and posing different questions (Ellen and O’Regan, 2010). Furthermore, proposals for fiscal transfers from cities to suburbs, or from metropolitan to non-metropolitan areas, are now being muted by the extensive but nuanced population movements that are currently taking place across the US urban hierarchy (Haughwout, 2010). 5. Fifth, some ten years ago, evidence began to mount that the cores of some very large US cities were losing people (Cox, 2010). In more recent years, UK observers have noted that people living in London were departing by so-called ‘elevators’ to peripheral places like Reading and Luton, with superior transportation options, while in Australia people living in Sydney were moving northwards to less congested Brisbane and other places in Queensland. With the early advances made in teleworking, a preference for more living space was a major driving force behind these dispersals. Then, in early 2020, the sudden appearance of the COVID-19 virus accelerated this trend, and streams of (mostly younger) people left these places and other great cities like New York, Tokyo and Toronto in search of locations that might facilitate physical distancing, expose households to less crime and provide this extra living space on a more affordable basis (Roberts, 2020; The Economist, 2020). Six months later, many hundreds of thousands of inner-city households had exited the inner realms of the so-called world cities: more Londoners had moved out to Croydon, down to places along the English Channel, and up to the smaller cities of the Midlands; New Yorkers had rushed over to Brooklyn and even farther afield into up-state New York and New England; and Torontonians had migrated out to Hamilton, a former steel mill community, or to one of the many smaller places strung out along the northern shores of Lakes Ontario and Erie. This huge and rapid dispersal, along with its substantial vacancy chains, was not at all anticipated by urban planners and the magnitude of these out-movements will have serious short-run, and possibly long-run, consequences for the redistribution of income, human capital and wealth across many of the more advanced countries. The rapid adoption of new technologies, like Zoom suggest that many people, especially those with established careers, might prefer to live permanently in sparsely settled areas and commute electronically. Continued Importance of Migration In the context of QOL studies, consideration of migration is vitally important for numerous reasons (Corcoran and Faggian, 2017; Franklin, 2020; Marans and Stimson, 2011; Molloy et al., 2011; Newbold, 2017; Poot et al., 2013). In the most general way, migration steadily changes each society’s landscape of wealth, health and politics. Very clearly, too, at different geographic scales, migration is not only driven by the existing geography of amenities, but the process itself then reassigns those amenities in space following the movement of both people and businesses (Partridge et al., 2010). So, in seeking to improve their QOL, people induce many direct and indirect socio-economic changes on the metropolitan landscape. Among other things, migration changes the internal social fabric of large metropolitan areas (Frey, 2015; Melby, 2020), shifts the fiscal balance between our inner and outer neighbourhoods (Kotkin, 2016) and, at various scales, rearranges

262  Handbook of quality of life research the so-called geography of money and finance (Martin and Pollard, 2017). Governments must recognise, even anticipate, some of these changes to avoid ensuing distortions in land and property markets, localised traffic bottlenecks and the demise of public goods and services, including education.

METHODOLOGY Measuring Amenities: Using Standard Scores In the study of migration in this chapter, amenities are measured using data available in the summary statistics for the USA’s metropolitan areas (Table 17.1), where the nation’s 25 most populous cities are listed in order from New York, the largest place in 2019 with more than 19 million residents, to Portland, OR, the smallest place with slightly fewer than 2.5 million residents. The second column of data shows those metropolitan populations (nearly) a decade earlier. Table 17.1

Population sizes and standardised amenity scores

Metro Area

Pop. Mil. 2019

Pop. Mil. 2010

Natural Amenities

Human Amenities 2015

New York

19.22

18.90

–0.782 (20)

1.314 (3)

Los Angeles

13.21

12.83

1.018 (6)

1.538 (1)

Chicago

9.46

9.46

–1.310 (24)

1.030 (4)

Dallas

7.57

6.37

0.222 (12)

0.581 (7)

Houston

7.06

5.92

0.765 (8)

–1.136 (22)

Washington

6.28

5.64

–0.446 (16)

–0.897 (21)

Miami

6.16

5.16

0.859 (7)

0.230 (10)

Philadelphia

6.10

5.96

–0.544 (18)

–1.797 (25)

Atlanta

6.02

5.29

0.433 (10)

–0.653 (20)

Phoenix

4.95

4.19

0.197 (13)

–0.392 (17)

Boston

4.87

4.55

–1.402 (21)

–0.023 (14)

San Francisco

4.73

4.33

1.581 (1)

0.556 (8)

Riverside

4.65

4.22

1.026 (5)

–0.437 (18)

Detroit

4.32

4.30

–1.286 (23)

0.227 (11)

Seattle

3.98

3.44

–0.273 (15)

0.596 (6)

Minneapolis

3.65

3.35

–2.003 (25)

0.167 (12)

San Diego

3.34

3.10

1.533 (2)

0.036 (13)

Tampa

3.19

2.78

1.027 (4)

0.500 (9)

Denver

2.97

2.54

–1.212 (22)

0.725 (5)

St. Louis

2.80

2.79

–0.750 (19)

–1.219 (23)

Baltimore

2.80

2.71

–0.465 (17)

–1.638 (24)

Charlotte

2.64

2.24

0.299 (11)

–0.650 (19)

Orlando

2.61

2.13

1.113 (3)

–0.083 (15)

San Antonio

2.55

2.14

0.531 (9)

–0.342 (16)

Portland

2.49

2.23

–0.132 (14)

1.767 (1)

Note: Amenity rankings are shown in parentheses. Source: The author.

Natural amenities are estimated by noting the magnitude of heating and cooling degree days, thereby reflecting the different amounts of energy needed to keep local temperatures in a com-

QOL, amenities and migration across the largest US metropolitan areas  263 fortable range (Savageau and Boyer, 1993). Other indicators, some perhaps better known, could have been adopted instead (Cadwallader, 1996; McGranahan, 1993). Human amenities could have been estimated by adopting the standardised residuals from the log-linear regression of median house price in 2015 on median household income in that year and total degree days (Carruthers and Mulligan, 2006; Glaeser and Maré, 2001). But this method was given a new twist as a transformed index of land-use regulation was included as a third explanatory variable (Gyourko et al., 2008). Human amenity valuations were (significantly) driven higher by rising income and stricter land-use policies but lowered by rising total degree days (indicating climate extremes). Substituting average wage for median income or, instead, dividing total degree days into its heating and cooling components, made only minor changes to the overall estimation. In the third column of Table 17.1 natural amenity as represented by the standardised total degree days were used as an index to rank cities, with San Francisco ranking highest and Minneapolis ranking lowest. The final column of Table 17.1 indicates the human amenity estimates after the regression residuals were transformed into standard scores. Many, but not all, of the nation’s very largest cities ranked high on human-created amenities but a few smaller cities, like Portland, OR and Denver, CO, also performed well on this dimension. It is worth emphasising that, by design, the two types of amenity scores exhibited no correlation. Calculating a rank sum from the two unweighted ranks gives a rough indication of how overall amenities are currently distributed across the nation’s 25 largest metropolitan areas. In 2015, the ranks suggest that the best combinations of the two amenities existed in Los Angeles (where 6 + 1 = 7, where the overall rank is (1)), San Francisco (9) and Tampa (13), and the worst combinations existed in Philadelphia (18 + 25 = 43, where the overall rank is (25)), St. Louis (42) and Baltimore (41). It is reassuring that these overall rankings are remarkably similar – as indicated by a Spearman correlation coefficient of r = 0.615 – to the general equilibrium estimates found in Albouy’s highly regarded research (2008; see pp. 58–62). Moreover, reweighting the pair of amenity scores by the two elasticities in the log-linear regression – an operation that upgrades the weighting of the human scores from 0.500 to 0.625 and downgrades the natural scores from 0.500 to 0.375 – does very little to shift the overall amenity rankings. In either case, the highest overall rankings were found throughout the Sunbelt and along the West Coast, while the lowest overall rankings were found in the nation’s northern interior and along the Northeastern Seaboard. Given the conjecture that migrants are attracted to places with high levels of both types of amenities, it follows that West Coast cities have generally benefited more from amenities than East Coast cities. The Migration Model The logic underlying the model of migration follows that suggested by Lowry (1966) and extended by others, including Porell (1982) and Plane and Rogerson (1994). This model exhibits standard distance-decay properties and different versions of the model are estimated in a series of steps. However, each version of the model retains the common logic that migration between an origin and destination is a positive function of the differences in the key attributes of those two places and a negative function of the physical distance separating those places. So, a hypothetical migrant compares conditions in the origin to those in the destination and then decides to move or stay, with the overall intention of improving their QOL or economic well-being. These decisions are reached independently, and, in the aggregate, the various

264  Handbook of quality of life research households cannot anticipate how their behaviour will affect matters, either in the destination or origin region. Although the implications of this shift in the supply of people (workers) is important to recognise, the (endogenous) adjustment of the regional labour markets is not addressed in the discussion that follows. Each model essentially shows the expected aggregate results across these separate migration decisions: ● Model 1 introduces employment as the key driver, where distance acts as a deterrent to the movement of people. ● Model 2 brings natural amenities into the conversation. ● Model 3 then brings in human-created amenities. ● These two amenity effects are then considered together in Model 4, which represents the simplest version of the general model. ● Models 5, 6 and 7 estimate these pairs of amenity effects for differential metropolitan nominal wages, prices (addressing cost of living) and unemployment rates.

EMPIRICAL RESULTS The Data Most of the data used in the analysis was collected by the US Census Bureau1 and then made available in the American Community Surveys. First, the annual estimates for metropolitan employment, wages and prices, all of which conform to the Census Bureau standards, were generated by the US Bureau of Economic Analysis (BEA).2 Second, rates were taken from the Bureau of Labor Statistics (BLS).3 The variables used to measure the incidence of natural amenities were collected from the Places Rated Almanac (Savageau and Boyer, 1993). More detailed information about these and related data can be found in various recent chapters and papers by the author (including Mulligan et al., 2020). As pointed out already, all estimates were made for two overlapping time periods between 2012 and 2018: the first (earliest) period, 2012–16, is denoted by the number 1 and the second (latest), 2014–18, by the number 2. The Amenity Effects on Migration In Table 17.2 the second column shows the three estimates of the standard distance-decay model, where all the variables are expressed in logarithmic format: ● origin employment (EMORIG); ● destination employment (EMDEST); and ● the spherical distance (DISTAN) between the origin and destination. Employment was chosen instead of population because job numbers are more relevant than person numbers for household migration decisions in an adjusting space economy. So, the various regression estimates indicate (at the appropriate means) the percentage responses in one-way migrant volumes to 1 per cent changes in EMORIG, EMDEST, DISTAN and the like.

QOL, amenities and migration across the largest US metropolitan areas  265 In addition, pooled estimates are given with a time dummy indicating the required intercept term. Both inflows and outflows from each metropolitan area were used in the pooled estimation, so a grand total of 1200 observations were available: 1200 = 25 (# metro areas) × 24 (# other metro areas) × 1 (inflows or outflows) × 2 (# time periods). As mentioned above, the standard case (Model 1) is extended on a step-by-step basis to include natural amenities (Model 2), human amenities (Model 3), both amenities (Model 4), nominal wages (Model 5), prices (Model 6) and unemployment levels (Model 7). Model 1 The first column of figures in Table 17.2 are the estimates for the basic three-variable model. Note that during 2014–18, the second period, the elasticity for distance decay was estimated as DISTAN2 = –0.667, indicating that a 10 per cent increase in distance between two average-sized metropolitan areas led to a decline of 6.67 per cent in the volume of migration between those areas. The distance deterrence was slightly steeper, DISTAN1 = –0.702, for the earlier period 2012–16, and the estimate ‘averaged out’ to DISTAN = –0.685 for the migration flows when the data were pooled across the two time periods. Note, too, that employment in the origin city always had a much stronger effect on migration than employment in the destination city, and this effect only became more pronounced over time. The ratio EMORIG/ EMDEST between the two employment estimates was 1.26 in the first five-year period (using 2010 job figures) but somewhat greater, 1.39, in the second period (using 2015 job figures). In fact, the pooled estimates indicate that the origin (repulsive) effect on migration was always about one-third stronger than the destination (attraction) effect. Separate regressions (results not shown) indicate that these two effects are even more disparate when population replaces employment (or, alternatively, the eligible labour force) as the measure of metropolitan size. Clearly, human movement was displaced outwards more by the job numbers in the prior city-of-residence than it was returned inwards by the job numbers in the new city of residence. Of course, this is a general conclusion that might not be appropriate for any given pairs of cities. More complicated models will indicate whether this gap in the two employment effects was dampened at all by the geographic heterogeneity existing in natural and human-created amenities (see below). Model 2 The second column of figures of Table 17.2 includes the effects for natural amenities as measured by logarithms of total degree days. Again, these can be interpreted as origin- and destination-specific elasticities, where a higher (negative) number indicates that the effect of natural amenities is more pronounced. Note in the second period a 10 per cent increase in total degree days at the origin lowers the outflow by 3.6 per cent but a similar increase at the destination lowers the inflow by 7.7 per cent. The properties of these amenity estimates are consistent across both periods although both the origin- and destination-specific effects were higher during the earlier period. However, the inclusion of natural amenities had only a slight effect on the three estimates of the standard distance-deterrence model, although each of the effects identified above became stronger. The two new variables reduced the standard error of the estimate and enhanced the adjusted R2 in the order of 6–7 per cent, so a migration model that does not include consideration of natural amenities will be somewhat incomplete.

266  Handbook of quality of life research Table 17.2

The effects of amenities on inter-metropolitan migration

 

Model 1

Model 2

Model 3

Constant

–13.006 (–17.6)

–4.274 (–4.0)

–12.569 (–17.8)

EMORIG2

0.979 (29.3)

1.001 (31.3)

0.976 (30.6)

EMDEST2

0.704 (21.1)

0.752 (23.5)

0.701 (21.9) –0.714 (–30.9)

DISTAN2

–0.667 (–28.1)

–0.680 (–30.2)

NTAMOR2

 

–0.356 (–5.0)

 

NTAMDE2

 

–0.772 (–10.8)

 

HUAMOR2

 

 

0.566 (6.8)

HUAMDE2

 

 

0.729 (8.7)

SEE, Adj R2

0.6424, 0.634

0.6089, 0.671

0.6141, 0.666

 

 

 

 

Constant

–13.401 (–18.7)

–3.096 (–3.1)

–13.311 (–18.9)

EMORIG1

0.968 (29.7)

1.009 (32.9)

0.971 (30.4)

EMDEST1

0.766 (23.5)

0.828 (26.9)

0.770 (24.1)

DISTAN1

–0.702 (–30.0)

–0.718 (–33.0)

–0.729 (–31.5)  

NTAMOR1

 

–0.551 (–7.9)

NTAMDE1

 

–0.816 (–11.8)

 

HUAMOR1

 

 

0.487 (5.0) 0.548 (5.6)

HUAMDE1

 

 

SEE, Adj. R2

0.6324, 0.661

0.5869, 0.708

0.6192, 0.675

 

 

 

 

Constant

–13.158 (–25.6)

–3.637 (–5.0)

–12.908 (–26.1)

DUMMY

–0.345 (–3.9)

–0.382 (–4.6)

–0.345 (–4.1)

EMORIG

0.973 (41.7)

1.004 (45.3)

0.974 (43.1)

EMDEST

0.736 (31.5)

0.790 (35.6)

0.736 (32.6) –0.721 (–44.1)

DISTAN

–0.685 (–41.1)

–0.699 (–44.6)

NTAMOR

 

–0.453 (–9.1)

 

NTAMDE

 

–0.793 (–15.9)

 

HUAMOR

 

 

0.533 (8.4)

HUAMDE

 

 

0.651 (10.2)

SEE, Adj. R2

0.6374, 0.649

 

0.6167, 0.671

Note: t-scores are shown in parentheses. Source: The author.

Model 3 The last column of figures of Table 17.2 shows the effects of human-created amenities as calculated by the method outlined earlier. Pooled over the two time periods the elasticity estimate at the origin is 0.533 and that at the destination is 0.651, suggesting that people often left places with high human amenities in search of alternative places with even better human amenities; however, the difference in the two regression coefficients is not significant. The origin and destination estimates are both higher in the more recent five-year period (0.566 > 0.487 and 0.729 > 0.548), indicating that human amenities became a more important determinant of inter-metropolitan migration patterns as the decade unfolded. However, the explanatory power of Model 2 remains somewhat superior to that of Model 3, indicating that natural amenities likely had a bigger impact on migration flows than did human amenities over the two time periods.

QOL, amenities and migration across the largest US metropolitan areas  267 Model 4 The first column of figures in Table 17.3 shows the various elasticities for the pooled data when both natural and human-created amenities are included. These estimates are nearly the same as those shown in Models 2 and 3 because the two types of amenities were designed to be orthogonal. Now the metropolitan destination effects prove greater than the matching origin effects for both types of amenities, and this differential is more pronounced for natural amenities. Table 17.3

Other factors affecting the amenity estimates

 

Model 4

Model 5

Model 6

Model 7

Constant

–3.308 (–4.7)

–17.282 (–12.7)

–10.428 (–9.6)

–8.782 (–6.3)

DUMMY

–0.382 (–4.8)

–0.996 (–10.7)

–1.088 (–9.7)

–1.439 (–6.6)

EMORIG

1.005 (47.1)

0.894 (34.9)

0.989 (49.7)

0.982 (43.3)

EMDEST

0.791 (37.1)

0.644 (25.2)

0.712 (32.0)

0.732 (32.3)

DISTAN

–0.736 (–48.1)

–0.758 (–50.1)

–0.748 (–49.7)

–0.747 (–49.8)

NTAMOR

–0.458 (–9.6)

–0.523 (–11.0)

–0.505 (–9.7)

–0.499 (–9.6)

NTAMDE

–0.798 (–16.6)

–0.883 (–18.6)

–1.026 (–19.8)

–1.043 (–20.1)

HUAMOR

0.541 (9.1)

0.550 (9.6)

0.584 (9.6)

0.616 (9.5)

HUAMDE

0.659 (11.1)

0.667 (11.6)

0.852 (13.9)

0.760 (11.7)

NWAGOR

 

0.762 (7.4)

 

 

NWAGDE

 

1.008 (9.7)

 

 

RWAGOR

 

 

0.295 (2.1)

0.405 (2.5)

RWAGDE

 

 

1.475 (10.3)

1.157 (7.1)

UNEMOR

 

 

 

0.110 (1.5)

UNEMDE

 

 

 

–0.316 (–4.2)

SEE, Adj R2

0.5756, 0.714

0.5593, 0.729

 

 

Note: t-scores are shown in parentheses. Source: The author.

Model 5 The second column of figures in Table 17.3 control the various amenity estimates for the nominal wage differentials (that is, NWAGOR versus NWAGDE) between the origins and destinations. One substantial change occurs in the estimates of the size variables for the various origins and destinations: the employment elasticity falls from 1.005 to 0.894 (–11.0 per cent) across the origins and the employment elasticity falls from 0.791 to 0.644 (–18.6 per cent) across the destinations. But it seems that nominal wages only play an important role in shifting the estimates for natural amenities, where the (negative) elasticity climbs from –0.458 to –0.523 (12.0 per cent) across the origins and climbs from –0.798 to –0.883 (10.6 per cent) across the destinations. There are no comparable shifts that occur in the elasticity estimates for human amenities. Nevertheless, the explanatory power of the model is substantially enhanced by the addition of differential wages. This finding endorses the view that households are willing to forgo higher wages for better natural and human-created amenities. In other words, households often seek out metropolitan areas with superior amenities to improve their quality of life.

268  Handbook of quality of life research Model 6 Given the mixed findings in Model 5, nominal wages were adjusted for the substantial differences in purchasing parity across the metropolitan areas. The overall index calculated by the BEA, which is standardised across the nation, was used for this transformation as that index addresses goods, services and housing costs. This led to the creation of origin- and destination-specific real wages RWAGOR and RWAGDE, respectively. The regression results are shown in the third column of figures in Table 17.3. The main changes can be seen in the estimates for the destination-specific amenity effects: note the elasticity for natural amenities shifts from –0.883 to –1.026 and the elasticity for human amenities shifts from 0.667 to 0.852. Moreover, the substitution of real wages for nominal wages mutes the origin-wage effect and strengthens the destination-wage effect. But, in the end, the use of real wages only fine tunes the results seen earlier for nominal wages. Model 7 The final column of Table 17.3 shows the shifts that take place in the amenity estimates once the metropolitan differences in unemployment rates have been accounted for. The estimates indicate, albeit somewhat weakly, that people leave those metropolitan areas with high unemployment but then avoid other metropolitan areas with similarly high unemployment. Now, however, several of the amenity shifts noted above for Model 6 are reversed. Ordered Importance of the Amenity Effects In the end, the importance of the four location-specific amenity effects for Model 7 can be ordered as follows: ● ● ● ●

natural, destinations (–1.043); human, destinations (0.760); human, origins (0.616); and natural, origins (–0.499).

Moreover, separate regressions (results not shown) for the two five-year periods suggest that the effects of natural amenities were more stable than those for human amenities, largely because the importance of the latter in determining migration patterns grew stronger over time. Evidently migration decisions are especially impacted by the differences in the levels of natural amenities between origins and destinations, where households leave those places with moderately extreme climatic conditions and avoid those other locations with even more extreme conditions. However, human-created amenities are very important because these are much more amenable to public policy choices, especially to those involving local taxes and expenditure patterns.

CONCLUDING REMARKS This chapter has examined the issue of recent amenity-driven migration across the very largest US metropolitan areas. Migration is worthy of our continued attention because it has so many short- and long-term economic, geographic, social and political implications, especially in countries nearing the end of their demographic transition. The middle part of the chapter

QOL, amenities and migration across the largest US metropolitan areas  269 discussed, in some detail, the range of these various implications. The last part of the chapter focused on the interpretation of Ordinary Least Square (OLS) regression results using migration data for the two five-year periods 2012–16 and 2014–18. When the data were pooled, and both size and distance effects were accounted for, the destination effects for amenities invariably proved to be stronger than the origin effects. In summary, people clearly were repulsed by places with worse natural amenities (more extreme climates) and they were attracted to places with superior human-created amenities. The repulsion of natural amenities and the attraction of human-created amenities directly bear on QOL. Moreover, the inclusion of amenities in the estimation only affected the absolute values of the origin- and destination-specific employment effects; surprisingly, little change occurred in the relative gap (or ratio) between the larger origin employment effect and the smaller destination employment effect once other factors were considered. Traditional studies of intra- and inter-regional movement have adopted the trade-off approach between wages and amenities as espoused by Rosen (1974), and this approach has been favourably compared to others (Carlsen and Leknes, 2021). With the great improvements made in the sharing of information, both about jobs and houses but also schools and recreation, more attention should be given to how households trade off one amenity type for the another while their wages are held constant. The analysis of inter-metropolitan migration flows could be improved in other ways: 1. First, more large places could be included in the estimation to see if the estimates shown above hold over lower levels of the urban hierarchy. A preliminary analysis of all metropolitan areas having more than 1 million people suggests that the results for Model 1 in Table 17.2 are entirely reasonable. 2. Second, as Albouy (2008) recognised in earlier census definitions, some of the observation units could be modified, either to include a smaller nearby metropolitan place (for example, adding San Jose to San Francisco and Oakland) or consolidating two large places (for example, Los Angeles and Riverside). 3. Third, the spatial attributes of the estimation might be improved by considering other approaches. Following Fik and Mulligan (1990), the regression models could be modified to include the possibility of competing destinations in migration. Or, alternatively, the spatial variation in the amenity estimates could be analysed by adopting some sort of geographically weighted regression model (Fotheringham et al., 2002). 4. Finally, the alternative five-year migration flows for 2011–15 could be studied to see how the results shown above vary across different household characteristics like age and race. There are also good reasons to consider disaggregating the amenity measures to distinguish between heating degree and cooling degree days on the one hand, and different types of taxes or fiscal expenditures on the other (Clark, 2006). The American economy is constantly reorganising in space but, with the advent of COVID-19, many changes in employment and migration are now unfolding very quickly. Events were already tipping the growth paradigm away from ‘people following jobs’ toward ‘jobs following people’, and the availability of new communication technologies is quickly making urban dispersal an important force in regional development. Some people will permanently prefer the extra living space that comes with either a suburban, micropolitan or rural residence, and those low-density locations will be shocked by the sudden influx of people hoping to enhance their well-being or life satisfaction. In these cases, people, ‘voting with their feet’, will have demon-

270  Handbook of quality of life research strated their preferences for more natural amenities and fewer human amenities. In other cases, people will return to large metropolitan areas and demonstrate their preferences for restaurants, entertainment venues, museums and other human amenities. The decisions of immigrants will likely remain consistent with the past, but the decisions of the nation’s domestic migrants are much more flexible. During this adjustment period, the consequences for real-estate markets, local-government revenues and, in general, household resource inequalities could prove to be unprecedented. So, it remains unclear whether a different low-density spatial equilibrium will be reached as a new generational norm unfolds, or whether a gradual return to our former high residential densities will occur over time. In any case, it follows that the stock-like attributes of human-created amenities will have to be maintained, or even enhanced, to remain critical to the growth prospects of the very largest destination cities. Overall, though, more research is clearly needed to articulate the complex streams of inter-metropolitan movement with the increasingly popular ‘jobs following people’ growth paradigm. Clearly the issues discussed in this chapter can have both direct and indirect implications that may affect people’s QOL and well-being.

NOTES 1. See the United Census Bureau website: https://​www​.census​.gov/​. 2. See the BEA website: www​.bea​.gov. 3. See the BLS website: www​.bls​.gov.

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PART V PUBLIC SPACES, QUALITY OF LIFE AND THE ENVIRONMENT

18. Residential neighbourhoods, nearby nature and quality of life Sara Hadavi

INTRODUCTION Different disciplines have different perspectives on the built environment and quality of life (QOL). Putting these two concepts together calls for interdisciplinarity, which is especially true when seeking practical solutions to life challenges, many of which are influenced by where we live. What aspects of the built environment impact well-being and QOL in urban spaces? Does satisfaction with the neighbourhood in which we live matter to our well-being? Does our neighbourhood use patterns have any associations with our sense of well-being? What if we believe there are barriers to use of outdoor spaces? In what way could it affect our well-being? These are key questions that tend to have been separately addressed in existing research in environmental psychology and other fields. This chapter examines these topics through a landscape architecture lens, arguing that to have better insights into creating more liveable spaces a holistic and more detailed approach is needed that considers those questions together. Such a perspective is expected to increase translatability of findings that may lead to applicable planning and design solutions in ways that might improve residents’ sense of well-being and QOL in urban areas. The chapter considers the concept of neighbourhood satisfaction and its multidimensionality as well as its association with greenness, life satisfaction and well-being. It discusses the associations between nearby nature both as a physical feature of neighbourhoods and as an evolutionary need that impacts people’s preferences, satisfaction, open space use patterns and well-being, all as interrelated indicators of QOL in residential areas. The chapter first overviews the literature on these topics and then presents empirical findings from a multifaceted study that examines those factors. It concludes with a discussion on the practicality of the suggested approach for planners, urban designers and policymakers in seeking to create more liveable neighbourhood outdoor spaces that contribute to people’s well-being and QOL.

NEIGHBOURHOOD SATISFACTION AND OUTDOOR SETTINGS Meeting Needs and Preferences Evolutionary approaches to cognition suggest that our judgements of outdoor settings have evolved through generations. Like our ancestors, we have a fast-paced mechanism to unconsciously evaluate outdoor settings based on the extent to which they might meet our needs and help us function effectively. This forms the basis of preferences for outdoor environments that satisfy our needs for information, safety and resources (Hadavi and Sullivan, 2018; Kaplan and 275

276  Handbook of quality of life research Kaplan, 1982) and helps us to function well and thrive (Charlesworth, 1995; Kaplan, 1983; Sullivan, 2005). Preference for nature settings in urban environments fits well in this evolutionary framework (Hadavi and Sullivan, 2018; Sullivan, 2005). Research shows that people have strong preferences for urban natural settings with various types and amounts of vegetation (Hadavi et al., 2015; Hur and Nasar, 2014; Jiang et al., 2015; Kaplan, 2001; Suppakittpaisarn et al., 2019). Accordingly, residential areas providing opportunities for contact with nature are shown to improve residents’ level of satisfaction with their neighbourhood. Nature Settings and People’s Needs Much research demonstrates that the physical aspects of outdoor environments have significant impacts on people who use those environments daily. Availability of opportunities for daily contact with nature is essential for psychological restoration and well-being in large cities (Hartig et al., 2011; Kaplan, 1995; Thompson, 2011). However, if outdoor spaces that provide nearby nature do not meet people’s needs and preferences then they may not be used as frequently and might not have the expected impact. It is thus plausible to include residents’ needs and preferences in the process of decision-making regarding the provision of public outdoor spaces. While an extensive body of evidence in environment–behaviour studies focus on environmental preferences (see Han, 2010; Herzog, 1989; Van den Berg et al., 2007), mounting empirical research with a focus on preferred qualities of the urban nature is helping planners and designers in decision-making. Preference studies uncover people’s needs, what they can do and how they can function in outdoor spaces. Such studies typically use photographs as surrogates for real landscape scenes (Coeterier, 1983; Hadavi et al., 2015; Kaplan, 1985) to identify the spatial characteristics of the environments that people prefer to use. Studies show that better access to safe public open spaces can predict more frequent walking in neighbourhoods and increase the amount of time spent outdoors (Giles-Corti et al., 2005; Humpel et al., 2004; Kaplan and Kaplan, 2003). In contrast, unpleasant scenery (Giles-Corti et al., 2005; Parkes et al., 2002), long distances to public spaces and amenities (Wendel et al., 2012), low maintenance (Hur and Nasar, 2014; Kaplan, 1985) and safety concerns (Ward Thompson and Aspinall, 2011) are associated with lower neighbourhood satisfaction and discourage people from spending time outdoors. Environmental Affordances According to Gibson’s affordance theory, people prefer an environment over others if that environment affords to provide opportunities for meaningful functions and activities that matter to users (Clark and Uzzell, 2006). Despite their significant role in preference judgements, environmental affordances have been mostly overlooked in preference studies and are mainly limited to certain age groups. When social, physical and emotional affordances of an environment are compatible with one’s needs and purposes, they facilitate effective functioning, which impacts preference for and satisfaction with the environment (Kaplan and Kaplan, 1989; Roe and Aspinall, 2011). Environmental affordances as indicators of neighbourhood satisfaction can be explored through preference research using photo-based approaches such as ‘participant-generated photo-grouping’. The limited number of studies in this area demonstrate people’s preference for greener environments with more flexible physical features –

Residential neighbourhoods, nearby nature and quality of life  277 such as movable seats or open spaces – that facilitate diversified environmental affordances. Furthermore, studies show strong preferences for environmental affordances that provide socialising opportunities in nearby green spaces (Hadavi et al., 2015). Neighbourhood Satisfaction and Use of Outdoor Spaces Urban neighbourhoods are an appropriate scale for investigating environment–well-being associations as they offer amenities, environmental affordances and opportunities for meeting residents’ wide range of needs. The extent to which a neighbourhood meets such needs indicates the level of satisfaction of the users. Satisfaction with a neighbourhood can play a significant role in residents’ decisions to use and spend time in outdoor settings (Kaplan and Austin, 2004), which may impact their long-term life satisfaction or QOL (see Ludwig et al., 2012; Sirgy and Cornwell, 2002). Given the significance of neighbourhood satisfaction and use of outdoor spaces, it is necessary to better understand their specifications and the way they are measured. There are two major issues regarding current approaches to conceptualisation and measurement of these two constructs: (1) many studies treat each of these as unidimensional constructs, often measured with single items (such as overall satisfaction with neighbourhood), when in fact each is multidimensional (that is, has multiple sub-domains); and (2) research often treats these two constructs separately (such as Ellis et al., 2006) or without considering their reciprocal relationships (Kearney, 2006). Lack of attention to these two points keeps us from understanding what neighbourhood features and attributes contribute to neighbourhood satisfaction, what kind of environmental affordances are positively perceived by users and how those characteristics and uses are associated with satisfaction. Given the complexity of people–environment relationships, we suggest that it is essential to include wider ranges of both neighbourhood satisfaction dimensions and neighbourhood use patterns, and to account for their interrelations in environment–well-being studies. Multidimensionality Neighbourhood satisfaction Studies have investigated various factors that contribute to neighbourhood satisfaction (for example, Cook, 1988; Hur and Nasar, 2014; Kaplan, 2001; Kweon et al., 2010; Lee et al., 2008; Sirgy and Cornwell, 2002). All have offered valuable findings, but most have considered neighbourhood satisfaction as a single entity, such as overall satisfaction. Caring about preferences for environmental attributes that are associated with satisfaction triggers several questions that deal with more nuanced aspects of satisfaction: ● What exactly do we mean by neighbourhood satisfaction? ● Do we mean satisfaction with the appearance of the neighbourhood or with the amount of green features in the neighbourhood? ● Do we mean satisfaction with quality of public spaces, level of safety, amenities or affordances available? Considering the multidimensionality of neighbourhood satisfaction helps address such questions and provides a better understanding of how environment, preference and satisfaction are

278  Handbook of quality of life research associated. This might help planners and designers to create more liveable neighbourhoods by properly changing environmental attributes and affordances (Hadavi and Kaplan, 2016). Neighbourhood use Research evidence demonstrates that use of green outdoor spaces has positive impacts on the well-being of urban residents (see Ellis et al., 2006; Ward Thompson and Aspinall, 2011; Wood et al., 2017). Like neighbourhood satisfaction, use is usually examined as a single variable that is defined differently across several studies. For example, it has been investigated in terms of physical activity (Björk et al., 2008), walking in the neighbourhood (Ball et al., 2001; Humpel et al., 2004), social activity (Hur and Morrow-Jones, 2008) and the use of parks (Tinsley et al., 2002). Given the diversity and complexity of environmental attributes and affordances that encourage use and affect frequency and types of use, it is plausible that a multidimensional approach to measuring use patterns in residential neighbourhoods will help with a more thorough understanding of the associations between use, satisfaction and the physical attributes of outdoor spaces. Bi-directional Associations Despite the important association between neighbourhood satisfaction and use of outdoor spaces, few studies have specifically examined this association, mostly considering use as a factor that affects satisfaction (Kaplan, 2001; Kearney, 2006). This one-way perspective towards satisfaction and use asserts that the more one uses outdoor settings then the higher the level of satisfaction one will have with the neighbourhood. However, when approaching this topic through the lens of preference and environmental affordances, it is worthwhile considering the possibility of a reverse direction as well. If people are happy with an environment because it has the potential to meet their needs (Kaplan and Kaplan, 1982), it is possible that their higher level of satisfaction with it may result in their higher likelihood of use of that environment for purposes that fulfil their needs. Recognising such two-way relationships can help guide planners and designers in creating and changing outdoor spaces that could indirectly predict health outcomes. Sense of Well-being Life satisfaction is one of the major constructs of QOL and is referred to as the cognitive element of self-reported well-being (Sirgy, 2012). Subjective well-being has been examined through a variety of direct and indirect factors, such as: ● ● ● ● ●

a sense of happiness (MacKerron and Mourato, 2013); social connectedness (Lee and Robbins, 1995); positive and negative affect (Watson et al., 1988); stress reduction (Thompson et al., 2012); and effective functioning (Berman et al., 2008).

While the associations between these well-being measures and the built environment have been increasingly investigated in recent years, there has been relatively little research into effective functioning as an aspect of well-being.

Residential neighbourhoods, nearby nature and quality of life  279 Urban dwellers are constantly exposed to a wide range of distractions and stressful conditions, making them use plenty of their mental resources to be able to suppress distractions and to function effectively. Given the limited attentional resources for individuals, urban conditions can cause resource depletion and mental fatigue in the long run. Some of the consequences of mental fatigue in urban areas include sense of incompetence, human error, helplessness, impatience, irritability and damage to social life (Kaplan et al., 1998). To keep mental well-being at an acceptable level, people need to find ways to restore their sense of peacefulness and attentional capacity. Attention restoration theory suggests that there is a cognitive mechanism that helps with recovery from mental fatigue, and one of the best restorative environments to help with this process is found to be nature settings (Berman et al., 2008; Kaplan, 1995). Contrary to nature settings, the built elements of urban spaces (see Berman et al., 2008; Peschardt and Stigsdotter, 2013) and lack of perceived accessibility to nature in neighbourhoods (see Hur and Nasar, 2014; Kaplan, 1985; Ward Thompson and Aspinall, 2011) have been found to reduce mental well-being among urban residents. Effective functioning and the sense of peacefulness that comes with it are significant indicators of well-being in urban settings. Therefore, when considering urban outdoor settings as the source of satisfaction and perceived QOL, both factors as measures of well-being need to be considered.

NEARBY NATURE, NEIGHBOURHOOD SATISFACTION, USE AND WELL-BEING: A COMPLEX FRAMEWORK The associations between different combinations of two or three of the concepts discussed above have been examined separately in many studies. For example, there is growing evidence that availability of nearby nature has a positive effect on neighbourhood satisfaction (Crow et al., 2006; De Jong et al., 2012; Kaplan and Austin, 2004; Ward Thompson and Aspinall, 2011). Some other studies have linked nearby nature with use of outdoor spaces (Shackleton and Blair, 2013; Sugiyama et al., 2009; Wendel et al., 2012). Further, the use of nearby nature has been found to improve neighbourhood satisfaction and sense of well-being in urban settings (Ellis et al., 2006; Kaplan and Kaplan, 1989; Ward Thompson and Aspinall, 2011). Neighbourhood satisfaction has also been shown to be associated with multiple QOL domains including well-being and life satisfaction (for example, Kweon et al., 2010; Toma et al., 2015; Vemuri et al., 2011). While these factors have been mostly investigated separately, as discussed previously, few studies have examined all of them and their multiple dimensions together as an interrelated set of factors. Considering proximity to nature, different dimensions of satisfaction, forms of use and well-being in a single study can provide a more comprehensive understanding of the mechanisms through which the built environment affects well-being. Acknowledging such associations offers an analytical framework (see Figure 18.1) that can provide better insight into the bigger picture of the interrelated processes that ultimately contribute to well-being and QOL. Why does considering such a detailed and comprehensive framework matter? In short, because of its practicality. There is growing evidence confirming the important impacts of the physical design of urban environments on the well-being of residents and their behavioural patterns in outdoor settings (for example, Matsuoka and Kaplan, 2008). This highlights the key role that planners and designers can play in creating spaces that can contribute to urban

280  Handbook of quality of life research residents’ well-being and life satisfaction. However, in practice, few planners and designers have paid enough attention to the health problems arising from cityscapes that fail to support residents’ needs and preferences or improve their well-being. This could be partly because of very limited evidence-based planning and design guidelines to help professionals implement the valuable findings from environmental psychology and related fields.

Source: The author.

Figure 18.1

The interrelated factors contributing to quality of life

To find practical health-supportive solutions to the planning and design of public outdoor spaces, a detailed examination of the physical attributes of the environment and the mechanisms through which each of them affects well-being remains essential. What follows summarises the findings of a research project that aimed to examine the effects of planning and design-related aspects of the environment on people’s mental well-being. The objective was to uncover complex relationships across multiple factors and facilitate more tangible planning and design solutions to improve urban residents’ mental well-being. It shows that putting them together and looking at the components as a whole provides a better understanding of the complex interrelationships that shape the framework in Figure 18.1.

Residential neighbourhoods, nearby nature and quality of life  281 The Study Sample The study was conducted in a moderately dense area in Chicago in the US to examine the interrelationships among: ● ● ● ●

perceived attributes of the environment; multiple aspects of neighbourhood satisfaction; use patterns; and self-reported well-being.

Given the wide range of socio-economic factors that may contribute to these issues, the study site selection was based on a city-wide spatial analysis and geographic information systems (GIS) modelling. Site selection criteria included: ● ● ● ●

multifamily housing stock type; median income of US$25 000–75 000; diverse types and sizes of green spaces; and low to moderate crime rate.

Accordingly, portions of four community areas – including Logan Square, Avondale, Humboldt Park and West Town – covering a 1375-hectare area were selected to conduct a survey. A sample of 434 residents participated in the study through a randomized mail survey or onsite convenience sampling from July to October 2013. The demographic data collected showed that: ● 60 per cent of respondents were female and 40 per cent male; ● more than 90 per cent were aged between 20 and 59 years; ● more than 50 per cent were married or lived with a partner, while more than 70 per cent had no children living with them; and ● around 80 per cent of respondents had college or postgraduate degrees. Measures The survey questionnaire designed for this multifaceted study included several sections with five-point rating scales to cover the topics discussed above. Table 18.1 shows six categories of latent variables drawn from the survey through exploratory factor analyses including: ● ● ● ● ● ●

perceived attributes of nearby outdoor spaces; landscape structure; barriers to neighbourhood use; neighbourhood satisfaction; use patterns; and well-being.

To measure well-being as defined by sense of peacefulness and effective functioning, 31 survey items drawn from three different scales were included in the survey (Hadavi, 2017), including: ● the Attentional Functioning Index (AFI, 13 items; Cimprich et al., 2011); ● the Positive and Negative Affect Scale (PANAS, 15 items; Watson et al., 1988); and ● the Social Connectedness Scale (SCS, 3 items; Lee and Robbins, 1995).

282  Handbook of quality of life research Table 18.1

Categories of latent variables defined through exploratory factor analysis

Variable Category

Latent Variable (number of survey items)

Cronbach’s α

Perceived attributes of nearby outdoor spaces Proximity to green social spaces (6)

0.91

Amount of nearby green features (5)

0.80

Available active recreation spaces (4)

0.84

Nearby open lawn with trees (5)

0.87

Nearby building-dominated space (4)

0.74

Transportation barriers (3)

0.81

Non-transportation barriers (7)

0.85

Satisfaction with amount of affordances (6)

0.84

Satisfaction with amount of green features (4)

0.77

Satisfaction with quality of public spaces (4)

0.88

Neighbourhood comfort (safety and peacefulness) (4)

0.76

Green social space use (10)

0.91

Active use (4)

0.76

Walk to non-nature destinations (5)

0.76

Sense of peacefulness and effective functioning (31)

0.94

Perceived landscape structure Perceived barriers to neighbourhood use Neighbourhood satisfaction

Use patterns

Well-being

Source: The author.

For the Chicago study, ‘nearby’ was defined as a five-minute walk from home. Perceived attributes of nearby outdoor spaces were determined through questions about the likelihood of seeing different types of green/non-green features and spaces, both in view from windows and within a five-minute walk from home. In addition, 20 photographs representing a variety of available landscape structures were included in the survey, and the participants were asked about the similarity of the pictured content to what they saw nearby their home. Perceived accessibility of public outdoor spaces was addressed through questions about what discouraged the participants from pursuing activities in their neighbourhood (that is, barriers to use of neighbourhood outdoor spaces). Data Analyses and Findings Exploratory factor analyses (EFA) and factor means determined the latent variables shown in Table 18.1. Standard linear regression modelling was used to examine the associations between independent and dependent variables. To assess the two-way associations between components of neighbourhood satisfaction and forms of use, two separate generalised linear models were examined. In each, barriers to neighbourhood use were included as interacting dichotomous variables (high/low barrier) to explore potential interaction effects of the perceived barriers (Hadavi and Kaplan, 2016). To explore the potential mediating role of neighbourhood satisfaction and use in the relationship between perceived attributes of the environment and well-being, three separate linear regression analyses were conducted, followed by a mediation analysis using Preacher and Hayes’s (2008) indirect mediation macro (Hadavi, 2017). In what follows, the findings are presented on: ● the associations between neighbourhood satisfaction, use patterns and barriers to neighbourhood use; ● the associations between perceived nearby nature, neighbourhood satisfaction and use; and

Residential neighbourhoods, nearby nature and quality of life  283 ● the mediating role of neighbourhood satisfaction and use patterns in the association between perceived nearby nature and well-being. Neighbourhood Satisfaction, Use and Barriers to Use: Two-way Associations As shown in Figure 18.2, among the four examined aspects of neighbourhood satisfaction and the three forms of use, it was satisfaction with quality of public space and use of green social spaces that were found to have a two-way association. That means higher satisfaction with quality of public space may encourage more frequent use of public space and that higher frequency of use may predict higher level of satisfaction with quality of the space.

Source: After Hadavi and Kaplan (2016).

Figure 18.2

The associations between aspects of neighbourhood satisfaction, forms of use and perceived barriers to use

It was found that: ● the more satisfied people are with the quality of public space, the more likely they are to: ● use green social spaces; ● be actively engaged with the environment; and ● walk to non-nature destinations; and ● among the three forms of use, it was use of green social spaces that predicted all aspects of satisfaction, meaning that the more frequently people use nearby green social spaces, the more satisfied they would be with:

284  Handbook of quality of life research ● ● ● ●

the quality of public space; the amount of green features; the amount of affordances; and neighbourhood comfort.

The results of the analysis also showed that perceived barriers to neighbourhood use can have a negative interaction effect with satisfaction to predict some forms of use. That means for some aspects of neighbourhood satisfaction and use, the effect of satisfaction on use is different when perceived barriers are high versus low. For example, when transportation barriers are perceived to be high, satisfaction with the amount of green features has a weaker impact on frequency of walking in the neighbourhood as compared to the condition in which perceived transportation barriers are low. Also, the effect of perceived barriers as a predictor of use can be different depending on high or low satisfaction. For example, when satisfaction with the amount of green features is high, the negative impact of perceived transportation barriers on frequency of walking in the neighbourhood is much less as compared to the condition in which we have low satisfaction. Figure 18.3 shows the complexity of the associations among certain aspects of satisfaction and use when accounting for the negative interaction effects of perceived barriers to use. It shows that: (1) transportation barriers only interact with satisfaction with the amount of green features to predict the frequency of walking to non-nature destinations; and (2) by contrast, non-transportation barriers (for example, lack of maintenance and pleasant public spaces or walkable sidewalks, lack of sense of community and safety) show more complex and broader impact by interacting with satisfaction with the amount of green features, walking to non-nature destinations and use of green social spaces to ultimately predict neighbourhood comfort. Perceived Nearby Nature, Neighbourhood Satisfaction and Use After examining the links between neighbourhood satisfaction and use, the associations between those factors and perceived attributes of the environment were explored (Hadavi et al., 2018). Given that perceived barriers to neighbourhood use were found to be linked to neighbourhood satisfaction and use of outdoor spaces they were included in the regression analyses. The results showed that both perceived nearby nature and barriers to use were significantly associated with all components of satisfaction and use. As shown in Table 18.2: (1) satisfaction with quality of public space, satisfaction with the amount of affordances and the frequency of use of green social spaces were strongly linked to perceived nearby nature and barriers to use; and (2) when people feel that they have more green social spaces within walking distance from their home, then they are more satisfied with the quality of public space and the variety of things they can do in their neighbourhood, have a better sense of safety and peacefulness, and generally they walk more in their neighbourhood. Those results confirm previous findings on the positive impacts of availability of nearby nature on sense of peacefulness (Kaplan and Austin, 2004) and walking frequency (Hogendorf et al., 2020; Kondo et al., 2018; Shackleton and Blair, 2013), as well as the positive effect of sense of safety on use of green settings (Ward Thompson and Aspinall, 2011).

Residential neighbourhoods, nearby nature and quality of life  285

Source: After Hadavi and Kaplan (2016).

Figure 18.3

The interaction effects of perceived barriers to neighbourhood use in the association between neighbourhood satisfaction and use patterns

Two more notable findings were: (1) when people feel there are transportation barriers to the use of outdoor spaces, their satisfaction with quality of public space decreases; and (2) the availability of active recreation was found not to be associated with neighbourhood satisfaction, which, again, highlights the significant role of green elements in satisfaction with outdoor settings. Nearby Nature and Well-being: The Mediating Roles of Satisfaction and Use After identifying the relationships between perceived nearby nature, neighbourhood satisfaction and forms of use, next added to the equation was the major outcome variable of interest, namely, well-being. Building on previous research findings, it was hypothesised that neighbourhood satisfaction and use patterns play a mediating role between perceived attributes of the environment and well-being. Figure 18.4 summarises the steps used to examine mediating effects: (1) first, three regression analyses were conducted (shown as path A, path B and path C in Figure 18.4) to assure the dependence of these components; and (2) then mediation analysis (A–B path and C′ path) was conducted to determine whether any percentage of the effect of an environmental attribute on well-being was indirect and operating through satisfaction and/or use. It was found that perceived attributes of the environment had a stronger link to satisfaction than to use of green social spaces. Results of the mediation analysis are shown in Table 18.3. What follows describes them regarding the four examined environmental attributes, including: nearby green social spaces; nearby open lawn with trees; nearby building-dominated spaces; and perceived barriers to use. While some of the results supported the hypothesis, there were also some unexpected findings. The mediation analysis results indicated the following:

Beta

F 49.97

0.36

R2**

ns

–0.23

ns

ns*

 

0.001

 

0.001

 

 

0.001

p

F 14.13

0.13

R2

ns

–0.14

ns

0.31

ns

Beta

 

0.001

 

0.010

 

0.001

 

p

Note: * Statistically non-significant; ** adjusted R2. Source: Redrawn from Hadavi et al. (2018).

 

Model summary

barriers

Transportation

barriers

Non-transportation

recreation

Nearby active

green features

Amount of nearby

space

Nearby green/social 0.44

Variables

F 84.64

0.49

R2

–0.10

–0.19

ns

0.18

0.48

Beta

 

0.001

0.013

0.001

 

0.001

0.001

p

F 33.56

0.27

R2

ns

–0.37

–0.14

0.16

0.25

Beta

comfort

 

0.001

 

0.001

0.005

0.001

0.001

p

F 48.08

0.35

R2

ns

ns

ns

0.14

0.47

Beta

 

0.001

 

 

 

0.005

0.001

p

F 13.20

0.12

R2

ns

ns

0.33

0.11

ns

Beta

 

0.001

 

 

0.001

0.05

 

p

Green/social space use Active engagement

space

features

affordances

Neighbourhood

Use Patterns Quality of public

Amount of green

Amount of

F 9.67

0.09

R2

ns

ns

–0.16

0.13

0.28

Beta

 

0.001

 

 

0.004

0.012

0.001

p

Walk to non-nature destinations

 

Neighbourhood Satisfaction

Regression analysis summary: testing the effects of both perceived proximity and barriers on each measure of neighbourhood satisfaction and use pattern

Table 18.2

286  Handbook of quality of life research

Residential neighbourhoods, nearby nature and quality of life  287

Note: C path = total effect; C′ path = direct effect when controlling for mediators; A–B path = indirect effect of independent variable on the outcome variable through mediators. Source: After Hadavi (2017).

Figure 18.4

Mediation analysis to uncover the direct and indirect effects of the environment on well-being

1. Nearby green social spaces (such as green boulevards, parks with large trees and outdoor sitting areas, as well as nearby picnic and gathering areas) have an indirect effect on mental well-being that operates through both neighbourhood satisfaction and use. In addition to confirming the previous direct effects of use of green social spaces on well-being (Ellis et al., 2006; Hartig and Staats, 2006; Ward Thompson and Aspinall, 2011), these findings advance understanding by showing indirect impacts of such spaces on well-being through frequency of use. 2. Nearby open lawns with trees had a significant indirect effect on well-being through satisfaction with quality of public space, with no effects through use. Such findings go beyond the past general statements about the positive impact of nearby nature on well-being and offer insights about specific characteristics of the nearby nature that has positive impacts on well-being. The study shows that landscapes that look like open lawns with trees demonstrate improvements in residents’ sense of peacefulness and effectiveness through increasing sense of satisfaction with the neighbourhood, without physically using such spaces. This sends a key message to planners and decision-makers of urban outdoor settings who tend to evaluate the usefulness of a green space based on amount of physical usage of the space. Health benefits of certain types of green spaces are found to go beyond their mere physical use. 3. The results also showed that nearby building-dominated spaces had a significant negative association with well-being that was fully mediated by both satisfaction and use. This finding was not surprising, as a growing body of evidence has already highlighted their negative effects on mental restoration of users (Peschardt and Stigsdotter, 2013; Scopelliti and Giuliani, 2004). An important contribution of the study to the residential environment– well-being literature is the finding that building-dominated spaces can indirectly decrease

Use of green

0.40

β

0.22

R2 = 0.39;

R2 = 0.53;

F = 120.89;

p = 0.000

R2 = 0.03;

F = 11.59;

p = 0.000

use

Source: The author.

ns

–0.23

–0.122

Barriers to neighbourhood

–0.08

R2 = 0.01; F = 5.28; p = 0.022

p = 0.000

F = 69.10;

 

–0.066

spaces

–0.19

0.30  

Nearby building-dominated

R2 = 0.03; F = 11.98; p = 0.001

 

0.35

β

quality of public space social spaces

Satisfaction with

A Path

R2 = 0.02; F = 11.90; p = 0.001

0.077

Nearby open lawn with trees 0.082

Nearby green social spaces

β

 

Attributes

Total Effect

Perceived Environmental

0.0176

Lower

CI 95%

0.0100 −0.0507 −0.0760

R2 = 0.07; F = 11.024; p = 0.0000

–0.0334

R2 = 0.06; F = 10.063; p = 0.0000

–0.0240

R2 =0.06; F = 9.678; p = 0.0000

0.0431

R2= 0.06; F = 9.720; p = 0.0000

0.0541

β

0.0020

−0.0038

0.0781

0.0953

Upper

Satisfaction with quality of public space

A–B Indirect Effect

−0.0200

−0.0095

 

0.0295

 

0.0349

β

−0.0462

−0.0277

 

−0.0067

 

0.0037

Lower

CI 95%

Use of green social spaces

−0.0022

−0.0001

 

0.0616

 

0.0736

Upper

Mediation analyses: testing the direct and indirect associations of environmental attributes, satisfaction, use and well-being

 

Table 18.3

288  Handbook of quality of life research

Residential neighbourhoods, nearby nature and quality of life  289 a sense of peacefulness and effective functioning through their negative associations with both satisfaction with quality of public space and frequency of use of those spaces. 4. Contrary to all the three perceived environmental attributes discussed above, perceived barriers to use showed both direct and indirect effects on well-being. This confirms previous research findings that demonstrate the negative impacts of barriers on frequency of outdoor use (for example, Barton and Pretty, 2010; Hur and Nasar, 2014). Another significant contribution of the study is its demonstration of the mechanisms of such relationships and their direct and indirect negative associations with well-being. Furthermore, the findings highlight that satisfaction with quality of public spaces does not mediate the impact of perceived barriers such as safety concerns, unpleasant open spaces, low maintenance, lack of walkable sidewalks and sense of community. Those findings underscore the need for further detailed examination of perceived barriers to find ways to reduce their negative impact on residents’ environmental perceptions.

IMPLICATIONS AND CONCLUSION The attributes of urban outdoor settings and the affordances they provide support people’s needs and preferences. A large body of evidence has evolved confirming people’s preferences of nature settings to built settings (for example, Hadavi et al., 2015; Hartig and Staats, 2006; Herzog, 1989) that ultimately impact sense of well-being. This chapter suggests that the impacts of the environment on well-being are a complex and multidimensional topic. To understand the interrelationships of the involved components, a holistic and detailed approach is needed. The multidimensionality of neighbourhood satisfaction needs to be recognised along with considering multiple forms of neighbourhood use as predictors of well-being. Moreover, there is a need to examine how different attributes of the environment affect each of these components. The use of photographs as surrogates of actual environments can help understand environmental attributes and affordances that matter to people. The research reported here suggests that the lack of proximity to green spaces that offer social affordances can impose costs to the well-being of urban residents. Being exposed to building-dominated spaces that fail to support residents’ needs and preferences results in stress and discomfort that decrease both satisfaction with quality of the environment and the likelihood of use of such settings. These consequences ultimately result in diminished sense of peacefulness and effective functioning relevant to life satisfaction and perceived QOL. By contrast, access to green spaces that provide opportunities for socialising within walking distance to where people live will increase the likelihood of frequent use of outdoor settings and make residents feel happier with their neighbourhood, which can lead to a greater sense of well-being. These findings are not only helpful from a theoretical perspective, but they also offer implications for planners and designers in terms of setting priorities for change and improving outdoor spaces. The reported evidence suggests that planners and designers focus on social research designs that are detailed enough to enable planning and design of everyday landscapes based on people’s input. This can be achieved by defining more tangible design-related environmental measures that could be explored through social research in terms of their potential associations with preference, satisfaction and use. Acknowledging the indirect effects of the environment on well-being through satisfaction and use can facilitate the process of collecting

290  Handbook of quality of life research data from outdoor users. It is not always easy for planners and designers to directly ask people about their well-being. People usually feel more comfortable talking about what they do in outdoor settings and what about such settings are satisfying to them. Knowing that there is a strong association between perceived environmental attributes, neighbourhood satisfaction and use patterns will help planners and designers evaluate the current conditions and develop plans for change that can help ultimately improve users’ well-being. Furthermore, in terms of new neighbourhood developments, the research evidence discussed in this chapter highlights the need for collecting social data from nearby residents with relatively similar demographics to that of future occupants regarding perceived attributes of outdoor spaces, their level of satisfaction and use patterns in nearby green spaces. This will help experts gain a better understanding of what should be avoided in design and what may be potentially embraced by future users. It is worth replicating the study reported here in other urban settings with different socio-economic and environmental conditions to examine similarities of findings as well as context-based differences that may affect planning and policy decisions. Finally, in a broader sense, this study highlights the necessity of green outdoor spaces in residential neighbourhoods to optimise QOL. This has been shown to be particularly true during the global COVID-19 pandemic when most people had to stay at home and work from home for months. It is not surprising to see that the residents who have more walkable nearby green spaces are more likely to use neighbourhood outdoor spaces more frequently and can adapt more easily with the limitations that work-from-home orders impose on people.

REFERENCES Ball, K., Bauman, A., Leslie, E. and Owen, N. (2001), ‘Perceived environmental aesthetics and convenience and company are associated with walking for exercise among Australian adults’, Preventive Medicine, 33, 434–40. Barton, J. and Pretty, J. (2010), ‘What is the best dose of nature and green exercise for improving mental health? A multi-study analysis’, Environmental Science and Technology, 44, 3947–55. Berman, M.G., Jonides, J. and Kaplan, S. (2008), ‘The cognitive benefits of interacting with nature’, Psychological Science, 19, 1207–12. Björk, J., Albin, M. and Grahn, P. et al. (2008), ‘Recreational values of the natural environment in relation to neighbourhood satisfaction, physical activity, obesity and wellbeing’, Journal of Epidemiology & Community Health, 62, Article e2. Charlesworth, W.R. (1995), ‘An evolutionary approach to cognition and learning’, in C.A. Nelson (ed.), Basic and Applied Perspectives on Learning, Cognition, and Development: The Minnesota Symposia on Child Psychology, Volume 28, Mahwah, NJ: Lawrence Erlbaum Associates, pp. 175–218. Cimprich, B., Visovatti, M. and Ronis, D.L. (2011), ‘The Attentional Function Index – a self-report cognitive measure’, Psycho-oncology, 20, 194–202. Clark, C. and Uzzell, D.L. (2006), ‘The socio-environmental affordances of adolescents’ environments’, in C. Spencer and M. Blades (eds), Children and Their Environments: Learning, Using and Designing Spaces, Cambridge, UK: Cambridge University Press, pp. 176–96. Coeterier, J.F. (1983), ‘A photo validity test’, Journal of Environmental Psychology, 3, 315–23. Cook, C.C. (1988), ‘Components of neighbourhood satisfaction: responses from urban and suburban single-parent women’, Environment and Behavior, 20, 115–49. Crow, T., Brown, T. and De Young, R. (2006), ‘The Riverside and Berwyn experience: contrasts in landscape structure, perceptions of the urban landscape, and their effects on people’, Landscape and Urban Planning, 75, 282–99.

Residential neighbourhoods, nearby nature and quality of life  291 De Jong, K., Albin, M. and Skärbäck, E. et al. (2012), ‘Perceived green qualities were associated with neighbourhood satisfaction, physical activity, and general health: results from a cross-sectional study in suburban and rural Scania, southern Sweden’, Health & Place, 18, 1374–80. Ellis, C.D., Lee, S.W. and Kweon, B.S. (2006), ‘Retail land use, neighbourhood satisfaction and the urban forest: an investigation into the moderating and mediating effects of trees and shrubs’, Landscape and Urban Planning, 74, 70–78. Giles-Corti, B., Broomhall, M.H. and Knuiman, M. et al. (2005), ‘Increasing walking: how important is distance to, attractiveness, and size of public open space?’, American Journal of Preventive Medicine, 28, 169–76. Hadavi, S. (2017), ‘Direct and indirect effects of the physical aspects of the environment on mental well-being’, Environment and Behavior, 49, 1071–104. Hadavi, S. and Kaplan, R. (2016), ‘Neighbourhood satisfaction and use patterns in urban public outdoor spaces: multidimensionality and two-way relationships’, Urban Forestry and Urban Greening, 19, 110–22. Hadavi, S., Kaplan, R. and Hunter, M.C.R. (2015), ‘Environmental affordances: a practical approach for design of nearby outdoor settings in urban residential areas’, Landscape and Urban Planning, 134, 9–32. Hadavi, S., Kaplan, R. and Hunter, M.R. (2018), ‘How does perception of nearby nature affect multiple aspects of neighbourhood satisfaction and use patterns?’, Landscape Research, 43, 360–79. Hadavi, S. and Sullivan, W.C. (2018), ‘Environmental aesthetics’, in D.R. Montello (ed.), Handbook of Behavioral and Cognitive Geography, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 307–21. Han, K.T. (2010), ‘An exploration of relationships among the responses to natural scenes: scenic beauty, preference, and restoration’, Environment and Behavior, 42, 243–70. Hartig, T. and Staats, H. (2006), ‘The need for psychological restoration as a determinant of environmental preferences’, Journal of Environmental Psychology, 26, 215–26. Hartig, T., Van den Berg, A.E. and Hagerhall, C.M. et al. (2011), ‘Health benefits of nature experience: psychological, social and cultural processes’, in K. Nilsson, M. Sangster and C. Gallis et al. (eds), Forests, Trees and Human Health, Dordrecht: Springer, pp. 127–68. Herzog, T.R. (1989), ‘A cognitive analysis of preference for urban nature’, Journal of Environmental Psychology, 9, 27–43. Hogendorf, M., Groeniger, J.O. and Noordzij, J.M. et al. (2020), ‘Longitudinal effects of urban green space on walking and cycling: a fixed effects analysis’, Health & Place, 61, Article 102264. Humpel, N., Owen, N. and Iverson, D. et al. (2004), ‘Perceived environment attributes, residential location, and walking for particular purposes’, American Journal of Preventive Medicine, 26, 119–25. Hur, M. and Morrow-Jones, H. (2008), ‘Factors that influence residents’ satisfaction with neighbourhoods’, Environment and Behavior, 40, 619–35. Hur, M. and Nasar, J.L. (2014), ‘Physical upkeep, perceived upkeep, fear of crime and neighbourhood satisfaction’, Journal of Environmental Psychology, 38, 186–94. Jiang, B., Larsen, L. Deal, B. and Sullivan, W.C. (2015), ‘A dose–response curve describing the relationship between tree cover density and landscape preference’, Landscape and Urban Planning, 139, 16–25. Kaplan, R. (1985), ‘The analysis of perception via preference: a strategy for studying how the environment is experienced’, Landscape Planning, 12, 161–76. Kaplan, R. (2001), ‘The nature of the view from home – psychological benefits’, Environment and Behavior, 33, 507–42. Kaplan, R. and Austin, M.E. (2004), ‘Out in the country: sprawl and the quest for nature nearby’, Landscape and Urban Planning, 69, 235–43. Kaplan, R. and Kaplan, S. (1989), The Experience of Nature: A Psychological Perspective, Cambridge, UK: Cambridge University Press. Kaplan, R., Kaplan, S. and Ryan, R. (1998), With People in Mind: Design and Management of Everyday Nature, Washington, DC: Island Press. Kaplan, S. (1983), ‘A model of person–environment compatibility’, Environment and Behavior, 15, 311–32.

292  Handbook of quality of life research Kaplan, S. (1995), ‘The restorative benefits of nature: toward an integrative framework’, Journal of Environmental Psychology, 15, 169–82. Kaplan, S. and Kaplan, R. (1982), Cognition and Environment: Functioning in an Uncertain World, New York: Praeger. Kaplan, S. and Kaplan, R. (2003), ‘Health, supportive environments, and the reasonable person model’, American Journal of Public Health, 93, 1484–9. Kearney, A.R. (2006), ‘Residential development patterns and neighbourhood satisfaction: impacts of density and nearby nature’, Environment and Behavior, 38, 112–39. Kondo, M.C., Fluehr, J.M., McKeon, T. and Branas, C.C. (2018), ‘Urban green space and its impact on human health’, International Journal of Environmental Research and Public Health, 15, Article 445. Kweon, B.S., Ellis, C.D., Leiva, P.I. and Rogers, G.O. (2010), ‘Landscape components, land use, and neighbourhood satisfaction’, Environment and Planning B: Planning and Design, 37, 500–517. Lee, R.M. and Robbins, S.B. (1995), ‘Measuring belongingness: the social connectedness and the social assurance scales’, Journal of Counseling Psychology, 42, 232–41. Lee, S.W., Ellis, C.D., Kweon, B.S. and Hong, S.K. (2008), ‘Relationship between landscape structure and neighbourhood satisfaction in urbanized areas’, Landscape and Urban Planning, 85, 60–70. Ludwig, J., Duncan, G.J. and Gennetian, L.A. et al. (2012), ‘Neighbourhood effects on the long-term well-being of low-income adults’, Science, 337, 1505–10. MacKerron, G. and Mourato, S. (2013), ‘Happiness is greater in natural environments’, Global Environmental Change, 23, 992–1000. Matsuoka, R.H. and Kaplan, R. (2008), ‘People needs in the urban landscape: analysis of landscape and urban planning contributions’, Landscape and Urban Planning, 84, 7–19. Parkes, A., Kearns, A. and Atkinson, R. (2002), ‘What makes people dissatisfied with their neighbourhoods?’, Urban Studies, 39, 2413–38. Peschardt, K.K. and Stigsdotter, U.K. (2013), ‘Associations between park characteristics and perceived restorativeness of small public urban green spaces’, Landscape and Urban Planning, 112, 26–39. Preacher, K.J. and Hayes, A.F. (2008), ‘Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models’, Behavior Research Methods, 40, 879–91. Roe, J. and Aspinall, P. (2011), ‘The emotional affordances of forest settings: an investigation in boys with extreme behavioural problems’, Landscape Research, 36, 535–52. Scopelliti, M. and Giuliani, M.V. (2004), ‘Choosing restorative environments across the lifespan: a matter of place experience’, Journal of Environmental Psychology, 24, 423–37. Shackleton, C.M. and Blair, A. (2013), ‘Perceptions and use of public green space is influenced by its relative abundance in two small towns in South Africa’, Landscape and Urban Planning, 113, 104–12. Sirgy, M.J. (2012), The Psychology of Quality of Life: Hedonic Well-Being, Life Satisfaction, and Eudaimonia, Dordrecht: Springer. Sirgy, M.J. and Cornwell, T. (2002), ‘How neighbourhood features affect quality of life’, Social Indicators Research, 59, 79–114. Sugiyama, T., Thompson, C.W. and Alves, S. (2009), ‘Associations between neighbourhood open space attributes and quality of life for older people in Britain’, Environment and Behavior, 41, 3–21. Sullivan, W. (2005), ‘Forest, savanna, city: evolutionary landscapes and human functioning’, in P.E. Bartlett (ed.), Urban Place: Reconnecting With the Natural World, Cambridge, MA: MIT Press, pp. 237–52. Suppakittpaisarn, P., Jiang, B., Slavenas, M. and Sullivan, W.C. (2019), ‘Does density of green infrastructure predict preference?’, Urban Forestry and Urban Greening, 40, 236–44. Tinsley, H.E., Tinsley, D.J. and Croskeys, C.E. (2002), ‘Park usage, social milieu, and psychosocial benefits of park use reported by older urban park users from four ethnic group’, Leisure Sciences, 24, 199–218. Thompson, C.W. (2011), ‘Linking landscape and health: the recurring theme’, Landscape and Urban Planning, 99, 187–95. Thompson, C.W., Roe, J. and Aspinall, P. et al. (2012), ‘More green space is linked to less stress in deprived communities: evidence from salivary cortisol patterns’, Landscape and Urban Planning, 105, 221–9. Toma, A., Hamer, M. and Shankar, A. (2015), ‘Associations between neighbourhood perceptions and mental well-being among older adults’, Health & Place, 34, 46–53.

Residential neighbourhoods, nearby nature and quality of life  293 Van den Berg, A.E., Hartig, T. and Staats, H. (2007), ‘Preference for nature in urbanized societies: stress, restoration, and the pursuit of sustainability’, Journal of Social Issues, 63, 79–96. Vemuri, A.W., Morgan Grove, J., Wilson, M.A. and Burch Jr, W.R. (2011), ‘A tale of two scales: evaluating the relationship among life satisfaction, social capital, income, and the natural environment at individual and neighbourhood levels in metropolitan Baltimore’, Environment and Behavior, 43, 3–25. Ward Thompson, C. and Aspinall, P.A. (2011), ‘Natural environments and their impact on activity, health, and quality of life’, Applied Psychology: Health and Well-Being, 3, 230–60. Watson, D., Clark, L.A. and Tellegen, A. (1988), ‘Development and validation of brief measures of positive and negative affect: the PANAS scales’, Journal of Personality and Social Psychology, 54, 1063–70. Wendel, H.E.W., Zarger, R.K. and Mihelcic, J.R. (2012), ‘Accessibility and usability: green space preferences, perceptions, and barriers in a rapidly urbanizing city in Latin America’, Landscape and Urban Planning, 107, 272–82. Wood, L., Hooper, P., Foster, S. and Bull, F. (2017), ‘Public green spaces and positive mental health – investigating the relationship between access, quantity and types of parks and mental well-being’, Health & Place, 48, 63–71.

19. Effects of environmental degradation on neighbourhood satisfaction and quality of life Byoung-Suk Kweon and Christopher D. Ellis

INTRODUCTION Nearly half the global population is affected by environmental degradation, which is responsible for significant declines in biodiversity, wildlife habitat and human health. Research on the effects of air pollution on human health has found significant negative relationships with well-being, life satisfaction and overall quality of life (QOL). There is a vast literature on how the environment in which people live affects people’s QOL. From restoration from mental fatigue (Kaplan, 1995), to social integration (Kweon et al., 1998), to physical health (Gwangndi et al., 2016; Majeed and Ozturk, 2016; Martin, 2003; Rahman, 2021; Ulrich, 1984), environments play a significant role in human physical, social and mental functioning. Much of the research focuses on the positive benefits of healthy environments and how elements such as trees or parks are shown to improve various measures of QOL, with relatively less focus on negative factors such as environmental degradation or pollution and its effect on QOL. This chapter looks at how environmental degradation such as the losses of natural areas and wildlife habitat and discharges of pollution into water, land and air are related to neighbourhood satisfaction and QOL. It first provides an overview of some of the research findings, including recent reports outlining the serious decline in environmental conditions worldwide and its effects on humans, wildlife habitat and biodiversity. Following that is an analysis of neighbourhood satisfaction and subjective QOL in relation to concerns about environmental degradation based on data drawn from a survey of residents in the metro-Detroit area in Michigan, US. The chapter concludes with a discussion of the implications of the findings for neighbourhood satisfaction and QOL and proposes areas for future research.

ENVIRONMENTAL DEGRADATION: WHAT IS IT? Environmental degradation is defined as any change or disturbance to the environment perceived to be deleterious or undesirable (Johnson et al., 1997). This can include: air, soil and water pollution; the disturbance or elimination of habitat; declines in biodiversity; significant changes in climate leading to extreme weather events; and reductions of agricultural production. The UN Millennium Ecosystem Assessment Board’s (2005, p.1) report states that: ‘humans have changed ecosystems more rapidly and extensively over the past 50 years than in any comparable period of time in human history’. The report points out that while these changes may lead to economic gains, they also result in degradation to fresh water, land and air, and biodiversity. 294

Environmental degradation on neighbourhood satisfaction and quality of life  295 The negative impacts can have measurable effects on human well-being. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2018) estimated that 3.2 billion people worldwide are negatively affected by land degradation. About 25 per cent of taxonomic species in the US are at risk of extinction due to environmental degradation and can lead to reduced water, energy and food security, with deleterious impacts on livelihoods and economic performance (ibid.). The loss of species and habitat due to environmental degradation is an immediate and growing concern worldwide (ibid.). Ferrer-i-Carbonell and Gowdy (2007) found that concerns about species extinction were related to well-being. Rangel (2003) has demonstrated through behavioural economic modelling that people are willing to support economic policies that preserve the environment for future generations when they themselves are socially and economically secure. Air pollution especially has been shown to negatively affect QOL. Numerous factors are considered when deciding whether a substance is viewed as an air pollutant (Vallero, 2014). The US Clean Air Act of 1970 identified six criteria of air pollutants that are commonly regulated for public health purposes: particulate matter (PM), ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2), nitrogen dioxide (NO2) and lead (Pb). Luechinger (2009) found that living downwind from a power plant (SO2) can have a negative effect on life satisfaction. Similarly, Orru and colleagues (2016) showed that both PM10 and SO2 concentrations were negatively related to life satisfaction. In another study, Darçın (2014) found that PM10 was negatively related to QOL as defined by health data published by WHO statistics. In still another study, Welsch (2006) found that air pollution (NO2 and lead-rich dust) is a significant predictor of well-being, and reductions in air pollution over time can be translated into increased monetary value in ten Western European countries. Welsch (2006) estimated the value of air pollution improvements at about US$760 per capita for NO2, and US$1390 for lead for the ten countries. The increased value might be due to the increased property values as well as health effects. Finally, Ferrer-i-Carbonell and Gowdy (2007) found that concerns about ozone pollution were negatively related to well-being.

INVESTIGATING ENVIRONMENTAL DEGRADATION, NEIGHBOURHOOD SATISFACTION AND QUALITY OF LIFE People spend a considerable amount of their daily lives at home, and thus their satisfaction with the physical and social characteristics of their neighbourhood helps to determine their well-being (Ciorici and Dantzler, 2019). During the COVID-19 pandemic, large portions of the population were likely to spend even more time at home. Researchers who study neighbourhood or residential satisfaction and QOL have looked at numerous environmental indicators, including: air, water, noise and waste (Marans and Mohai, 1991); physical conditions and maintenance (Ciorici and Dantzler, 2019; Ellis et al., 2006; Lansing and Marans, 1969); nature, parks and greenery (Ellis et al., 2006; Hur et al., 2010, Lee et al., 2008); and safety and crime (Cook, 1988). Understanding the relationships between neighbourhood or residential satisfaction and loss of natural areas and wildlife habitat has been a growing body of research on QOL conducted in the area around residents’ homes. In this chapter, people’s perceptions of environmental degradation – such as the loss of natural areas and wildlife habitat, as well as concerns for air pollution – are analysed as they relate to self-reported QOL and neighbourhood satisfaction.

296  Handbook of quality of life research Methodology A survey Data was collected from a survey of more than 4000 households in the Detroit metropolitan area consisting of seven counties including the city of Detroit (the centre of the automobile industry in the US). The survey was conducted as part of the 2001 Detroit Area Study undertaken at the University of Michigan, which focused on the quality of community life (Kweon and Marans, 2011; Marans and Kweon, 2011). It consisted of two parts: (1) a mail questionnaire sent to a sample of households throughout the seven counties of Livingston, Macomb, Monroe, Oakland, St. Clair, Washtenaw, and Wayne including the City of Detroit (n = 4077); and (2) face-to-face interviews with residents in a sample of households drawn from the three largest counties: Wayne including Detroit, Oakland and Macomb (n = 315). Key topics in the study included transportation, public services, community and community involvement, housing, parks and recreation, and environmental degradation. The overall response rate was 59.8 per cent. Perception of environmental degradation To find out how much of a problem environmental degradation causes in nearby living environments, the survey participants were asked to indicate how much of a problem each of the following environmental issues are in their county: ● ● ● ●

loss of natural areas; loss of natural places for fish and wildlife to live; discharge of waste into lakes, rivers and streams; and air pollution.

Response categories for the four conditions were: ● ● ● ●

not a problem at all; not a serious problem; a somewhat serious problem; and a very serious problem.

In addition to examining the relationships between each environmental issue and both neighbourhood satisfaction and subjective QOL, an environmental degradation index consisting of responses to the four questions was created and examined. Subjective quality of life QOL was measured using people’s satisfaction responses to the following eight items: ● ● ● ● ● ● ● ●

the amount of time you have to do the things you want to do; your family life; your health; your friends; your job; your overall standard of living; the way you spend your leisure time; and your life as a whole.

Environmental degradation on neighbourhood satisfaction and quality of life  297 A seven-point scale was used where: 1 = completely dissatisfied and 7 = completely satisfied. A QOL index for each respondent was created using the average score of the eight items. Neighbourhood satisfaction One item was used to measure neighbourhood satisfaction with survey respondents being asked: ‘How satisfied are you with your neighbourhood?’ Participants responded using the same seven-point scale as used in the QOL questions. Respondent characteristics In addition to the satisfaction questions, respondent demographic characteristics were also measured. These included: ● ● ● ● ● ● ● ●

gender; educational attainment; work status; annual household income; number of children under the age of 18; housing status (homeowner versus renter); length of time at residence; and place of residence (urban, suburban or rural).

The number of relatives and friends in the neighbourhood was also measured. These respondent characteristics and measures of neighbourhood social ties were found to be important in previous research investigating neighbourhood satisfaction and QOL (Sun, 2005) and are incorporated in this analysis. Results The results are discussed in three parts: ● first, descriptive statistics of environmental degradation, QOL, neighbourhood satisfaction as well as respondent characteristics are discussed; ● next, correlation analysis is used to explore relationships among all variables; and ● finally, results from a hierarchical regression analysis are presented showing how environmental degradation impacts the QOL and neighbourhood satisfaction after controlling for individual characteristics. Descriptive statistics The researchers first examined the descriptive statistics for the variables for which data was generated by the survey, including means, standard deviations, minimums and maximums (Table 19.1). For the four environmental degradation variables, respondents indicated that environmental degradation was somewhat of a serious problem in their counties in terms of: ● ● ● ●

loss of natural areas (mean = 2.9); loss of natural places for fish and wildlife to live (mean = 2.9); discharge of waste into lakes, rivers and streams (mean = 3.2); and air pollution (mean = 2.9).

298  Handbook of quality of life research Table 19.1

Descriptive statistics

Variables

Mean (SD)

Min.

Max.

Environmental degradation index

3.0 (0.7)

1

4

Loss of natural areas

2.9 (0.8)

1

4

Loss of natural places for fish and wildlife to live

2.9 (0.9)

1

4

Discharge of waste into lakes, rivers and streams

3.2 (0.9)

1

4

Air pollution

2.9 (0.9)

1

4

Quality of life index

5.3 (1.1)

1

7

Neighbourhood satisfaction

5.5 (1.3)

1

7

Gender:

 

 

 

Female

56%

 

 

Male

44%

 

 

Age

52.3 (15.1)

18

100

Education:

 

 

 

High school or less (0: Other; 1: High school or less)

32%

 

 

College graduate or some college (0: Other; 1: College graduate or some

48%

 

 

Graduate/Professional degree (0: Other; 1: Graduate/Professional degree)

21%

 

 

Income (1: Less than $20 000; 4: $40 000–49 999; 5: $50 000–74 000;

4.9 (2.1)

1

8

Median income

$64 649

 

 

Marital status:

 

 

 

Married or living together (Coded 1)

76%

 

 

Single, divorced or widowed (Coded 2)

25%

 

 

Children:

 

 

 

No children (Coded 1)

59%

 

 

Has children under 18 (Coded 2)

41%

 

 

Number of relatives in the neighbourhood

1.5 (0.9)

 

 

Number of friends in the neighbourhood

2.9 (1.4)

 

 

Work status:

 

 

 

Full- and part-time working (Coded 1)

63%

 

 

Students, retired, homemaker and other (Coded 2)

37%

 

 

Home ownership:

 

 

 

Owner (Coded 1)

88%

 

 

Other (Coded 2)

12%

 

 

Length of stay

15.2 (13.7)

0

87

Place of residency:

 

 

 

Urban (0: Other; 1: Urban)

20%

 

 

Suburban (0: Other; 1: Suburban)

57%

 

 

Rural (0: Other; 1: Rural)

24%

 

 

college)

10: $175 000 or more)

Source: The authors.

The discharge of waste into lakes, rivers and streams had the highest mean value, but the difference between the highest (3.2) and lowest mean (2.9) was slight. The composite environmental degradation index had a mean score of 3.0, revealing that environmental degradation was viewed throughout the region as problematic. The mean of the QOL index was 5.3, indicating that respondents were generally satisfied with their lives. Findings were similar with neighbourhood satisfaction scores, averaging 5.5. In terms of respondent characteristics, women outnumbered men in the sample (56 per cent versus 44 per cent, respectively) while the mean age was 52 years old. The mean household

Environmental degradation on neighbourhood satisfaction and quality of life  299 income was closer to $49 999. The median household income was estimated to be $64 649 by interpolating the frequency of the grouped income variable. Respondents were highly educated: 69 per cent of the respondents had at least some college education. A majority of the respondents owned their residences (88 per cent), and a majority were married or living together with a partner (76 per cent). Most reported residency in a suburban area (57 per cent) and had lived in their residence an average of 15 years. Respondents reported having an average of 1.5 friends in their neighbourhood, and they had twice as many relatives living nearby. Correlation analysis Correlation analysis was used to investigate the relationships between the variables (Table 19.2). All the correlations among the four environmental degradation variables were statistically significant. The strongest correlation was between loss of natural areas and loss of natural places for fish and wildlife (r = –0.78). These two variables had 61 per cent of shared variance compared to 39 per cent of their own unique variance. Also, air pollution and discharge of waste into lakes, rivers and streams were highly correlated (r = 0.66). All four environmental degradation variables were significantly related to the QOL index as well as to neighbourhood satisfaction. The contribution of each environmental degradation variable was examined in relation to respondents’ neighbourhood satisfaction and QOL scores. However, due to high correlations among environmental degradation variables, a composite environmental degradation index was created to evaluate its contribution to neighbourhood satisfaction and QOL. This index is significantly correlated with the QOL index (r = –0.15) as well as neighbourhood satisfaction (r = –0.16), indicating that as environmental degradation in the study region increased both neighbourhood satisfaction and QOL decreased. The highest correlation with the QOL index was respondents’ neighbourhood satisfaction (r = –0.35) followed by work status (r = 0.20) and age (r = 0.19). In other words, respondents who were satisfied with their neighbourhoods also reported higher QOL scores. At the same time, respondents who were working full or part time had a lower QOL than did students, those who were retired and homemakers. Older people tended to have a higher QOL score. Finally, respondents who were older (r = 0.19), were married or living with a partner (r = –0.13), had many friends in their neighbourhood (r = 0.18), had higher income (r = 0.13), owned their home (r = –0.18) and did not live in an urban area were more satisfied with their neighbourhoods than their counterparts. Hierarchical regression analysis I: effects of individual characteristics and environmental degradation on neighbourhood satisfaction To determine the combined effects and the most important predictors of QOL and of neighbourhood satisfaction, several models were tested using hierarchical regression analysis. First the effects of individual characteristics and environmental degradation on neighbourhood satisfaction were modelled using three models (Table 19.3). Model I: Individual Characteristics (see Table 19.3) indicates that gender, age, graduate or professional education, income, work status, home ownership, tenure length and number of

–0.01

7. –0.16** –0.10** –0.11** –0.12** –0.21**   Neighbourhood satisfaction

0.03*

–0.09** –0.10** –0.11** –0.01

8. Gender

9. Age

0.06** 0.04*

0.03

–0.00

0.02

0.00

12. Graduate/ Professional degree

13. Income

14. Married or living together

15. Children

0.02

–0.01

0.03

0.02

0.05** 0.02

0.03

–0.01

 

1

 

 

 

 

1

 

 

 

 

 

 

 

 

9

 

 

 

 

 

 

 

 

 

10

–0.02

–0.19** –0.65** 1

 

 

 

 

 

 

 

 

 

 

11

 

 

 

 

 

 

 

 

 

 

 

12

0.08** –0.07** –0.09** –0.35** –0.48** 1

–0.02

–0.05** 0.08** 0.27** 1

0.12** –0.01

 

 

 

 

8

 

 

 

 

 

 

 

 

 

 

 

 

13

 

 

 

 

 

 

 

 

 

 

 

 

 

14

–0.09** –0.02

0.00

–0.50** –0.11** 0.08** 0.03*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

15

0.21** –0.24** 1

0.05** –0.13** –0.13** 0.14** 0.14** 0.08** –0.04* –0.05** –0.44** 1

–0.07** 0.13** 0.20** –0.15** –0.26** –0.39** 0.11** 0.30** 1

–0.04* –0.00

0.01

–0.02

–0.02

–0.00

0.00

0.02

11. College 0.02 graduate/Some college

0.02

0.00

10. High school –0.04* –0.07** –0.05** –0.01 or less

0.01

 

–0.06**  

0.04*

1

6. Quality of life –0.15** –0.11** –0.15** –0.09** –0.15** 1 index

–0.04** –0.10

 

 

5. Air pollution 0.80** 0.45** 0.48** 0.66** 1

0.04*

 

 

 

 

4. Discharge 0.81** 0.46** 0.50** 1 of waste into lakes, rivers and streams

 

 

 

7

 

 

 

6

 

 

 

5

3. Loss of 0.85** 0.78** 1 natural places for fish and wildlife to live

 

4

 

 

3

 

0.82** 1

 

2. Loss of natural areas

2

1

Correlations among variables

1. 1 Environmental degradation index

 

Table 19.2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

16

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

17

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

18

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

19

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

20

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

21

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

22

300  Handbook of quality of life research

7 0.02

8

0.05** –0.01

0.01

–0.05** 0.02

23. Rural

–0.01

–0.01

0.02

–0.08** –0.12** 0.03*

0.14** –0.02

0.07** 0.05** –0.01

0.12** –0.12** –0.21** 0.03

0.05** 0.01

0.02

Note: ** p ​≤​ 0.01 level (2-tailed); * p ​≤​ 0.05 level (2-tailed). Source: The authors.

–0.01

0.03

22. Suburban

0.09** 0.02

11

12

13

14

–0.01

–0.02

–0.02

0.01

15 1

16  

17

–0.05** 0.04** 0.21** 1

0.14** –0.04** –0.10** –0.12** 0.01

10

0.16** 0.03*

0.03

9

 

 

18

0.03

–0.04** –0.30** 0.29** –0.08** 0.01

–0.09** –0.05** 1

 

 

 

19

 

 

 

 

20

–0.02

0.03

–0.01 –0.02

0.03

–0.01 0.03

0.03 –0.02

–0.01

–0.00

0.08** –0.10** 0.05** 0.02

–0.06** 0.07** –0.07** –0.02

–0.06** 0.09** –0.17** 0.20** –0.03

0.02

0.00

–0.02

–0.07** 0.00

0.21** 0.03

 

 

 

 

 

 

22

–0.57** 1

1

 

 

 

 

 

21

–0.05** –0.11** –0.03* –0.28** –0.64**

0.03*

0.02

0.59** 0.24** –0.14** –0.11** –0.23** 0.07** –0.31** 0.08** 0.20** 0.39** –0.22** 1

–0.15** 0.01

0.16** 0.59** 0.26** –0.12** –0.14** –0.31** 0.06** –0.28** 0.07** 0.16** 1

21. Urban

–0.01

0.20** 0.01

–0.03* –0.05** –0.06** 0.01

0.03

20. Length of stay

–0.02

0.06** –0.16** –0.18** 0.04*

–0.01

–0.01

0.15** 0.18** –0.02

0.04** 0.00

6

0.03*

0.01

0.03*

5

0.01

0.01

4

19. Home ownership

–0.01

–0.01

3

18. Work status –0.05** –0.08** –0.09** 0.01

0.00

17. # of 0.01 friends in neighbourhood

2

–0.00

1

16. # of 0.01 relatives in neighbourhood

 

Environmental degradation on neighbourhood satisfaction and quality of life  301

302  Handbook of quality of life research Table 19.3

Hierarchical regression results of the effects of individual characteristics and environmental degradation on neighbourhood satisfaction

 

Model 1: Individual

Model 2: Individual

Model 3: Individual

Characteristics

Characteristics + Four

Characteristics +

Environmental Degradation Environmental Degradation Index

Variables Gender (1: Male; 2: Female)

Beta

p-value

VIF

Beta

p-value

VIF

Beta

p-value

VIF

0.04

0.01

10.08

0.05

0.00

10.08

0.05

0.00

10.08

Age

0.20

0.00

20.48

0.18

0.00

20.50

0.18

0.00

20.49

Education:

 

 

 

 

 

 

 

 

 

High school or less (reference group)

–

–

–

–

–

–

–

–

–

College graduate or some college

0.01

ns

10.54

0.01

ns

10.54

0.01

ns

10.54

Graduate/Professional degree

0.05

0.01

10.68

0.06

0.01

10.69

0.06

0.01

10.68

Income

0.15

0.00

10.77

0.15

0.00

10.78

0.15

0.00

10.77

Married or living together (1: Married

–0.03

ns

10.39

–0.03

08

10.39

–0.03

ns

10.39

–0.03

ns

10.45

–0.03

10

10.45

–0.03

ns

10.45

or living together; 2: Single, divorced or widowed) Children (0: No children; 1: Has children under 18) Number of relatives in the neighbourhood –0.01

ns

10.08

–0.00

ns

10.08

–0.01

ns

10.08

Number of friends in the neighbourhood

0.17

0.00

10.12

0.17

0.00

10.12

0.17

0.00

10.12

Work status (1: Full- and part-time

–0.05

0.01

10.70

–0.05

0.01

10.71

–0.05

0.01

10.70

working; 2: students, retired, homemaker and other) Home ownership (1: Own; 2: Other)

–0.06

0.00

10.33

–0.06

0.00

10.33

–0.06

0.00

10.33

Length of stay

–0.08

0.00

10.67

–0.08

0.00

10.67

–0.08

0.00

10.67

Type of places:

 

 

 

 

 

 

 

 

 

Rural (reference group)

–

–

–

–

–

–

–

–

–

Urban

–0.22

0.00

10.61

–0.20

0.00

10.65

–0.21

0.00

10.61

Suburban

–0.09

0.00

10.49

–0.08

0.00

10.51

–0.08

0.00

10.49

Loss of natural areas

 

 

 

–0.02

ns

20.70

 

 

 

Loss of natural places for fish and wildlife  

 

 

–0.02

ns

20.84

 

 

 

 

 

0.02

ns

10.95

 

 

 

to live Discharge of waste into lakes, rivers and

 

streams Air pollution

 

 

 

–0.16

0.00

10.94

 

 

 

Environmental degradation index

 

 

 

 

 

 

–0.14

0.00

10.02

R

0.38

0.00

 

0.41

0.00

 

0.41

0.00

 

R2

0.14

0.00

 

0.17

0.00

 

0.16

0.00

 

Note: ns: not significant. Source: The authors.

friends in respondents’ neighbourhoods were significant predictors of neighbourhood satisfaction. In other words: ● respondents who were female and older, had higher income, worked full or part time, owned their own home, had longer tenure stay at the address, and had more friends in their neighbourhood were more satisfied with their neighbourhoods; ● respondents who had graduate or professional degrees had higher neighbourhood satisfaction compared with those with a high school or less education; and

Environmental degradation on neighbourhood satisfaction and quality of life  303 ● residents living in urban and suburban areas had lower neighbourhood satisfaction compared with those who were living in rural areas. These individual characteristics in Model 1: Individual Characteristics explained 14 per cent of the variance for neighbourhood satisfaction. In addition, the variation inflation factors indicated that there was no multicollinearity among the individual characteristic variables. In a second step in the hierarchical regression analysis, four environmental degradation variables were added in addition to the individual characteristics to form Model 2: Four Environmental Degradation Variables (see Table 19.3). After controlling for individual characteristics, the air pollution variable was the only significant predictor of neighbourhood satisfaction. The zero-ordered multicollinearity between loss of natural areas and loss of natural places for fish and wildlife to live was eliminated after controlling for all other variables. Also, there was a 3 per cent R-squared increase from Model 1: Individual Characteristics (14 per cent) to Model 2: Four Environmental Degradation Variables (17 per cent). As the final step in the hierarchical regression analysis, the environmental degradation index variable was added to form Model 3: Environmental Degradation Index (see Table 19.3). Model 3 explained 16 per cent of the variance for neighbourhood satisfaction. The results of Model 2: Four Environmental Degradation Variables and Model 3: Environmental Degradation Index were very similar. However, Model 2 pinpointed the specific contribution of air pollution to neighbourhood satisfaction. Hierarchical regression analysis II: effects of individual characteristics and environmental degradation on quality of life Hierarchical regression analysis was also undertaken with QOL as the dependent variable (Table 19.4). In Model 1: Individual Characteristics (see Table 19.4), almost all individual characteristics were significant predictors of the QOL index except for educational differences and the difference between rural and urban living. Significant results were not surprising since variables known to affect QOL were chosen. Significant differences were not found between those with a high school education or less in comparison with the other two education categories. The individual characteristics in Model 1 explained 12 per cent of the variance for QOL. In addition, the variation inflation factors indicated no multicollinearity among the individual characteristics. In the second step of the hierarchical regression analysis, Model 2: Four Environmental Degradation Variables (in Table 19.4) shows that, after controlling for all individual characteristics, the loss of natural places for fish and wildlife to live and the air pollution variables were significant predictors of residents’ QOL, while the loss of natural area and discharge of waste were not significant. Adding the four environmental degradation variables into the hierarchical regression resulted in a 2 per cent increase in R2, totalling 14 per cent. In addition, the variation inflation factors indicated no multicollinearity among the individual characteristics as well as environmental degradation variables. As the final step in the hierarchical regression analysis, the four environmental degradation variables were replaced with the environmental degradation index to form Model 3: Environmental Degradation Index (see Table 19.4). This model explained 14 per cent of the variance for QOL. In other words, greater environmental degradation as a whole was associated with lower QOL.

304  Handbook of quality of life research Table 19.4

Hierarchical regression results of the effects of individual characteristics and environmental degradation on quality of life

 

Model 1: Individual

Model 2: Individual

Model 3: Individual

Characteristics

Characteristics + Four

Characteristics +

Environmental Degradation Environmental Degradation Index

Variables Gender (1: Male; 2: Female)

Beta

p–value

VIF

Beta

p–value

VIF

Beta

p–value

VIF

0.04

0.02

10.08

0.04

0.01

10.08

0.04

0.01

10.08

Age

0.12

0.00

20.48

0.10

0.00

20.50

0.11

0.00

20.49

Education:

 

 

 

 

 

 

 

 

 

High school or less (reference group)

–

–

–

–

–

–

–

–

–

College graduate or some college

0.03

ns

10.54

0.03

ns

10.54

0.04

ns

10.54

Graduate/Professional degree

0.04

06

10.68

0.04

0.04

10.69

0.04

0.04

10.68

Income

0.16

0.00

10.77

0.15

0.00

10.78

0.15

0.00

10.77

Married or living together (1: Married

–0.07

0.00

10.39

–0.07

0.00

10.39

–0.07

0.00

10.39

Children (0: No children; 1: Has children –0.06

0.00

10.45

–0.07

0.00

10.45

–0.07

0.00

10.45

0.04

10.08

0.04

0.03

10.08

0.04

0.03

10.08

or living together; 2: Single, divorced or widowed) under 18) Number of relatives in the

0.03

neighbourhood Number of friends in the neighbourhood 0.11

0.00

10.12

0.11

0.00

10.12

0.11

0.00

10.12

0.16

0.00

10.70

0.16

0.00

10.71

0.16

0.00

10.70

Work status (1: Full and part-time working; 2: students, retired, homemaker and other) Home ownership (1: Own; 2: Other)

–0.06

0.00

10.33

–0.06

0.00

10.33

–0.06

0.00

10.33

Length of stay

–0.05

0.01

10.67

–0.05

0.01

10.67

–0.05

0.01

10.67

Type of places:

 

 

 

 

 

 

 

 

 

Rural (reference group)

–

–

–

–

–

–

–

–

–

Urban

–0.07

0.00

10.61

–0.06

0.01

10.65

–0.06

0.00

10.61

Suburban

0.00

ns

10.49

0.01

ns

10.51

0.01

ns

10.49

Loss of natural areas

 

 

 

0.01

ns

20.70

 

 

 

Loss of natural places for fish and

 

 

 

–0.10

0.00

20.84

 

 

 

Discharge of waste into lakes, rivers and  

 

 

0.01

ns

10.95

 

 

 

wildlife to live streams Air pollution

 

 

 

–0.08

0.00

10.94

 

 

 

Environmental degradation index

 

 

 

 

 

 

–0.13

00

10.02

R

0.35

0.00

 

0.38

0.00

 

0.37

0.00

 

R2

0.12

0.00

 

0.14

0.00

 

0.14

0.00

 

Note: ns: not significant Source: The authors.

DISCUSSION AND CONCLUSIONS This chapter shows that environmental degradation – as reported by respondents throughout the metro-Detroit survey results – affects both neighbourhood satisfaction and people’s QOL. Specifically, concerns about the loss of fish and wildlife habitat, as well as air pollution, were associated with self-reported QOL and neighbourhood satisfaction. Most people were aware

Environmental degradation on neighbourhood satisfaction and quality of life  305 of the health effects of toxic air, and in many cases, noxious odours and fumes interfere with the enjoyment of a place. This would be of even larger concern in chronic cases where these conditions are experienced on a regular basis. When people live in neighbourhoods where they are exposed day to day to polluted air, it would be sensible for this to weigh heavily on their residential satisfaction. Thus, findings showing that air pollution is negatively related to neighbourhood satisfaction was not surprising. A more in-depth examination of neighbourhood satisfaction that includes additional questions about the conditions in the neighbourhood might well yield even more robust findings. The findings reported in this chapter are consistent with the findings of other studies (such as Darçın, 2014; Ferrer-i-Carbonell and Gowdy, 2007; Luechinger, 2009; Orru et al., 2016; Welsch, 2006), which found associations between objective measures of air pollution and life satisfaction. As with neighbourhood satisfaction, QOL is negatively related to concerns about air pollution. When the survey respondents see air pollution as a serious problem, they also reported a lower QOL. Again, this is consistent with findings that used objective measures of air pollution. However, the concern about loss of habitats for fish and wildlife adds a new dimension to previous findings. Loss of wildlife habitat generally does not pose an immediate threat to a person’s health or well-being unless they supplement their diet through fishing or foraging, or their livelihood is dependent on local well-functioning ecosystems. If that were the case, a significant relationship might be expected with neighbourhood satisfaction. Instead, it suggests that a more altruistic motivation exists that is affecting QOL. Several explanations are worth exploring in this area. One might focus on the effect of education and public awareness about global habitat loss and its effect on QOL. Although public engagement on environmental issues may vary across populations, the simple (yet emotional) act of caring about other creatures and their plight may induce a QOL response. Another direction might be to measure people’s knowledge and opinions of urban growth and expanding human settlements. As people witness farmland and woodlands being converted into housing developments and shopping centres, the loss of what was once perceived as habitat may also induce a QOL response. Although the findings reported here are limited in their scope by a single survey question, the findings could lead to some interesting future investigations. The data used represents survey participants from the city of Detroit and from the surrounding suburbs. One could imagine that the results might be different if inner-city residents were studied separately from those in the suburbs and rural areas. For example, concerns about habitat loss or rural land degradation might be higher in the suburbs and beyond where land conversion to urban development is more prevalent. Alternatively, concerns about air pollution might be more acute in the heart of Detroit where current and legacy pollution from industrial sources are more concentrated. That is, there may be room to drill down even deeper into geographic sub-regions for even greater clarity on some findings. In addition, attitudes and observations may change over time. If the Detroit area survey were repeated today at a time when concerns about climate change and biodiversity loss are in the public consciousness more frequently, the findings could be strengthened, or even more associations might be found, especially if it was expanded to include more questions about environmental degradation. Environmental degradation is an important issue affecting QOL. Identifying the relationships between subjective measures of environmental degradation and QOL helps to reveal the perceptions people have about their living environments, and how they are affected by them.

306  Handbook of quality of life research As demonstrated here, habitat loss and air pollution are two factors that negatively affect QOL and neighbourhood satisfaction. Future studies need to continue to refine these factors and explore other potential negative influences in efforts to improve the human condition. Emphasis might be given to the impact of environmental degradation on people of lower socio-economic means who often live in the less desirable parts of a city or region. Also, with COVID-19 increasing the time people spend at home, it would be wise to expand questions on neighbourhood satisfaction to gain a more refined picture of its relationship to environmental degradation and QOL.

REFERENCES Ciorici, P. and Dantzler, P. (2019), ‘Neighborhood satisfaction: a study of a low-income urban community’, Urban Affairs Review, 55, 1702–30. Cook, C.C. (1988), ‘Components of neighborhood satisfaction: responses from urban and suburban single-parent women’, Environment and Behavior, 20, 115–49. Darçın, M. (2014), ‘Association between air quality and quality of life’, Environmental Science and Pollution Research, 121, 954–1959. Ellis, C.D., Lee, S. and Kweon, B. (2006), ‘Retail land use, neighborhood satisfaction and the urban forest: an investigation into the moderating and mediating effects of trees and shrubs’, Landscape and Urban Planning, 74, 70–78. Ferrer-i-Carbonell, A. and Gowdy, J.M. (2007), ‘Environmental degradation and happiness’, Ecological Economics, 60, 509–16. Gwangndi, M.I., Muhammad, Y.A. and Tagi, S.M. (2016), ‘The impact of environmental degradation on human health and its relevance to the right to health under international law’, European Scientific Journal, 12, 485–503. Hur, M., Nasar, J.L. and Chun, B. (2010), ‘Neighborhood satisfaction, physical and perceived naturalness and openness’, Journal of Environmental Psychology, 30, 52–9. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (2018), The IPBES Assessment Report on Land Degradation and Restoration, L. Montanarella, R. Scholes and A. Brainich (eds), Bonn: Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Johnson, D.L., Ambrose, S.H. and Bassett, T.J. et al. (1997), ‘Meanings of environmental terms’, Environmental Issues, 26, 581–9. Kaplan, S. (1995), ‘The restorative benefits of nature: toward an integrative framework’, Journal of Environmental Psychology, 15, 169–82. Kweon, B. and Marans, R.W. (2011), ‘Disaggregating the measurement of quality of urban life dimensions across a complex metro-region: the case of Metro Detroit’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 369–84. Kweon, B., Sullivan, W.C. and Wiley, A.R. (1998), ‘Green common spaces and the social integration of inner-city older adults’, Environment and Behavior, 30, 832–58. Lansing, J.B. and Marans, R.W. (1969), ‘Evaluation of neighborhood quality’, Journal of the American Institute of Planners, 35, 195–9. Lee W., Ellis, C.D., Kweon, B. and Hong S. (2008), ‘Relationship between landscape structure and neighborhood satisfaction in urbanised areas’, Landscape and Urban Planning, 85, 60–70. Luechinger, S. (2009), ‘Valuing air quality using the life satisfaction approach’, The Economic Journal, 119, 482–515. Majeed, M.T. and Ozturk, I. (2016), ‘Environmental degradation and population health outcomes: a global panel data analysis’, Environmental Science and Pollution Research, 27, 15901–11. Marans, R.W. and Kweon, B. (2011), ‘The quality of life in Metro Detroit at the beginning of the millennium’, in R.W. Marans and R.J. Stimson (eds), Investigating Quality of Urban Life: Theory, Methods, and Empirical Research, Dordrecht: Springer, pp. 163–83.

Environmental degradation on neighbourhood satisfaction and quality of life  307 Marans, R.W. and Mohai, P. (1991), ‘Leisure resources, recreation activity, and the quality of life’, in B.L. Driver, P. Brown and G.L. Peterson (eds), The Benefits of Leisure, State College, PA: Venture Publishing, pp. 351–63. Martin, D. (2003), ‘Causes and health consequences of environmental degradation and social injustice’, Social Science and Medicine, 56, 573–87. Millennium Ecosystem Assessment Board (2005), Ecosystems and Human Well-Being: Synthesis, Washington, DC: Island Press. Orru, K., Orru, H. and Maasikmets, M. et al. (2016), ‘Well-being and environmental quality: does pollution affect life satisfaction?’, Quality of Life Research, 25, 699–705. Rahman, M.M., Alam, K. and Velayutham, E. (2021), ‘Is industrial pollution detrimental to public health? Evidence from the world’s most industrialised countries’, BMC Public Health, 21, Article 1175. Rangel, A. (2003), ‘Forward and backward generational goods: why is social security good of the environment?’, American Economic Review Revies, 93, 813–34. Sun, Y. (2005), Developing Neighborhood Quality of Life Indicators, Saskatchewan: Community-University for Social Research. Ulrich, R.S. (1984), ‘View through a window may influence recovery from surgery’, Science, 224, 420–21. Vallero, D.A. (2014), Fundamentals of Air Pollution, 5th edition, Atlanta, GA: Academic Press. Welsch, H. (2006), ‘Environment and happiness: valuation of air pollution using life satisfaction data’, Ecological Economics, 58, 801–13.

20. Nature-based solutions and quality of life: protecting, restoring and building human capacities Marino Bonaiuto and Susana Alves

INTRODUCTION This chapter provides a social-psychological analysis of nature-based solutions (NBS) using a tripartite framework that addresses harm reduction, restoration and instoration as three possible environmental psychology mechanisms shaping the interaction between nature and health outcomes enhancing people’s quality of life (QOL) and well-being. NBS is defined as ‘actions which are inspired by, supported by, or copied from nature’ (European Commission, 2015, p. 4). As a new type of green infrastructure, NBS refers to living solutions underpinned by natural processes and structures designed to address various ecological and environmental challenges, at the same time positively contributing to health, QOL and urban sustainability (Benedict and McMahon, 2006; Kabisch et al., 2017). Studies mostly focus on ecosystem-related benefits – such as coastal resilience and ecosystem restoration – giving less attention to short- or long-term social, health and psychological benefits. A new rational approach is needed to understand how environmental protection, restoration and instoration can enhance QOL, which is in tune with an ecological view of perception. Rather than an intrinsic set of qualities, the benefits of NBS can be specified as affordances – that is, possibilities to act in the environment (Gibson, 1979) – and their actualisation in goal-oriented, nature-related activities (Kyttä, 2004). Studying the entanglements of perception and action in daily life may help overcome instrumentalisation, reconnect humans with nature and promote QOL (see Fornara et al., 2020; Molinario et al., 2020; Scopelliti et al., 2018), and enhance planning for sustainable outcomes. Bonaiuto and Albers (2020) propose using an NBS conceptual framework to understand the mechanisms linking NBS to QOL outcomes through three pathways: ● harm reduction that mitigates the harmful effects of environmental stressors; ● restorative mechanisms that are offered to sustain emotional regulation, relieve stress and replenish cognitive capacities; and ● instorative experiences that foster capacity-building. The chapter analyses this tripartite framework and its components.

CONCEPTUAL FRAMEWORK FOR NBS QOL can be enhanced by reducing stress and fostering well-being. Reducing stress refers to protecting people from negative environmental and social stressors as well as restoration after 308

Nature-based solutions and quality of life  309 stress. Fostering well-being focuses on salutogenic effects by developing new capabilities (Devlin, 2018). These may be integrated using a life-course approach to better explain the health-related benefits of nature (Douglas et al., 2017), but they are not clearly integrated into NBS, which requires both mitigation of harm and restoration of healthy behaviours approached using a relational view of nature by identifying affordances; that is, actionable properties – dangers and opportunities – relating perceiver and the world perceived (Gibson, 1979), impacting QOL. Dangers or harms need to be mitigated, and opportunities facilitated to replenish deprived cognitive and physical resources (that is, restoration) or to instore and build new capabilities (that is, instoration) to sustain good QOL. It is a person–environment relation constantly emerging out of the entanglement of diverse agents, including human and non-human participants (Inauen et al., 2021). The three main components of the NBS framework proposed in Figure 20.1, accompanied by their implications to people’s QOL, health and well-being, are now discussed.

Source: Adapted from Bonaiuto and Albers (2020) and Markevych et al. (2017).

Figure 20.1

Conceptual framework for NBS

Harm Reduction from NBS Harm reduction interventions refer to solutions created to help cope with hazards by identifying drivers and finding opportunities to reduce negative outcomes through achievable

310  Handbook of quality of life research objectives (Secretariat of the Convention on Biological Diversity, 2009). The effects of NBS on harm reduction are discussed in relation to water quality, air pollution and noise pollution. Water quality Addressing harm reduction effects in treating water pollution at a regional level, a study of a water park in Gorla Maggiore, Italy (Liquete et al., 2016) compared different traditional interventions with using ‘green’ interventions proposed in the NBS framework, with the latter shown to be beneficial for treating water pollution and for flood production, with cost and other benefits such as wildlife support and enhancing recreation. A follow-up study showed that households valued green interventions more than conventional ones and were willing to pay for them (Reynaud et al., 2017). At the household level, a study of a squatter settlement in Cochabamba, Bolivia (Wutich and Ragsdale, 2008) showed the negative impact of water insecurity to be mediated by social and economic factors. Emotional distress is higher when access to water distribution systems procedures is unclear or absent, with women experiencing more emotional distress than men as they have more responsibility for the acquisition and use of water. In low- and middle-income countries, water insecurity is usually associated with gender: women suffer the highest sources of emotional distress, as they directly bear responsibility for water procurement and distribution (Agarwal, 2018; Cooper-Vince et al., 2018). Domestic wastewater treatment systems are a significant source of risk. Removing water contaminants may not be enough to promote long-term health outcomes, but combining technical interventions (such as well-designed treatment systems) facilitates people’s adaptation by sustaining their awareness (Mooney et al., 2020). Older individuals seem more likely to recognise treatment systems as a potential hazard to health compared to younger people. Stakeholders are more likely to actively engage in changing wellness maintenance behaviours when they perceive a tangible risk to health. In positive terms, living near and being exposed to ‘blue spaces’ promotes health and well-being (Chen and Yuan, 2020; Dempsey et al., 2018). A New Zealand study showed that higher visibility of mostly oceanic blue space was associated with lower psychological distress after controlling for covariates, such as income (Nutsford et al., 2016). Air pollution NBS may reduce negative effects of air pollution near large transportation sources, consequently enhancing health. Urban vegetation helps reduce local air pollution hotspots, especially when barriers are created to limit pollution dispersion into areas where people live and recreate (Baró et al., 2014). Street trees are strongly associated with lower air pollution levels and better mental health (Wang et al., 2020). Installing vertical greenery (such as plants in planter boxes and vertically on walls) in the environment makes significant changes in terms of decreasing particulate matter. Indoor vertical greenery also improves environmental conditions and positively influences behaviour and people’s patterns of use (Ghazalli et al., 2018). Noise pollution Urban green spaces act as a psychological buffer from the impact of noise pollution (Dzhambov and Dimitrova, 2014). Key buffers are plants and green walls to absorb ambient noise and temperature in dense urban areas (Paull et al., 2020). Views of sea, urban rivers or greenery can lower the probability of annoyance, while views of industrial noise barriers may

Nature-based solutions and quality of life  311 increase annoyance perception (Leung et al., 2017). Moreover, combining ‘green’ NBS and ‘blue’ NBS (such as noise barriers created by means of vegetation and of water) has additive buffering effects by further lowering people’s annoyance relating to noise (ibid.). A combination of green and blue spaces can confer health and cognitive benefits (Gidlow et al., 2016). The visual characteristics of green barriers (green/plant related) are also important and they produce less noise annoyance compared with an industrial barrier (Maffei et al., 2013). Prevention against risks to health and well-being NBS can prevent the risk of people developing negative symptoms and pathological symptoms. A large epidemiological study of almost 1 million people in Europe (Engemann et al., 2019) found that green space can provide long-term mental health benefits and possibly lower risk of psychiatric disorders. After controlling for other known risk factors, children who had lower levels of green space exposure growing up had up to a 55 per cent higher risk of developing a psychiatric disorder. This strong association between childhood accumulated green space and risk provides further evidence that long-term exposure to green space is important (ibid.). Another study involving 5 million residents in California (Van Den Eeden et al., 2022) found an inverse relationship between the amount of green area and healthcare expenditure. Adjusting for comorbidity, the results were consistent with the hypothesis that green space improves health status, possibly reducing healthcare costs (ibid.). Such findings indicate that integrating natural environments into urban planning may be a promising approach to preserving mental health, reducing the upward trend in psychiatric disorders, and reducing the cost of healthcare. QOL Restoration from NBS Restoration involves the renewal or recovery of psychological and adaptive resources used up in everyday life and needed for effective functioning (Hartig et al., 2014). Stress recovery is made possible by natural scenery (such as vegetation and water) evoking positive emotions (Ulrich, 1983). Promoting physiological, affective and cognitive NBS restoration can improve a person in many respects, and so restoring overall QOL. Physiological and stress recovery Non-threatening nature elicits immediate positive change in affective and stress-reducing outcomes (Ulrich et al., 1991). For people experiencing high levels of stress, walking in nature compared with just viewing nature can decrease overall stress (Olafsdottir et al., 2020). Stress relief (such as increased meditation and decreased arousal or frustration) is apparent in people walking in green spaces compared with an urban shopping street (Aspinall et al., 2015). Exposure to virtual nature using virtual reality (VR) also elicits relaxation, improves functioning of the immune system and decreases the deleterious effects of chronic stress (White et al., 2019). Both VR and exposure to outdoor spaces enhance positive moods and are more restorative compared with indoor settings (Browning et al., 2020). A virtual forest environment can increase people’s vigour and decrease confusion, fatigue and depression (Yu et al., 2018). Direct involvement in nature also has stress-relieving effects. Examining the impact of gardening and reading after completing a stressful task, Van den Berg and Custers (2011) found cortisol levels decreased to a greater extent when gardening compared with reading indoors.

312  Handbook of quality of life research Besides indirect and direct involvement in nature, activity type has been associated with a drop in amylase response (that is, a marker of physical and mental stress measured by salivary amylase levels), showing that the experience of walking in nature produced a higher drop per hour than just sitting or sitting plus walking (Hunter et al., 2019). Immunological and hormonal systems can be positively impacted by some kinds of NBS. For example, ‘forest bathing’ (that is, trips to the forest for the purpose of relaxation and recreation) not only promotes the feeling of ‘getting away’ and reduces stress (Tsunetsugu et al., 2010), it also increases natural killer (NK) cell activity, which supports immune system functioning (Li, 2010), triggered by the respiration of phytoncides present in the air and trees. The oxytocinergic system mediates the positive mental and physiological effects of contact with nature. Release of oxytocin (a hormone that facilitates bonding between individuals) also exerts powerful anti-stress effects (Uvnäs-Moberg et al., 2015). Nature phenomena signalling beauty, pleasure and safety may trigger the activation of the oxytocinergic system, in turn facilitating psychological development and attachment to nature (Grahn et al., 2021). Health benefits of oxytocin include stimulation of social interactions, healing and restorative effects, and reduction of fear and stress (ibid.). Affective restoration Affective restoration from NBS is studied by measuring affect intensity after exposing people to a stress manipulation condition (for example, frightening movies). In general, people who view natural environments compared to built environments demonstrate a greater decrease in affective measures such as depression, anger and tension (Van den Berg et al., 2003). These positive emotional effects are found in both healthy and clinical populations. People experiencing high or continuous levels of stress and/or mental disorders also seem to benefit from NBS. Blue space visits are associated with high levels of happiness and low levels of anxiety across the entire population, including people suffering from common mental health disorders (Tester-Jones, 2020). People with more depressive symptoms display less stress after viewing nature compared with observing built settings (Meuwese et al., 2021). Research is inconclusive about the right dose of nature to ensure health benefits. In some cases, 120 minutes of contact with nature every week led to increases in health and well-being (White et al., 2019). In other cases, a daily dose of as little as 10–20 minutes sitting or walking in an array of green spaces increased positive affect (Meredith et al., 2020). Brief walks in nature also increase positive affect and decrease negative affect when compared with urban walks (Fuegen and Breitenbecher, 2018), and visits to rural and coastal locations provide restoration and a greater sense of connectedness with nature. Growing up surrounded by natural environments is related to lower rates of mental disorder. Using green areas during childhood helps to buffer later adult mental disorders, including depression, bipolar and schizophrenic disorders (Engemann et al., 2020). The closer one lives to blue spaces, the higher the likelihood one visits them to gain QOL benefits (Elliott et al., 2020). Passive interaction with water elements or just living close to the coast is one way to improve health (Gascón et al., 2017; Hooyberg et al., 2020). Cognitive restoration Cognitive restoration (that is, performing better in cognitive and memory-related tasks) happens after individuals view scenes of natural environments (Kaplan and Kaplan, 1989; Stevenson et al., 2018). NBS in the form of green breaks are cognitively restorative to chil-

Nature-based solutions and quality of life  313 dren, both in standardised team play and in relation to free play activities. An increase in sustained and selective attention and working memory has been observed in children after taking a break in a green natural environment, while there was no increase or even a decrease in these measures after the children interacted with the built environment break condition (Amicone et al., 2018). The greatest likelihood of recovering from mental fatigue has been achieved in streetscapes with a higher number of plants (with a diversity of species) and fewer non-motor vehicles, thus indicating specific environmental features are associated with cognitive restorative experiences (Zhao et al., 2020). In the work environment, Aristizabal et al. (2021) found that introducing immersive biophilic design in offices improved the satisfaction and cognitive performance of occupants. Those findings stress how different restorative processes (that is, affective, cognitive) can work in parallel within a given context of person–environment interaction, also highlighting the need to consider both visual and auditory factors, as well as their combination, in biophilic design. QOL Instoration from NBS Instorative processes involve the building of new adaptive capacities by changing a person’s pattern of behaviour and activities. It also refers to giving individuals and communities access to knowledge and information. Improving communication, establishing a repository of best practices and evidence is key to building skills, networks and planning/implementing NBS. Instoration is not necessarily connected to a prior depletion of affective or attentional resources (Korpela and Ratcliffe, 2021), but it needs to be conceived in relation to the affordances provided by the larger person–environment context. NBS can improve the person under many respects, by means of physical and/or social activities, and therefore it can enhance QOL. Improved physical and mental health Nature-related pursuits, such as physical activity in outdoor spaces, have both physical and psychological positive outcomes (Ward Thompson and Aspinall, 2011). Contact with nature improves immune functioning and consequently prevents the development of ill health (Kuo, 2015). Green spaces increase physical activity, which in turn may provide tranquillity, restfulness, mental health and cognitive development (Ngulani and Shackleton, 2019). Green spaces provide self-regulation (Weeland et al., 2019). Pathways for promoting self-regulation include increasing children’s opportunities to play outside to benefit from the positive effects of daylight exposure and increased physical activity (Hunter et al., 2015). Self-regulation also happens when NBS broadens children’s mindset (see broaden-and-build theory; Fredrickson, 2004). It helps build the capacity for mindfulness – a state of non-judgemental awareness of experiences in the present moment (Lymeus et al., 2019), engaging people’s effortless attention and decreasing ruminative thoughts (Bratman et al., 2019; Golding et al., 2018). Higher residential green space, for example, has been associated with mental health via increased capacity-building and capacity-restoring pathways (Dzhambov et al., 2019). Similar to restorative experiences, instorative experiences seem to have a greater effect for those experiencing high levels of stress. Green exercise (that is, physical activity in natural environments) appears to facilitate well-being, particularly for those reporting a ‘low’ starting

314  Handbook of quality of life research level of well-being compared with those reporting average to high starting levels (Rogerson et al., 2020). Moreover, wilderness experiences can also lead to instoration via personal growth, enhanced self-esteem and a greater sense of vitality. Feeling at home in the wilderness contributes to a sense of wonder and affective attachment to places, which are motivating forces toward greater reflection (Løvoll et al., 2020). Even settings considered less than ideal for physical activity – such as wetlands – make a difference to mental health or individuals diagnosed with anxiety and depression (Maund et al., 2019). For adults, exercising alone or with others in urban wastelands or brownfield sites is important for recreation (Püffel et al., 2018) and can lead to positive health outcomes. People appreciate non-cultivated, spontaneous vegetation in urban environments (Chen et al., 2021), demonstrating that even unspectacular nature activates people’s senses and contributes to positive experiences, such as naturalism and nostalgia. People who visited diverse types of natural spaces (ranging from wild nature to managed parks and beaches) had higher life satisfaction (Chang et al., 2020), suggesting that a diversity of natural spaces can contribute to increased life satisfaction and QOL. Diversity in green and blue spaces increases health benefits when compared with environments with low-quality natural elements such as agricultural areas with monocultural plant communities (Dean et al., 2011; Fuller et al., 2007). Social cohesion Increased social cohesion is usually associated with various physical and psychological health benefits, with research showing that people’s contact with nearby green spaces contributes to place attachment and community satisfaction (Bonaiuto et al., 2016; Jennings and Bamkole, 2019). Outdoor walking, gardening and visiting nearby green spaces enhances informal contacts with others, creating opportunities for forging social networks (Hartig et al., 2014). Positive interactions between members of different groups may reduce intergroup prejudice and promote equity and justice (Cohen, 1991). Thus, NBS may improve QOL via promoting social cohesion. Social cohesion is also associated with various patterns of use (de Vries et al., 2013; Wan et al., 2021) and with the frequency and duration of visits to outdoor spaces (Kaźmierczak, 2013). Trees are a key element encouraging the use of common spaces and facilitating positive informal social interactions (Elands et al., 2018). Community gardening activities also promote social cohesion and build resilience (Hartig et al., 2014). Positive changes in attitudes and behaviour NBS may facilitate the assimilation and accommodation of new information, leading people to think or act in new ways and to change attitudes or behaviours. Sense of connectedness – defined as the cognitive, emotional and biophysical linkages to places, landscapes and ecosystems (see Ives et al., 2017) – is positively correlated with pro-environmental behaviour, attitudes and intentions (Kals et al., 1999; Schultz et al., 2004). A significant factor shaping positive or negative attitudes and behaviour is exposure to nature during childhood. For example, direct exposure to nature through a camp increases children’s emotional affinity towards the natural environment, as well as their ecological beliefs (Collado et al., 2013). Studies show a strong relationship between frequent childhood visits to woodlands and being willing to visit green spaces as an adult (Ward Thompson et al., 2008). Molinario et al. (2020) found that recalled frequency of activities in nature during childhood

Nature-based solutions and quality of life  315 (in school or family settings) correlates with nature connectedness and pro-environmental behaviour in adulthood. Indirect experiences of nature through images and programmes – such as watching nature programmes or learning activities related to nature – prompt sustainable actions (Cárdenas et al., 2021; Martin et al., 2020). The parents of children engaged in an environmental education school programme for one school year based on visits to natural reserved areas show increased pro-environmental attitudes (De Dominicis et al., 2017). Extraordinary nature-based experiences in addition to ordinary activities may be resources that trigger epiphanies and have the potential to positively affect people’s behaviour and QOL (Vining and Merrick, 2012). Increased connection with nature and other species (more-than-human nature) Instorative processes can be fostered through human–nature connectedness. Human–nature connectedness refers to ‘a stable state of consciousness comprising symbiotic cognitive, affective, and experiential traits that reflect a realisation of the interrelatedness between one’s self and the rest of nature’ (Zylstra et al., 2014, p. 119). This expansion of the notion of nature connectedness to a more-than-human world is consistent with the belief that when individuals extend their self-definitions to include the natural world, they act in an eco-friendlier way towards the environment (Clayton, 2003; Tooth and Renshaw, 2020). People’s emotional connection to nature may lead to an expanded sense of self and greater valuing of non-human species, which can consequently lead to pro-environmental behaviours (Gosling and Williams, 2010). Routes towards greater connectedness to nature and instorative experiences include enhancing biophilic values (Kellert et al., 2008), engaging in nature-based activities themed around compassion and beauty (Lumber et al., 2017), and increasing contact with nature during childhood. Childhood nature experiences directly and negatively affect both disgust sensitivity and fear expectancy (Sugiyama et al., 2021). A sense of kinship with nature also motivates people (such as farmers) to protect wildlife later in life (Gosling and Williams, 2010). To support both connectedness to nature and pro-environmental actions, place-based pedagogies may be the next step moving forward. Experiential learning and outdoor education can promote students’ sense of connectedness as students can learn about nature by observing and engaging directly with the surrounding local environment in its different aspects, such as learning about diverse types of biomes, vegetation, minerals, fauna, and so on (Lynch and Mannion, 2021).

CONCLUDING COMMENTS ABOUT NBS FOR QOL NBS and their contributions to QOL takes at least three distinct forms by: ● mitigating stress; ● restoring people’s emotion and cognitive resources; and ● fostering new resources and skills. Pathways to experiencing nature need to be considered for societal and technological change (Clayton et al., 2017). It is the experience of nature that enables positive changes in ways that can ultimately be integrated into people’s identities (Clayton, 2012), or within dyadic or small

316  Handbook of quality of life research group relations (Hartig, 2021). NBS depend on people’s ability to attach to places, create meaning and share their experience with others (Devine-Wright, 2013). The studies examined in this chapter are cross-sectional in nature, thus it is difficult to make firm conclusions to identify positive patterns of change in mere exposure to nature compared with studies that are more intentional and goal-oriented (an exception is De Dominicis et al., 2017). What can be said is that both small- and large-scale NBS are beneficial to diverse groups of people, and that activities in both green and blue spaces positively impact QOL. Future NBS will benefit from knowing where one feels connected to nature and where one acts sustainably and pro-environmentally. Archetypes of relatedness and interactions (that is, equal interactions, embedded interactions and extended interactions) can be a useful guideline for the development of future empirical studies and consequently NBS (Klaniecki et al., 2018). Archetypes of relatedness bring to the forefront the intrinsic need for connectedness to nature (Alves et al., 2021). Connectedness to nature may represent a cornerstone in communication strategies and interventions geared towards restoration and instoration in addition to the idea of risk and disease prevention. In that sense, instorative experiences gain a new force. Instead of being just the other side of the coin of restoration, they will reflect the larger goal of facilitating and enabling people to experience nature in its dynamic human and non-human complexity (Clayton et al., 2017). The idea of a deficit at the basis of restoration theory (for both attention restoration and psychophysiological stress) is not enough to accommodate the complexity of instorative experiences. Building the case for renewed attention to connectedness to nature, it can be claimed that if it is evolutionarily functional to affiliate with life and life-like processes, biophilia should be at the core of NBS to secure good QOL. Biophilia serves as an overarching framework for connectedness, NBS and a relational ethics of nature. Identifying affordances for connectedness and greater biophilia opens up opportunities to design NBS to discover, experiment and manage nature through a long-term, cyclical, transdisciplinary and more inclusive approach entailing both human and non-human beings.

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PART VI AGEING, PLACE AND QUALITY OF LIFE

21. The importance of place-making for quality of life in later life: contrasting home and hospital environments Hans-Werner Wahl, Julia Kirch, Kathrin Büter and Gesine Marquardt

INTRODUCTION The interchange between a person (P) and the environments (E) they encounter plays a fundamental role in the quality of life (QOL) of older adults (Rowe and Kahn, 2015), including those with dementia-related disorders (Cohen and Weisman, 1991). However, environmental perspectives largely ignore the role of contextual influences. Wahl and Gitlin (2019) argue that the environment plays a complex and multifaceted role in models of successful ageing, serving as cause, moderator, mediator, as well as outcome. Theories and empirical findings are frequently discussed under the term environmental gerontology (Lawton, 1982; Wahl et al., 2012; Wahl and Oswald, 2016). This chapter focuses on two key environments in which older adults typically spend more time compared to younger and middle-aged adults – namely, the home and hospital environments. This chapter does not focus on retirement communities (see Chapter 22) nor on the broader residential environment in which older adults are situated (for example, the neighbourhood; see Chapters 23 and 24), but rather focuses on the internal aspects of the home and hospital environments. The home environment is one of the ‘classic’ environments addressed in environmental gerontology (Lawton and Nahemow, 1973; Oswald et al., 2007, 2011; Oswald and Wahl, 2005; Wahl and Oswald, 2010, 2016) and by architects (Kirch et al., 2018; Regnier, 2018), geographers and anthropologists (Golant, 2015; Harper and Laws, 1995; Rowles, 1983; Rubinstein, 1989), as well as combinations of multiple social science disciplines (Oswald and Rowles, 2007; Wahl and Marquardt, 2019; Wahl and Weisman, 2003). An outcome of this literature is the term ageing in place, widely used to indicate the importance of home environments for older adults’ QOL, reflecting their need to stay put if it is the ‘right’ place for their needs (Golant, 2015; Vasunilashorn et al., 2012). But for many reasons, hospital environments are of increasing importance for older adults, including the need for acute care (Bickel et al., 2018). They may suffer from comorbid cognitive impairment and dementia, which may increase the length of hospital stays and lead to functional decline, higher complication rates and mortality (Moellers et al., 2019; Mukadam and Sampson, 2011; Van Rensbergen et al., 2006). Marquardt (2019) argues that research in environmental gerontology needs greater emphasis in studying such environments. In focusing on both the home and hospital environments in tandem, the chapter strives to counteract the common habit in the literature to separate these environments, which ignores that they both are closely interrelated in the QOL of older adults, especially in making the transition from early old age to advanced old age and eventually terminal stages of life (Diehl 323

324  Handbook of quality of life research and Wahl, 2020). We do this as a joint effort between behavioural and architectural science, which we see as a key requirement to come to terms with the conceptual, empirical and applied challenges. The chapter first outlines the common principles of environmental gerontology relevant for behavioural and architectural sciences. We then introduce major conceptualisations for the understanding of person (P) and environment (E) systems in later life and present exemplary empirical findings. That is followed by a systematic comparison of the home and hospital environments, outlining practical implications.

PRINCIPLES OF ENVIRONMENTAL GERONTOLOGY RELEVANT FOR BEHAVIOURAL AND ARCHITECTURAL SCIENCE FOCUSING ON OLDER ADULTS Research on ageing rests on numerous general principles acknowledged in geropsychology and geroarchitecture. We elaborate on three: ● First, environments are seen rather holistically, encompassing the built and social environment, including purpose-built housing such as assisted living facilities (Regnier, 2018), long-term care solutions, naturally occurring care environments and special care units for older adults with dementia-related disorders. ● Second, it is assumed that P and E are interwoven and in a constant transaction. Think of an older person – Alex – living in the third level of a townhouse without a lift, with a strong preference to walk outside every day. Alex is bothered by severe gait impairment and fluctuating bodily weakness. On some days, being in good bodily condition, he can rather easily exert personal preferences and takes the steps down to enjoy the park area in close proximity. At other times, being in not so good bodily condition, the staircase becomes an inaccessible barrier and Alex’s strong desire to go outside on a sunny day cannot be fulfilled, eventually requiring compensatory efforts to maintain well-being, such as watching more TV or making longer phone calls. ● Third, an additional helpful organisational scheme for P–E analysis in ageing science comes from Bronfenbrenner’s (1979, 1999) now classic bio-ecological model, which differentiates between: ● the microsystem (the interpersonal interactions within the immediate environment); ● the mesosystem (two or more microsystems directly impacting the developing individual); ● the exosystem (linkages between subsystems that indirectly influence the individual); and ● the macrosystem (values, norms and legislation of a given society). Bronfenbrenner also considered the role of flow of ontogenetic and historical time in what he called the chronosystem (see also Wahl and Gerstorf, 2018). Returning to Alex’s case, the care-giving relative lives with their family about 20 minutes away – therefore two microsystems form a mesosystem together. When the care-giving relative is able to come over, that person can assist Alex, thus making outdoor activities possible also during ‘bad days’. Hence, the mesosystem may additionally support outdoor activities even at times when gait impairment and overall bodily condition would undermine Alex’s

The importance of place-making for quality of life in later life  325 preferences. Being part of an exosystem for Alex’s micro- and mesosystem on Wednesdays, the community invites senior citizens to attend an activity specially designed for older adults, including a pick-up service for those in need. Therefore, going out on Wednesday is mostly facilitated by the community exosystem. This is made possible by a government programme that offers communities financial support for providing senior service programmes, which is an indirectly important macrosystem for Alex. Finally, time is a key factor in Bronfenbrenner’s model, both ontogenetically and historically. COVID-19 is an example of a historical occurrence, which may have forced the community to shut down their senior programmes, making Wednesdays different for Alex. Furthermore, various other life events may impact the individual’s so far well-balanced bio-ecological model; for example, if Alex develops a health condition that requires immediate medical attention, his world may expand in unexpected ways: Alex may pass through an emergency room, several examination rooms and stay in a hospital ward. While the encountered spaces in the hospital environment may fulfil all Alex’s functional needs (for example, no steps or staircases to navigate), to Alex, these spaces may at the same time have no meaning as a place to live. In this case, Alex will be unable to connect and apply strategies of place-making from his home to the hospital environment. Thus, Alex may become disoriented, either agitated or apathetic, and deteriorate in ability to perform activities of daily living. This may then require further environmental transitions for Alex, such as to a nursing care environment.

CONCEPTUAL MODELS OF AGEING IN HOME AND HOSPITAL ENVIRONMENTS: BEHAVIOURAL AND ARCHITECTURAL PERSPECTIVES Conceptualising Home Environments Although originally developed in an institutional context, the docility hypothesis (Lawton and Simon, 1968) is widely applied in the ageing and home research and practice area (Wahl and Gerstorf, 2020). It contends that the less competent and resourceful a person is, the greater the impact of home environmental factors. Consequently, a given objective home context characteristic affects individual outcomes disproportionately more at low levels of individual competencies and resources than at high levels (Lawton and Nahemow, 1973). Kahana (1982) and Carp and Carp (1984), in discussing the docility hypothesis, argue that P–E interchange is driven predominately by objective characteristics of the ageing person (for example, objectively measurable motor or cognitive functioning) and its context (for example, environmental hazards, accessibility). They also considered concepts such as environmental preferences and personally meaningful life goals. It is assumed that if home environments no longer fit with a person’s needs and preferences, the long-term outcome may be lower QOL and reduced likelihood of ageing successfully. This dependence on the environment is particularly increased in the case of advanced cognitive decline. Therefore, people with dementia are more sensitive to their surroundings, and as a result, more dependent on their environment. However, a dementia-friendly design, which is adapted to the specific needs and abilities of the inhabitants, can support functional abilities and contribute to improved QOL (Marquardt et al., 2014).

326  Handbook of quality of life research Wahl and Oswald (2010, 2016) strived to integrate objective space-like and subjective place-like elements of environments in the tandem concepts of P–E belonging and P–E agency (Figure 21.1) with a focus on home environments. Processes of P–E belonging account for the experiential and personal meaning part of P–E interchange as people age in home environments, while processes of P–E agency build on goal-directed P–E cognitions and behaviours such as adapting one’s home environment to evolving needs. That is, P–E belonging contains feelings of being positively attached and bound to the home environment, together with the creation of place identity. In contrast, processes of P–E agency deal with the exertion of physical environment-related cognitions such as actively using one’s environment and implementing compensatory means (for example, removing barriers and instalment of a lift). Both processes of P–E belonging and agency are especially important for older adults experiencing a decline in functional capacity. This is because the immediate environment may become the only context for maintaining personal meaning as well as acting efficiently. Furthermore, both P–E belonging and agency are assumed to be positively linked to major developmental outcomes important for maintaining QOL such as autonomy, well-being and identity.

Source: After Wahl and Oswald (2016, p. 629).

Figure 21.1

Model of person–environment (P–E) interchange by Wahl and Oswald

Conceptualising Hospital Environments A key concept relevant for hospital environments and introduced by Barker (1968) is behaviour setting. It suggests that constant behavioural patterns may be observed in certain spatial environments independently of the person occupying or inhabiting those spaces. The spatial context generates culturally and socially shaped behaviours and actions that are learned as

The importance of place-making for quality of life in later life  327 ‘environment-appropriate’ behaviour. Furthermore, spatial properties of the environment – such as furnishing – can stimulate or prevent certain behaviour patterns. The theory of affordances (Gibson, 1979) helps us understand the concept of behaviour setting. It assumes that objects cause a resonance in the user, inducing specific actions. Depending on the individual experience and their current needs, people’s behaviour is adjusted accordingly to the observed object. An environment that is easy to interpret can stimulate learned behaviours and support people using them appropriately, which may be true especially for people with dementia. Thus, architectural design needs to consider the personal experiences and expectations of the people that will use a purpose-built environment. Two important levels of architectural design to address are: (1) the spatial design of the floor plan; and (2) the interior design. In understanding the connection between floor plan design and behaviour, it is important to consider the role of learned behaviours from environments that a person has inhabited in the past – that is, ‘space biography’. For instance, after transitioning to a nursing home, an older person’s personal space is reduced to a single room, which may include an ensuite bathroom. If the learned spatial behaviour leads to the expectation that to get to the bathroom it is necessary to walk down a hallway, nursing home residents may not find the ensuite bathroom but look for it outside their individual room. On the level of interior design, connections to past spatial experiences include the use of fixtures and fittings, furnishings, and the choice of colours. Hospital environments are a neglected setting in the environmental gerontology literature, especially for research on older adults with dementia (Bickel et al., 2018). Such patients are at particular risk of functional decline, increased disorientation, muscle atrophy during their hospital stay and restlessness. They frequently run the risk of not being discharged back home and instead need to make a transition to a long-term care facility. Finding ways to help patients cope with their new hospital environment as well as ways to sustain their functional health and self-care ability is, therefore, an important treatment goal in hospitals. Multilevel treatment approaches have been implemented, including adaptations of architectural design (Fleming et al., 2020; Zieschang et al., 2019). A dementia-friendly architecture includes aspects that compensate for age and dementia-related limitations. Thus, spaces need to offer positive sensory experiences. Also important is ensuring that experiences are positive and enjoyable, such as feeling intimacy, privacy and calmness (Chaudhury et al., 2018; Day et al., 2000; Marquardt et al., 2014). Such findings on dementia-friendly design overwhelmingly stem largely from the field of long-term care environments and have only recently emerged in designing hospital environments. Older Adults Interacting with the Home and Hospital Environment: Some Established and Emerging Empirical Findings Research findings on how the home and hospital environment shapes the QOL of older adults are now presented. Older adults and the role of the home environment The role of the home environment received early attention in environmental psychology (Altman et al., 1984). Private home environments provide an important ecology for older adults because they tend to live in the same place for a long time. This evokes rich cognitive and affective ties to the place in which one lives, those ties being felt not only to the home

328  Handbook of quality of life research itself, but also to the wider environment – the neighbourhood, community life, landscape characteristics, cultural habits, and more. This complex melange unfolding in place-related cognitive–emotional experience is captured with the German term Heimat (homeland). This refers to place identity and place attachment, which gets at the very specific idea of ‘feeling at home’ (Oswald and Wahl, 2005). The meaning of home found early interest among North American qualitative environmental gerontologists – for example, the importance of ‘surveillance zones’ for older adults allowing one to maintain feelings of being in control of one’s environment even in vulnerable life circumstances (Rowles, 1983); and the role of cherished objects and photographs in the home, and how these may represent artefacts closely linked with biographical events of major importance (Rubinstein, 1989). Qualitative research by Oswald and Wahl (2005) with 126 older adults aged 61 to 92 found that one-third were in good health, one-third suffered from severe mobility impairment and one-third were blind. The following meaning categories were established with satisfying reliability (Cohen’s kappa: 0.77–0.83): ● physical, focusing on the experience of housing conditions such as the experience of the residential area and furnishings; ● behavioural, related to everyday behaviour in the home and to ways of rearranging items within; ● cognitive, representing biographical bonding to the home, such as the experience of familiarity and insideness; ● emotional, expressing the experience of privacy, safety, pleasure and stimulation; and ● social, expressing relationships with fellow lodgers, neighbours or visitors. Concerning group differences: ● healthy participants were more appreciative of the physical location, access and amenity aspects of the home; and ● impaired participants emphasised the cognitive and biographical significance of the home. These findings are in line with our conceptual expectation of linkages between ageing and P–E belonging, while P–E agency is assumed to decrease with age because functional limitations such as mobility or vision loss are strongly age-driven occurrences. Concerning behavioural and social aspects, blind participants concentrated more on their social and cognitive sphere and less on behavioural and physical aspects of the home, while the meaning patterns for the mobility-impaired participants included behavioural aspects to a greater extent. About the same share of statements was made with regard to emotional themes in all three groups. As argued in the Wahl and Oswald model (2010, 2016), processes of both P–E belonging and P–E agency go hand in hand as people age in their home environments. The interplay was investigated in the European ENABLE-AGE project (Iwarsson et al., 2007). It found that: ● the core components of healthy ageing were independence in daily activities and subjective well-being; ● regarding processes of belonging in the meaning of home, the perceived usability of one’s home and the concept of residential satisfaction were important; and

The importance of place-making for quality of life in later life  329 ● in terms of P–E agency, the fit processes were considered via matching existing functional limitations with existing environmental barriers, resulting in a total accessibility score (Iwarsson, 2004). In addition, housing-related control beliefs (Oswald et al., 2003) were assessed. The findings underscore that older adults living in more accessible homes who perceive their home as meaningful and useful and who think that external influences are not responsible for their housing situation were more independent in daily activities and had a better sense of well-being. More details of the national samples and of the methods used are provided in Nygren et al. (2007) and Oswald et al. (2007). Older adults and the role of the hospital environment While older adults can get along well at home in familiar surroundings, once in a hospital they often react with fear and discomfort, struggling to find their way around this new environment. The many unfamiliar faces, missing relatives and unfamiliar or loud background noises leave them confused. These effects are especially prevalent among older patients with cognitive impairments, such as dementia (Dewing and Dijk, 2016; Moellers et al., 2019). Hospitals aim to provide efficient treatment of primarily somatic diseases but offer little in terms of meeting individuals’ holistic needs (Rohde, 1962). Standardised and performance-oriented care processes usually require fully oriented and communicative patients who understand treatment processes and can accompany them cooperatively. However, due to cognitive impairments, people with dementia may be unaware of their health status and the need for a hospital stay, and it can thus be difficult for them to understand the purpose of medical or therapeutic measures. As a result, they may not actively cooperate, even showing defensive behaviour (German Alzheimer’s Society, 2013). Thus, they may be perceived as a disruptive factor in the care routine. All these environmental factors lead to an increased risk of developing symptoms of acute disorientation (delirium), which is common with patients with dementia (Eckstein and Burkhardt, 2019; Inouye et al., 2014). Further, forced passivity in a hospital can reduce functioning, subsequently requiring temporary or even permanent transition to a long-term care facility (Joray, 2004; Mukadam and Sampson, 2011). A hospital stay can be a difficult situation to master for those with dementia and may be associated with a steep decline in abilities. Thus, staying active during a hospital stay is crucial for older patients with cognitive impairments to preserve their QOL, specifically their functional abilities, independence and psychological health. Activity is one of the five basic psychological needs of people with dementia (Kitwood, 2008, p. 124). Activity engagement among people with dementia can enhance positive emotions (Schreiner et al., 2005), reduce behavioural problems, lead to less use of psychotropic drugs (Volicer et al., 2006) and improve mobility (Brown et al., 2016; Scherder et al., 2010). Conversely, understimulation can lead to apathy, boredom, depression and loneliness (Samus et al., 2005). Meaningful activities for people with dementia include recreational activities, housework, social interactions and work-related activities (Phinney et al., 2007). Activities tailored to personal interests and skills are most successful and can reduce challenging behaviours (Trahan et al., 2014). Although residents in long-term care facilities can be easily engaged in daily activities such as helping with housework or preparing mealtimes, hospital staff state that their options to mobilise patients and engage them in meaningful activities are limited.

330  Handbook of quality of life research Furthermore, hospital environments are characterised by mostly standardised (room) structures and organisational processes, which focus on the medical treatment of patients due to acute health problems. They are typically designed for short lengths of stay, making it harder to tailor the hospital environment and activities to meet personal needs of older adults, especially those with dementia, who present as a heterogeneous patient group (age, gender, social and cultural background, etc.). For more than 30 years, research has investigated ways to make the architecture of care facilities more dementia-friendly, especially in nursing homes (Calkins, 2018; Chaudhury et al., 2018; Fleming et al., 2020). Architecture and design can positively influence patient symptoms and the progression of dementia and help the person feel at home in the care facility. Principles of dementia-friendly hospital design to improve the care experience of patients and hospital staff have been derived from evidence-based findings from the nursing home setting (Büter and Marquardt, 2020; Büter et al., 2017). Empirical Study 1: Exploring the Overall Relevance of the Built Hospital Environment for Older Adults with Dementia In Germany, a multicentre, retrospective cohort study has examined the overall effect of the built environment on dementia patients’ self-care abilities through dementia-friendly design characteristics. Spatial data of 25 dementia special care units in acute care hospitals were assessed, and the extent of dementia-friendly design measures evaluated. Additionally, retrospective routine data from geriatric patients with dementia were gathered. Patients’ improvement in self-care abilities was operationalised as a positive change in the Barthel Index – developed in 1965 – as an index of independence. It scores the ability of patients to take care of themselves in basic everyday functions from complete need for care to complete independence) (Mahoney and Barthel, 1965). The extent of implementation of dementia-friendly design measures was scored based on research by Büter et al. (2017) and a rating tool (King’s Fund, 2014). The main findings were that: ● almost all units demonstrated a high level of dementia-friendly design characteristics – for example, small unit sizes, differentiated common areas, visual guidance systems; ● patients showed general improvement on the Barthel Index in self-care abilities, but there were differences between hospitals; ● multilevel modelling revealed that dementia-friendly design measures were overall positively associated with an improvement in patients’ self-care abilities (Kirch and Marquardt, 2021); ● safe and protected space for mobility and clear sight lines were positively associated with improvement of self-care abilities; ● there was a positive relationship between patient outcome and flexibly furnished patient rooms, the existence of visual guidance systems, the appropriate use of light, colours, visual contrasts, barrier-free and ergonomic design of furniture, a homelike appearance; and ● surprisingly, as it contradicts findings of previous studies (Chard et al., 2009), temporal and situational cues were negatively associated with self-care abilities. The study showed the powerful effects of design criteria in the hospital setting on dementia patients, reinforcing the findings of other studies (Chaudhury et al., 2018). The only design criterion negatively associated with patients’ self-care abilities were temporal and situational

The importance of place-making for quality of life in later life  331 cues. Fleming and Purandare (2010) had earlier reported a lack of evidence for the beneficial effects of signage and visual cues. It can be assumed that an overload of temporal cues is responsible for the negative effects. Although the importance of maintaining self-care abilities is widely recognised and an important contributor to QOL, investigating the relationship between the hospital setting and maintenance of the self-care abilities of patients with dementia is an underexamined topic (Røsvik and Rokstad, 2020). The German study showed how architectural design can be an enabler of non-pharmacological care for dementia patients. With increasing longevity and a corresponding dementia epidemic, the entire hospital setting should be as supportive as possible. Developing inclusive concepts and implementing a holistic approach in dementia-friendly hospital design, instead of limiting these measures to separate areas in the hospital, is crucial. Architectural innovations are interdependent and cannot be treated singularly. To achieve this, well-designed studies are required to tease the multitude of overlapping aspects of the environment and assess them with greater certainty. Empirical Study 2: Physical Features in the Hospital Environment Promoting or Hindering Activity for Older Adults with Dementia Another German study has sought to identify physical features in the hospital environment that promote or hinder activity in patients with dementia. A pre- and post-occupancy evaluation was conducted in an internal medicine ward in a general acute care hospital. Baseline data (t1) were collected through patient observations (behavioural mapping) and interviews with ward staff and patients. Outcomes included type, frequency, duration and location of active behaviour among patients with dementia. After baseline (t1) data collection, a variety of changes were made to the environment. A centrally located activity zone was created in the hallway close to the nursing station. It consisted of a seating area and was enhanced with a variety of opportunities for activities such as reading material, a TV simulating an aquarium and headphones to listen to music and short stories. It was important to choose activities that considered the time and human resources of hospital staff. Further, new signage and colours to highlight doors were implemented in the hallway to improve spatial orientation. Nineteen patients with dementia were included in the observation. The activity and interaction levels were defined as the time patients spent engaged in activities and interactions in the public areas (hallway, common areas) of the ward. Findings show: (1) that both the activity and interaction levels of dementia patients significantly increased after implementation of the intervention; and (2) that patients spent more time outside of their rooms and were engaged longer and in a greater variety of activities than before the intervention. Another important finding was that the central location of the activity zone and its visual and spatial connectivity to the nursing station were essential to promote active behaviour and interaction among dementia patients. The new activity zone became a spatial anchor point on the ward, a highly recognisable space that was very meaningful for the patients, with the space integrating both social and design qualities. Its central location, unmistakable design and audible properties, together with the constant presence of other people – like the ward staff – resulted in an intensively used area with significant meaning where patients could orient themselves and where they enjoyed spending time. These findings are in line with studies from long-term care settings, which found that a centrally located nursing station can become a place of communication and serve as an anchor

332  Handbook of quality of life research point for orientation (Campo and Chaudhury, 2012; Passini et al., 2000). Brief interactions were increasingly observed in areas that were easily visible and accessible. However, to facilitate longer-lasting and more intense interactions among persons with dementia, spaces with more privacy (Ferdous and Moore, 2015) and an appropriate level of visual and acoustic stimulation are needed (Stanyon et al., 2016). Therefore, hospital environments should provide a variety of common spaces with different spatial qualities to help maintain dementia patients’ QOL.

HOMES, HOSPITALS AND OLDER ADULTS: A COMPARATIVE APPROACH Given the different phenomenological types of home and hospital and empirical findings, we now provide a systematic comparison of both ecologies with a focus on older adults as users of these environments. Table 21.1 depicts the outcome of such a comparison. The table shows the constructs of environmental press and P–E fit (# 1–6) come into play in both the home and the hospital environments, but in opposite ways. The descriptive architectural dimensions of both settings (# 7–8) are also the same; however, they require quite different architectural responses. In home environments, many supportive environmental elements do not need specific attention by architects and designers, while this is quite different for hospital environments. To ensure a smooth transition from (and return to) the home environment, not only the architectural design of functional spaces, but also the creation of places relevant and legible for older adults are necessary to help maintain QOL. This is particularly important given that both settings are highly contrasting in terms of social structures (# 9), which makes it even more difficult for patients to adapt to the new context, and as a result, for this transition to potentially have a negative impact on QOL.

PRACTICAL IMPLICATIONS AND OUTLOOK As different as the two environments of home and hospital may be, they share similar properties. Both are places where people reside for a considerable amount of time, even though the respective time frames are quite different. To optimise QOL, both environments need to be adapted to older adults’ spatial needs, providing spaces that are safe, barrier-free and easy to navigate. The corresponding architectural features are relatively easy to implement when guidelines applicable to both environments are available (for example, regulations such as British Standards, Americans with Disabilities Act, German DIN Standards). For older adults to be able to successfully navigate both environments, and transition between the two while maintaining QOL, employing evidence-based place-making strategies is essential. In the hospital context, these strategies need to build on individuals’ experiences in their home environment. This means that P–E fit in hospitals needs to consider older adults’ life experiences and especially for those with dementia. However, this is a field with many knowledge gaps. Design guidance is just emerging, mainly in the field of dementia-friendly design of acute care environments. In the future, it is hoped that hospitals will no longer be built under the main imperative of delivering functional spaces that optimise medical and organisational procedures, but rather emphasise patient QOL, which may in turn actually promote faster recovery

The importance of place-making for quality of life in later life  333 Table 21.1

Contrasting home and hospital environments being used by older adults

Domain/Content Area

Important Constructs

Home

Hospital

Environment

Environment

 

 

Environmental press for behaviour

Low

High

Person–environment fit

Low

High

(3) Space versus place characteristic (Rowles and

Space character dominant

Low

High

Watkins, 2003)

Place character dominant

High

Low

(4) P–E belonging and P–E agency (Wahl and

P–E belonging possible

High

Low

Oswald, 2010, 2016)

P–E agency possible

High

Low

(5) Behaviour setting driven behaviour vs

Behaviour setting driven (conform

Low

High

non-behaviour setting driven behaviour (Barker,

to expectations of others)

1968)

Not behaviour setting driven

High

Low

 Fundamental concepts important for environmental   gerontology and architectural planning/action (1) P–E competence-environmental press/docility (Lawton and Nahemow, 1973) (2) Person–environment fit (Carp and Carp, 1984; Kahana, 1982)

(correspond to own needs) (6) Concept of affordances (Gibson, 1979)

Object-induced activity behaviour

High

Low

Additional concepts important for architectural

 

 

 

(7) Generally accepted dimensions to quality P–E

Orientation and wayfinding

Low

High

interchange (Sloane et al., 2002)

Provision of safety and security

Low

High

Provision of privacy

Low

High

Provision of control and autonomy

Low

High

Exposure to stimulation (both

Low

High

High

High

Low

High

Low

High

response

positive and negative) Enhancement of socialisation and social interaction Ways for environmental personalisation Enjoying familiarity and homelikeness (8) Dementia-relevant attributes (Büter and

Architectural response necessary to:  

 

Marquardt, 2020)  

Floor plan structure

Low

High

 

Floor space requirements

Low

High

 

Safety

Low

High

 

Orientation

Low

High

 

Guidance and orientation systems

Low

High

 

Lighting

Low

High

 

Colours and contrasts

Low

High

 

Atmosphere

Low

High

 

Activation concepts

High

High

 

Stimulus densities

Low

High

334  Handbook of quality of life research Domain/Content Area (9) Social structure (Rohde, 1962)

Important Constructs Perform everyday tasks

Home

Hospital

Environment

Environment

High

Low

independently  

Role obligations

High

Low

 

Familiar social relations

High

Low

 

Focus on disease

Low

High

 

Hierarchical position

High

Low

 

Autonomy

High

Low

from acute conditions. Moreover, a healing environment needs places that incorporate the users’ experiences and QOL needs in all dimensions – physical, behavioural, cognitive, emotional and social – so that spaces can become places that provide opportunities to socialise, be active, reflect and heal. Future research may thus follow this reasoning: place-making is closely related to activities. In the home environment, there is a node where food is prepared and cooked, a node for where friends are entertained, another node for where laundry is folded, and so on. These activity nodes add up to a place with meaning, which is the home. These activity nodes are generally missing in hospitals. Patients are mostly expected to stay in their rooms, even in their beds. Activities are mostly limited to medical examinations and therapies, and, if a common room is provided, lunch and dinner with fellow patients. Hospitals need to allow for and encourage different activities that take place in several activity nodes and thus facilitate the emergence of a place with meaning. This may help to enable older adults to recognise and thus know how to navigate the spaces successfully. Implementing place-making strategies not only requires an adaption of spatial design, but also a change in medical and organisational procedures. An interdisciplinary planning process that includes all users, including patients, will help to translate future research findings into the architectural practice.

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The importance of place-making for quality of life in later life  335 Calkins, M.P. (2018), ‘From research to application: supportive and therapeutic environments for people living with dementia’, The Gerontologist, 58(Suppl_1), S114–S128. Campo, M. and Chaudhury, H. (2012), ‘Informal social interaction among residents with dementia in special care units: exploring the role of the physical and social environments’, Dementia, 11, 401–23. Carp, F.M. and Carp, A. (1984), ‘A complementary/congruence model of well-being or mental health for the community elderly’, in I. Altman, M.P. Lawton and J.F. Wohlwill (eds), Human Behavior and Environment: Elderly People and the Environment, Vol. 7, New York: Plenum Press, pp. 279–336. Chard, G., Liu, L. and Mulholland, S. (2009), ‘Verbal cueing and environmental modifications: strategies to improve engagement in occupations in persons with Alzheimer disease’, Physical and Occupational Therapy in Geriatrics, 27, 197–211. Chaudhury, H., Cooke, H.A., Cowie, H. and Razaghi, L. (2018), ‘The influence of the physical environment on residents with dementia in long-term care settings: a review of the empirical literature’, The Gerontologist, 58, e325–e337. Cohen, U. and Weisman, G.D. (1991), Holding On to Home: Designing Environments for People with Dementia, Baltimore, MD: Johns Hopkins University Press. Day, K., Carreon, D. and Stump, C. (2000), ‘The therapeutic design of environments for people with dementia: a review of the empirical research’, The Gerontologist, 40, 397–416. Dewing, J. and Dijk, S. (2016), ‘What is the current state of care for older people with dementia in general hospitals? A literature review’, Dementia, 15, 106–24. Diehl, M.K. and Wahl, H.-W. (2020), The Psychology of Later Life: A Contextual Perspective, Washington, DC: American Psychological Association Books. Eckstein, C. and Burkhardt, H. (2019), ‘Multicomponent, nonpharmacological delirium interventions for older inpatients: a scoping review’, Zeitschrift für Gerontologie und Geriatrie, 52(Suppl. 4), S229–S242. Ferdous, F. and Moore, K.D. (2015), ‘Field observations into the environmental soul: spatial configuration and social life for people experiencing dementia’, American Journal of Alzheimer’s Disease and Other Dementias, 30, 209–18. Fleming, R. and Purandare, N. (2010), ‘Long-term care for people with dementia: environmental design guidelines’, International Psychogeriatrics, 22, 1084–96. Fleming, R., Zeisel, J. and Bennett, K. (2020), World Alzheimer Report 2020 – Design Dignity Dementia: Dementia-related Design and the Built Environment, Volume 1, London: Alzheimer’s Disease International. German Alzheimer’s Society (2013), Menschen mit Demenz im Krankenhaus: Auf dem Weg zum demenzsensiblen Krankenhaus [People with Dementia in the General Hospital: Toward a Dementia-sensitive Hospital], Berlin: Deutsche Alzheimer Gesellschaft e.V. Gibson, J.J. (1979), The Ecological Approach to Visual Perception, Boston, MA: Houghton Mifflin Harcourt. Golant, S.M. (2015), ‘Residential normalcy and the enriched coping repertoires of successfully ageing older adults’, The Gerontologist, 55, 70–82. Harper, S. and Laws, G. (1995), ‘Rethinking the geography of ageing’, Progress in Human Geography, 19, 199–221. Inouye, S.K., Westendorp, R.G. and Saczynski, J.S. (2014), ‘Delirium in elderly people’, The Lancet, 383, 911–22. Iwarsson, S. (2004), ‘Assessing the fit between older people and their home environments – an occupational therapy research perspective’, in H.-W. Wahl, R. Scheidt and P. Windley (eds), Annual Review of Gerontology and Geriatrics (Focus on Aging in Context: Socio-Physical Environments, Volume 23), New York: Springer, pp. 85–109. Iwarsson, S., Wahl, H.-W. and Nygren, C. et al. (2007), ‘Importance of the home environment for healthy ageing: conceptual and methodological background of the ENABLE-AGE project’, The Gerontologist, 47, 78–84. Joray, S. (2004), ‘Cognitive impairment in elderly medical inpatients: detection and associated six-month outcomes’, American Journal of Geriatric Psychiatry, 12, 639–47. Kahana, E. (1982), ‘A congruence model of person–environment interaction’, in M.P. Lawton, P.G. Windley and T.O. Byerts (eds), Aging and the Environment: Theoretical Approaches, New York: Springer, pp. 97–121.

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The importance of place-making for quality of life in later life  337 Røsvik, J. and Rokstad, A.M.M. (2020), ‘What are the needs of people with dementia in acute hospital settings, and what interventions are made to meet these needs? A systematic integrative review of the literature’, BMC Health Services Research, 20, Article 723. Rowe, J.W. and Kahn, R.L. (2015), ‘Successful aging 2.0: conceptual expansions for the 21st century’, The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70, 593–6. Rowles, G.D. (1983), ‘Geographical dimensions of social support in rural Appalachia’, in G.D. Rowles and R.J. Ohta (eds), Aging and Milieu: Environmental Perspectives on Growing Old, New York: Academic Press, pp. 111–29. Rowles, G.D. and Watkins, J.F. (2003), ‘History, habit, heart and hearth: on making spaces into places’, in K.W. Schaie, H.-W. Wahl, H. Mollenkopf and F. Oswald (eds), Aging Independently: Living Arrangements and Mobility, New York: Springer, pp. 77–96. Rubinstein, R.L. (1989), ‘The home environments of older people: a description of the psychological process linking person to place’, Journal of Gerontology, 44, 45–53. Samus, Q.M., Rosenblatt, A. and Steele, C. et al. (2005), ‘The association of neuropsychiatric symptoms and environment with quality of life in assisted living residents with dementia’, The Gerontologist, 45(Suppl. 1), S9–S26. Scherder, E.J., Bogen, T. and Eggermont, L.H. et al. (2010), ‘The more physical inactivity, the more agitation in dementia’, International Psychogeriatrics, 22, 1203–8. Schreiner, A.S., Yamamoto, E. and Shiotani, H. (2005), ‘Positive affect among nursing home residents with Alzheimer’s dementia: the effect of recreational activity’, Aging and Mental Health, 9, 129–34. Sloane, P.J., Mitchell, M.C. and Weisman, G. et al. (2002), ‘The Therapeutic Environment Screening Survey for Nursing Homes (TESS-NH): an observational instrument for assessing the physical environment of institutional settings for persons with dementia’, The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences, 57, S69–S78. Stanyon, M.R., Griffiths, A., Thomas, S.A. and Gordon, A.L. (2016), ‘The facilitators of communication with people with dementia in a care setting: an interview study with healthcare workers’, Age and Ageing, 45, 164–70. Trahan, M.A., Kuo, J., Carlson, M.C. and Gitlin, L.N. (2014), ‘A systematic review of strategies to foster activity engagement in persons with dementia’, Health Education and Behavior, 41, 70S–83S. Van Rensbergen, G., Nawrot, T.S. and Van Hecke, E. (2006), ‘Where do the elderly die? The impact of nursing home utilisation on the place of death: observations from a mortality cohort study in Flanders’, BMC Public Health, 6, Article 178. Vasunilashorn, S., Steinman, B.A., Liebig, P.S. and Pynoos, J. (2012), ‘Aging in place: evolution of a research topic whose time has come’, Journal of Aging Research, Article 120952. Volicer, L., Simard, J. and Pupa, J.H. et al. (2006), ‘Effects of continuous activity programming on behavioral symptoms of dementia’, Journal of the American Medical Directors Association, 7, 426–31. Wahl, H.-W. and Gerstorf, D. (2018), ‘A conceptual framework for studying Context Dynamics in Aging (CODA)’, Developmental Review, 50, 155–76. Wahl, H.-W. and Gerstorf, D. (2020), ‘Person–environment resources for aging well: environmental docility hypothesis and life space as conceptual pillars for future contextual gerontology’, The Gerontologist, 60, 368–75. Wahl, H.-W. and Gitlin, L.N. (2019), ‘Linking the socio-physical environment to successful aging: from basic research to intervention to implementation science considerations’, in R. Fernandez-Ballesteros, J.-M. Robine and A. Benetos (eds), The Cambridge Handbook of Successful Aging, Cambridge, UK: Cambridge University Press, pp. 570–93. Wahl, H.-W., Iwarsson, S. and Oswald, F. (2012), ‘Aging well and the environment: toward an integrative model and research agenda for the future’, The Gerontologist, 52, 306–16. Wahl, H.-W. and Marquardt, G. (2019), ‘Spaces, places, and long-term care in (Northern) Europe and the U.S.: can we do better? Book review essay on Regnier, V. (2018). Housing Design for an Increasingly Older Population. Redefining Assisted Living for the Mentally and Physically Frail. New York: Wiley’, The Gerontologist, 59, 596–7. Wahl, H.-W. and Oswald, F. (2010), ‘Environmental perspectives on aging’, in D. Dannefer and C. Phillipson (eds), The SAGE Handbook of Social Gerontology, London: SAGE, pp. 111–24.

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22. Quality of life of older adults in continuing care retirement communities Liat Ayalon

INTRODUCTION Continuing care retirement communities (CCRCs) represent a unique living environment available to mostly affluent, functionally independent (at least upon entering the setting) older people. They are designed to enhance older people’s ability to live independently and maintain quality of life (QOL). Thus, it is important to identify factors that contribute to or hinder QOL within that setting. The chapter identifies the unique features of CCRCs that contribute to or hinder residents’ QOL, providing an overview of current knowledge concerning the QOL of older adults. Findings of research on QOL in CCRCs (particularly in Israel) are presented, highlighting (1) the role of the physical environment – for example, location and design features; (2) the social environment, focusing on intimate social relations and loneliness; and (3) the care environment, discussed in relation to variations across models of care in promoting QOL. Institutions catering for older people range from traditional long-term care models that allow for limited autonomy, to person-centred care institutions that empower people to take care of their own health. CCRCs are closer to the latter model, allowing residents to choose to use all, some or none of the services offered. The measurement of QOL among older CCRC residents is discussed in relation to the physical, social and care environments. The chapter concludes with recommendations for future research and practice implications.

QUALITY OF LIFE OF OLDER ADULTS Several processes take place in the second half of life that impact QOL of older adults. This is because physical, social, emotional and environmental changes occur over time, with manifestations in the second half of life. A significant proportion of older adults suffer from chronic medical illness and impaired physical functioning (Hank, 2011; McLaughlin et al., 2010). Even though longevity has rapidly and steadily increased, the gap between the number of healthy life years and the average lifespan remains somewhat steadier. As a result, older adults are expected to spend a substantial number of years towards the end of their lives in poor or deteriorating health (Marteau et al., 2019). A recent systematic review examining QOL among older adults identifies current health status and health changes as important components of QOL (Van Leeuwen et al., 2019). Specifically, older adults’ sense of feeling healthy is an essential component of their QOL. Often, older adults compare their health to others of the same age in order to infer how they themselves are doing. Health is also important because it often determines many other aspects of older adults’ lives, including what they can or cannot do (ibid.). 339

340  Handbook of quality of life research Socially, older adults’ lives may change as some older adults lose family members and friends to illness or death. Others may retire, thus losing their daily contact with colleagues (Böger and Huxhold, 2018). Impaired physical functionality – including hearing or vision limitations – may also hamper older adults’ ability to interact with others in their social environment. Those changes may contribute to the social isolation of older adults (Ayalon et al., 2012). It is important to distinguish between loneliness and aloneness. Even though older adults might be physically or socially isolated, they may not necessarily report feeling lonely, which is the subjective experience of not having adequate social relations compared with one’s ideals or desired social ties (Shiovitz-Ezra and Leitsch, 2010). A review of loneliness over the lifespan shows that relative to middle-aged individuals, both younger and older individuals are more likely to report being lonely (Pinquart and Sorensen, 2001). It is not surprising that social relations play a major role in the QOL of older adults. Close, personal relationships, which consist of love, affection and reciprocity, comprise the relationship dimension of QOL (Van Leeuwen et al., 2019). Compared to younger people, older adults are more emotionally positive (Carstensen, 1992). For example, they are more likely to report moderate emotions rather than strong, negative ones. Also, older adults are more likely to recall positive rather than negative events (Charles et al., 2003). Additionally, they are more likely to rely on strong intimate ties, rather than on superficial ties (Carstensen et al., 1999). The opportunity to continue contributing to society and remain part of the social fabric are also important contributors to many older adults’ QOL (Gonzales et al., 2015). Related to this, maintaining a sense of value and enjoyment in life has been identified as another dimension that comprises older adults’ QOL. That includes ability to stay busy, engage in valuable activities, contribute to society, and make a difference in their lives and the lives of loved ones (Van Leeuwen et al., 2019).

QOL IN CCRCs The transition to a CCRC can be challenging for some older adults due to the fact that living in one’s own home, rather than in an institution, is considered an important component of QOL in old age (Van Leeuwen et al., 2019). Although the CCRC is defined as a community setting, it can have many features that make it an institution, including its physical structure, the presence of staff and availability of in-house health, social and recreational services. A study examining the way residents and their adult children view the CCRC found that both residents and their adult children struggle with the definition of the CCRC as a ‘community setting’ (Ayalon and Green, 2012). Very few called the CCRC a ‘home’. Instead, terms such as ‘hotel’, a ‘golden cage’ or a ‘kibbutz’ were used to describe the CCRC. Those terms reflect how both older adults and their adult children are quite ambivalent about the setting and struggle to view it as their home (ibid.). Even though many CCRCs have the term ‘home’ in their name, they are still viewed as institutions. As a result, this setting may not fulfil older adults’ wish to stay in their home for as long as possible (ibid.), which is referred to as ‘ageing in place’.

Quality of life of older adults in continuing care retirement communities  341 Nonetheless, as older people’s ability to control their environment declines and they become confined to their home, a CCRC may represent a valuable option, making both social life and services more accessible (Ayalon and Greed, 2016). Types of Migration Patterns to a CCRC The move to a CCRC can involve three different migration patterns: ● the first is ‘amenity migration’, when older adults move to a CCRC to enjoy the facilities and social services it offers; ● the second type occurs at a later age, when older adults transition to be closer to their family members as they may need to increasingly rely on the support that the latter may provide; and ● the third type of migration may involve a move to a long-term care setting and occurs when care needs increase and there is a motivation not to burden family members (Litwak and Longino, 1987). All three types can occur within the CCRC setting. The flexible features of the CCRC can meet the residents’ needs and preferences as they evolve (Krout et al., 2002). Specifically, the CCRC is a residential alternative available to older adults who are independent upon entering the setting. It is thus expected that over time, many older adults will develop physical impairments and poor health, which prevent them from being fully independent. As such, the setting may allow for different levels of care: starting from the independent unit, which may offer meal services, healthcare services and social activities upon request; followed by the assisted living facility or the nursing unit for those who require more substantial levels of care (Ayalon, 2016). Autonomy An important dimension of QOL for older adults is autonomy. It is linked with motivation to remain independent for as long as possible, not to be a burden on family members and loved ones, and maintain dignity as one grows older (Van Leeuwen et al., 2019). Indeed, the transition into a CCRC has been characterised as a wish to maintain independence and not to burden one’s adult children (like the third migration type). Often, older adults make the transition to the CCRC when they sense a change in their health status. The transition is made with the understanding that future health changes may be imminent (Ayalon, 2016; Krout et al., 2002). Supposedly, the CCRC represents an opportunity for autonomy and dignity, as it follows a residential model that allows residents to choose their own services and the level of support they require. Having more choices – especially in fulfilling one’s most basic needs – results in better QOL among older adults (Duncan-Myers and Huebner, 2000). Nonetheless, the level of autonomy available to older CCRC residents is directly related to the level of care required. In other words, those who are independent in their activities of daily living enjoy the highest levels of autonomy, while those who require substantial assistance end up losing their autonomy, becoming restricted not only physically, but also in terms of their physical and social surroundings (Shippee, 2009).

342  Handbook of quality of life research The Physical Environment in the CCRC and Older Adults’ QOL The physical environment plays a major role in determining QOL, as the interaction between the person and the environment is dynamic and reciprocal (Lawton, 1991). According to Lawton (1991), the ‘person–environment fit’ is of utmost importance. It can potentially improve people’s QOL, but it may hamper it. The transition to a CCRC could thus represent an attempt to improve the person–environment fit and as a result improve one’s QOL. This is because the CCRC provides services at different levels of care needs in order to flexibly adjust to the residents’ changing health, cognitive, physical and social functioning (Ayalon, 2016). Social Interactions As older adults’ physical abilities decline, their social interactions may become confined to their home and the nearby environment. This surrounding area is called the surveillance zone (Rowles, 1981). It is an area that is close enough to the home to allow older adults to take part in the social fabric, mainly vicariously through observation. CCRCs are designed to address the shrinking life space often experienced in later life. Specifically, they are built to facilitate easy navigation to meet residents’ social and health needs. Research shows that the only consistent predictor of intimate ties between CCRC residents is their proximal physical location (Ayalon and Yahav, 2019). Those older adults who shared a floor were significantly more likely to develop intimate ties. All other demographic and social characteristics were inconsistently related to the formation of social ties. This clearly demonstrates the importance of the physical environment within the CCRC to the social lives of older adults. A Successful Social Space Attribute Model and the Importance of Design To understand the role that the physical environment plays in the lives of older adults, Campbell (2015) has proposed the Successful Social Space Attribute Model. It suggests there are three tiers by which older adults’ needs are defined and addressed within the CCRC (Campbell, 2015). This classification follows Maslow’s ‘hierarchy of needs’, starting from the lowest level – which reflects physiological or foundational needs – and moving upward to higher-level needs (Maslow, 1943, 1954). Physiological needs Physiological needs include the need for mobility both within and outside the CCRC as well as the need to accommodate older adults’ sensory and physical declines. Lighting, acoustics and transferability can make an area within the setting more or less attractive in helping to meet those needs. Additionally, the inclusion of grab-bars or no-step entrances can assist in the prevention of falls and in improving older adults’ mobility within the setting. In contrast, the environment becomes less preferred if it does not meet residents’ physiological needs. Meeting older adults’ physiological needs is not all that is required from the physical environment. Once basic needs are met, higher-level needs also need be addressed (Campbell, 2015).

Quality of life of older adults in continuing care retirement communities  343 Privacy and security The second tier consists of security needs, which reflect the privacy one feels in a space and a preference for social interaction within vs outside the CCRC. When residents view their setting as highly secure, security measures, such as locking their door, may be seen as unnecessary. Additionally, the physical setting should provide residents with spaces and opportunities to socialise with other residents, and provide spaces within which they can maintain their privacy (Graham and Tuffin, 2004). Having a home that provides privacy and comfort is identified as a major factor that contributes to older adults’ QOL. Feeling more safe and secure in one’s home and neighbourhood and being able to access goods and services within one’s neighbourhood are additional important considerations (Van Leeuwen et al., 2019). Consistently, a sense of safety and security has been described as a push/pull factor that leads people to relocate to a CCRC (Krout et al., 2002). Research shows older adults report a heightened sense of insecurity in their living environment (Ayalon, 2016). For example, they may have experienced burglaries and vandalism in their living environment. Once they transitioned to a CCRC, residents reported a sense of safety that they were lacking in their previous home environment. Their children too reported a growing sense of satisfaction with the fact that their parents were safe and secure in their new environment, with a lobby and a person monitoring the entrance (ibid.). Belonging The top tier of needs concerns belonging, which is highly affected by the length of time one spends in the CCRC as well as by the characteristics of the residents and the physical design of the setting (Campbell, 2015). In a CCRC, older adults who live closer to the central activity building, those who live physically closer to their neighbours and those who share an outdoor garden space have been found to report a stronger sense of belonging to the CCRC (Sugihara and Evans, 2000). The design of the setting is also associated with one’s sense of belonging. The importance of meeting older adults’ tastes and preferences, rather than simply conveying a generic sense of homeliness, has been stressed. It has been argued that there are no universal indicators of ‘homeliness’ and it is not enough to make settings home-like. Instead, it is important to personalise the setting according to the unique preferences of the residents. The personalisation of one’s residence and meeting residents’ aesthetic preferences are prominent predictors of residents’ satisfaction with their living environment, an important component of QOL (Eshelman and Evans, 2002). In addition to the physical design of the CCRC communal areas, the living units within the CCRC also require consideration. Although CCRCs substantially differ from one another, they often share one characteristic – namely, the small size of the living units. Even if CCRCs offer larger units, the cost of these units is usually substantial, and they are quite scarce. Hence, when people transition to a CCRC they usually need to downsize and dispose of a substantial portion of their possessions (Green and Ayalon, 2019). The transition to a CCRC should be viewed not only from the prism of place attachment, but also from the perspective of material attachment (Cookman, 1996). Older adults who transition to a CCRC may have to separate not only from their living environment and friends, but also from their material possessions, which likely have been accumulated over many years. Possessions – such as photos or souvenirs – can serve as a connection between the past and the present. They also may be a bridge to the future, as in the case of older adults who acquire new

344  Handbook of quality of life research furniture in preparation for the move. The most intimate physical environment of the living unit and the possessions that older adults bring with them or acquire during the transition are important determinants of older adults’ QOL (Green and Ayalon, 2019). The Social Environment in the CCRC and Older Adults’ QOL A major precipitator for the transition to a CCRC concerns changes in people’s social lives. Research shows that, often, older adults consider the transition to a CCRC following the death of a partner. Hence, it is a social transition that motivates a move to a CCRC. As the CCRC offers a variety of social activities, it is viewed as a setting designed to alleviate loneliness, foster the development of new friendships and encourage social relations (Ayalon, 2018a; Krout et al., 2002). A study examining older adults’ adjustment to the CCRC shows many residents ‘found’ themselves socially and emotionally upon entering the CCRC. Specifically, a substantial number of older adults reported that their lives changed for the better. They established new social relations, embraced old ties and engaged in social activities they did not pursue in the past. Others reported limited changes in their lives following the transition, and only a small minority reported a decline in their overall social well-being following the transition (Ayalon and Greed, 2016). The small number of dissatisfied residents possibly reflects the fact that older adults in CCRCs are those who have come to terms with the choice to transition to a CCRC. Overall, older adults report higher levels of social support and reduced levels of loneliness once they transition to a CCRC (Ayalon, 2018a; Ayalon and Greed, 2016). Moreover, compared with older adults who attend adult day care centres for several hours a few times per week, older adults in CCRCs report lower levels of loneliness (Ayalon, 2018a; Ayalon et al., 2018). This suggests that a CCRC may meet older adults’ social needs more adequately than community resources. However, research in Israel found that compared to older adults in the community, older adults in CCRCs did not report reduced levels of loneliness (Ayalon, 2018a). Moreover, for some older people, the CCRC represents a highly socially controlled place, in which gossip flows easily and behaviours are restricted to meet the CCRC’s norms (Ayalon, 2018b; Koren and Ayalon, 2019). Hence, the role of CCRCs in alleviating loneliness and improving social interactions among older adults is still equivocal and may be influenced by other factors. According to Lawton (1991), one’s QOL is thought to be changing along the continuum of time, with the past having a direct impact on the individual’s present and future perceptions of QOL. Research shows that time also plays a role in the creation of friendships, so that the most meaningful social relations are described as those that occurred in the past, prior to the transition to the CCRC. Older adults view past relationships as stronger and more intimate than relationships created in the present in the CCRC. In addition, a spatial division is also employed (Ayalon and Green, 2013). Relationships that took place in the community or in one’s room are viewed as more intimate than relationships confined to the CCRC lobby. Hence, the lobby is portrayed as a non-intimate place that allows for the establishment of superficial relations rather than intimate ones (ibid.). Several studies examining social relations in CCRCs take account of the entire social environment rather than the characteristics of the individual residents. It was shown that those who have more central roles in the CCRC also enjoy better health. As such, health was portrayed as

Quality of life of older adults in continuing care retirement communities  345 an asset in CCRCs, possibly because of the fact that with age, health becomes a scarce resource (Schafer, 2013). Another study found a connection between the physical environment of the CCRC and one’s social network by identifying intimate relations among those who shared the same residential floor. The study suggested that demographic characteristics, health status or social status were of limited relevance compared with geographic proximity, which played a major role in determining individuals’ most intimate ties (Ayalon and Yahav, 2019). Another approach to understanding the social environment in CCRCs has been to examine individuals’ sense of community belonging. The term community may carry multiple meanings, including a psychological, a social or a spatial/geographical context. Research in the UK found that while some older people report a strong sense of community in the CCRC, others questioned its existence. The spatial clustering of residents based on their tenure resulted in residents befriending those of similar number of years in the CCRC (Evans, 2009). Another study found sense of community belonging to be associated with one’s number of social ties and degree of constraints (for example, being socially invested in a single group of interconnected ties) within the network (Ayalon, 2020). The Care Environment in the CCRC and Older Adults’ QOL The total institution Goffman (1958) describes the ‘total institution’ as a place with totalistic features. These include the integration of the home environment with leisure and work activities, so that different activities take place within the same setting and involve the same set of people. In ‘total institutions’, it is easy to monitor and control the behaviours of residents via a set of rewards and punishments imposed by the staff. Autonomy and ability to connect with the outside world are limited as the institution takes control over residents’ lives (ibid.). ‘Total institutions’ are detrimental to older adults’ QOL, as past research has stressed the importance of autonomy and agency for older adults (Pirhonen and Pietilä, 2018). Person-centred care The negative features of ‘total institutions’ have led to the ‘person-centred care movement’ (Ekman et al., 2011). Person-centred care aims to improve quality of care and QOL by respecting the individual’s values and needs while simultaneously promoting personal growth. The emphasis of this approach is on well-being and QOL as defined by the individual (Rogers, 1995). The movement encourages institutions to make a shift from the medical perspective to focusing on residents’ autonomy and preferences. It follows the rationale that giving residents choices in terms of activities and care empowers them and provides them with a sense of control over their environment (Andrew and Meeks, 2018). A person-centred approach is promoted via active listening, acceptance, caring, empathy and sensitivity (Rogers, 1995). It has been argued that for human growth, individuals need access to ongoing learning, personal challenges and social relations (ibid.). The idea is that the care provided in an institution would be similar to the type of care one would expect in one’s home as it respects the needs, experiences, preferences and routines of the individual (Boise and White, 2004). Independent living and the CCRC Theoretically, the CCRC is designed for independent older adults who require no or very limited care upon entering the setting. Hence, it aims to move away from the totalistic features

346  Handbook of quality of life research of the total institution. Nonetheless, the setting itself does have both written and unwritten rules and expectations that older adults are expected to follow (Koren and Ayalon, 2019). When not followed, social sanctions by other residents and staff are likely to take place (Ewen et al., 2019). Moreover, as older adults’ level of independence declines and they require additional care, their autonomy is often compromised, as they are forced to move across the different levels of care available in the CCRC. In reality, safety and physiological needs often receive a priority over older adults’ preferences even in the CCRC setting (Shippee, 2009). In the author’s qualitative research conducted during the COVID-19 outbreak, it was found that many older CCRC residents were deprived of their free will and autonomy. CCRC residents made a clear distinction between the CCRC of the past, prior to the outbreak, and the CCRC during the pandemic. Specifically, to protect residents from the virus, they were instructed to stay in their residential units for several months. This resulted in high levels of depression and anxiety among residents, who viewed their stay as serving time in jail. Older adults who were Holocaust survivors compared their COVID-19 experiences to that period of their lives. In the few settings that allowed older adults greater freedom and were more transparent in decision-making, older adults reported better adjustment (Avidor and Ayalon, 2020; Ayalon and Avidor, 2021).

MEASURING QOL IN CCRCs Although considering the impact of the situational setting or physical environment on people’s QOL is a theme of this Handbook, for older adults who live in a CCRC setting, the social and the care environments should also be addressed in developing measures of the QOL in order to holistically understand linkages between the CCRC setting and residents’ QOL. Four types of measures are thus considered: ● ● ● ●

measures of QOL specifically designed for older adults; measures of QOL focused on the physical environment; measures focusing on the social environment; and measures of the care environment of the CCRC.

Measures of QOL Specifically Designed for Older Adults Many of the QOL measures that would be appropriate for use in the general population are also appropriate to use with older adults living in CCRCs, but, in addition, there are measures that have been specifically developed for older adults (Evans, 2010). Some QOL instruments used are discussed below. Often the measures are derived using a Likert scale. The Older People Quality of Life Questionnaire (OPQLQ) This Older People Quality of Life Questionnaire (OPQLQ) was developed from the perspective of older adults. The survey questionnaire includes 35 statements that tap into several domains, including: ● life overall (‘I enjoy my life’); ● health (‘I have a lot of physical energy’);

Quality of life of older adults in continuing care retirement communities  347 ● social relations and participation (‘I would like more companionship or contact with other people’); ● independence, control over life and freedom (‘I am healthy enough to have my independence’); ● home and neighbourhood (‘I feel safe where I live’); ● psychological and emotional well-being (‘I feel lucky compared to most people)’; ● financial circumstances (‘I cannot afford to do things I would enjoy’); and ● religion/culture (‘Religion, belief or philosophy is important to my quality of life’) (Bilotta et al., 2011). Research using this measure shows that it has good reliability/internal consistency, with Cronbach’s alpha ranging between 0.73 and 0.91 and satisfactory validity. The research shows that with increasing age and a greater number of health problems, scores on this measure decline (Mares et al., 2016). Further, the total score on the measure and its health sub-scale have been found to predict severe health outcomes one year later (Bilotta et al., 2011). A brief (13-item) version of this measure – Older People’s Quality of Life questionnaire (OPQOL-brief) – has been found to be reliable and valid (Bowling et al., 2013). The World Health Organization Quality of Life-OLD (WHOQOL-OLD) This contains 24 items covering six domains that encompass: ● ● ● ● ● ●

sensory abilities (impairments to senses affect daily life); autonomy (freedom to make own decisions); past, present and future activities (satisfied with opportunities to continue achieving); social participation (having enough to do each day); death and dying (concerned about the way you will die); and intimacy (sense of companionship in life).

The measure represents a complementary module of a general health-related QOL questionnaire called the WHOQOL-BREF. Following the development of a version for younger adults, it was adapted for use with older adults. Cronbach’s alpha ranges between 0.72 and 0.88 per domain and the total score has an alpha of 0.89. A study supports the structural validity of the measure (Van Biljon et al., 2015). Shorter versions of the questionnaire have been developed with adequate psychometric properties (Fang et al., 2012). Measures of QOL Focusing on the CCRC Physical Environment As the environment plays a significant role in the QOL of CCRC residents, there are several specific measures of relevance for research into the QOL of CCRC residents: Service quality in the CCRC This measure was developed based on the Service Quality (SERVQUAL) framework. The measure includes 25 items adapted for use in CCRCs and includes the following domains: ● tangibility (four items; up-to-date equipment); ● reliability (six items; the community is dependable); ● responsiveness (five items; ‘are willing to help residents’)

348  Handbook of quality of life research ● assurance (three items; ‘trust employees’); and ● empathy (seven items; ‘individual attention’). The measure has shown adequate reliability and validity. A more recent version of this measure includes additional items to assess food service quality – for example, ‘The dining room is usually attractive’; ‘I feel comfortable and confident in dining with them’. Cronbach’s alpha has been shown to range between 0.74 and 0.90 (Goh et al., 2013). The Campbell questionnaire Using a three-tier approach, Campbell (2014) designed a questionnaire to evaluate three different aspects of the living environment: context and stimulation; security; and negotiation and comfort. ● Context and stimulation is operationalised as opportunities for active engagement: ‘How much do you like to be involved with activities (such as playing cards, doing puzzles, etc.) occurring in each space?’ ● Security was operationalised as the degree of privacy perceived to be available in each space: ‘How private does each of the following spaces feel?’ ● Negotiation and comfort was operationalised as the following items’ proximity to home: ‘How close is each space to your apartment home?’, ‘How conveniently located is each space in relation to other places you go on a daily basis (that is, mailboxes, parking, etc.?)’. The Sheffield Care Environment Assessment Matrix (SCEAM). This measure was developed in the UK to evaluate the living environment of older adults (Parker et al., 2004). The SCEAM evaluates care home building regulations and guidelines. The instrument is based on direct observations of non-experts, who record the presence or absence of multiple aspects of the residential care environment. The SCEAM consists of ten domains: ● ● ● ● ● ● ● ● ● ●

privacy (43 items); personalisation (19 items); choice and control (23 items); community (18 items); safety (60 items); comfort (37 items); support for physical frailty (42 items); support for cognitive frailty (26 items); awareness of the outside world (29 items); and normalness and authenticity (31 items).

Items are recorded as either present (1) or absent (0). In addition, the measures assess subjective ratings of: ● ● ● ●

air quality; cleanliness; light level; smell;

Quality of life of older adults in continuing care retirement communities  349 ● sound level; and ● temperature. Measures of QOL Focused on the Social Environment in the CCRC The social environment can be operationalised by: ● ● ● ●

social networks; social support; sense of community; or social cohesion.

Any of these domains can be assessed in relation to the CCRC’s social environment as a potential determinant of older adults’ QOL. As the CCRC setting represents a closed social network with clear physical/space boundaries, measures relevant to sociocentric networks – whereby all members of the setting are questioned about their social relations – can be used to assess a variety of social properties both at the individual and the CCRC level (Ayalon et al., 2018). Data are usually gathered through a list of all participants in the residential setting. Residents are asked to nominate those with whom they are familiar, share intimate secrets or frequently engage. For a detailed description of sociocentric measures commonly used in studies with older adults, see Ayalon and Levkovich (2019). Table 22.1 presents a selected list of some of the sociocentric measures that can be calculated either at the CCRC level, as an average and/ or as an individual property of CCRC residents. Table 22.1

Social network terms and their definitions

Term

Definition

Degree centrality

The number of ingoing and outgoing ties that an individual member has (e.g., degree centrality indicates the number of other residents who indicate liking or knowing a specific resident)

Isolated

Individuals who have no ties with other network members

Reciprocity

The likelihood of actors in a directed (e.g., resident A likes B, but B may or may not like A) network to be mutually linked

Component

The proportion of the network that includes a path between each pair of individuals

Source: The author.

In addition to sociocentric measures, egocentric measures – which address the ties of the ego (that is, focal person) – inside and/or outside the CCRC can also be used. Often, egocentric measures are created through a name generator, when respondents are asked to name all the people they socialise with, feel close to or provide/receive support to/from. Additional indicators of frequency of contact or satisfaction with those named can be calculated (Marsden, 2002). Both egocentric and sociocentric approaches can be used to help understand negative aspects of relationships within the CCRC – for example, the extent to which others/residents get on each other’s nerves or make too many demands.

350  Handbook of quality of life research Sense of community index (McMillan and Chavis, 1986) The most common measure consists of 12 items asked on a true/false response scale (Perkins et al., 1990). A recent study modified the measure to directly refer to the CCRC setting (Ayalon, 2020). Example questions address: ● ● ● ●

membership, ‘Very few of my neighbours know me’; influence, ‘I have no influence over what this CCRC looks like’; fulfilment of needs, ‘I think this CCRC is a good place for me to live in’; and shared emotional connection, ‘It is very important to me to live in this particular CCRC’.

Cronbach’s alpha was 0.80 in past research (Perkins et al., 1990). Social cohesion (SC-5PT) The SC-5PT is a five-item measure of collective efficacy in which respondents are asked to indicate their level of agreement with five statements (such as ‘People around here are willing to help’) (Sampson et al., 1997). Measures of the Care Environment in the CCRC Person-centred care is considered an indicator of QOL. Although it is not a direct measure of QOL, it can be used as a proxy of residents’ QOL. Moreover, although older adults in CCRCs are supposedly independent and have no care needs upon entering the setting, the CCRC does provide varied levels of care and services to meet older adults’ changing care needs. The Person-centred Care Assessment Tool (P-CAT) The Person-centred Care Assessment Tool (P-CAT) is a 13-item measure administered to staff who are asked to indicate their level of agreement with statements. The items measure three sub-domains: ● the extent of personalising care; ● the amount of organisational support; and ● the degree of environmental accessibility. The Cronbach’s alpha in past research was 0.87 (Edvardsson et al., 2010). The Person-centred Climate Questionnaire – Patient Version The Person-centred Climate Questionnaire – Patient Version was designed to assess the perceptions of patients in healthcare centres with a focus on the degree of person-centred care provided by the setting. It has been used in nursing homes and other residential facilities. The measure consists of 17 items, asked on an agree–disagree scale. It is classified into three factors: ● safety (ten items); ● everydayness (four items); and ● hospitality or a caring and welcome place for residents (three items). The Cronbach’s alpha in past research was 0.93 (Edvardsson, Koch et al., 2009).

Quality of life of older adults in continuing care retirement communities  351 Person-centred Climate Questionnaire – Staff Version The Person-centred Climate Questionnaire – Staff Version is a 14-item questionnaire, asked on an agree–disagree scale. It has four factors: ● ● ● ●

a climate of safety; a climate of everydayness; a climate of community; and a climate of comprehensibility.

The Cronbach’s alpha has been demonstrated to be adequate (Edvardsson, Sandman et al., 2009).

RECOMMENDATIONS FOR FUTURE RESEARCH AND PRACTICE This chapter has stressed the importance of the situational environment in determining the QOL of older CCRC residents. Future research will benefit from identifying possible interactions between the various environments discussed above, namely: ● the physical environment; ● the social environment; and ● the care environment. There is still limited research on the ways in which the physical environment interacts with the social and care environments. Additional research also should explore how these three environments are perceived not only from the point of view of the residents, but also from the perspective of their family members, who often play a major role in older adults’ transition and adjustment to the CCRC. Considering these three types of environments as important indicators of older adults’ QOL is important not only theoretically, but also practically. The focus on these three environments as potential determinants of residents’ QOL can be used to develop innovative CCRC designs and layouts that ensure that the needs and wishes of older adults are met. It is also important to assess the effects of the three environments on the QOL of staff members, who received almost no acknowledgement in this chapter, yet play a major role in residents’ QOL (Ayalon and Avidor, 2021). Finally, examining the three environments in different care settings is highly important given the fact that the CCRC represents only one alternative, often available mainly to affluent and independent older people. As different long-term care settings cater to different populations and employ a somewhat different philosophy of care, it is important to examine their characteristics in relation to residents’ QOL.

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354  Handbook of quality of life research McLaughlin, S.J., Connell, C.M. and Heeringa, S.G. et al. (2010), ‘Successful aging in the United States: prevalence estimates from a national sample of older adults’, The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences, 65, 216–26. McMillan, D.W. and Chavis, D.M. (1986), ‘Sense of community: a definition and theory’, Journal of Community Psychology, 14, 6–23. Parker, C., Barnes, S. and McKee, K. et al. (2004), ‘Quality of life and building design in residential and nursing homes for older people’, Ageing and Society, 24, 941–62. Perkins, D.D., Florin, P. and Rich, R.C. et al. (1990), ‘Participation and the social and physical environment of residential blocks: crime and community context’, American Journal of Community Psychology, 18, 83–115. Pinquart, M. and Sorensen, S. (2001), ‘Influences on loneliness in older adults: a meta-analysis’, Basic and Applied Social Psychology, 3, 245–66. Pirhonen, J. and Pietilä, I. (2018), ‘Active and non-active agents: residents’ agency in assisted living’, Ageing and Society, 38, 19–36. Rogers, C.R. (1995), A Way of Being, Boston, MA: Houghton Mifflin Harcourt. Rowles, G.D. (1981), ‘The surveillance zone as meaningful space for the aged’, The Gerontologist, 21, 304–11. Sampson, R.J., Raudenbush, S.W. and Earls, F. (1997), ‘Neighborhoods and violent crime: a multilevel study of collective efficacy’, Science, 277, 918–24. Schafer, M.H. (2013), ‘Structural advantages of good health in old age: investigating the health-begets-position hypothesis with a full social network’, Research on Aging, 35, 348–70. Shiovitz-Ezra, S. and Leitsch, S.A. (2010), ‘The role of social relationships in predicting loneliness: the National Social Life, Health, and Aging Project’, Social Work Research, 34, 157–67. Shippee, T.P. (2009), ‘“But I am not moving”: residents’ perspectives on transitions within a continuing care retirement community’, The Gerontologist, 49, 418–27. Sugihara, S. and Evans, G.W. (2000), ‘Place attachment and social support at continuing care retirement communities’, Environment and Behavior, 32, 400–409. Van Biljon, L., Nel, P. and Roos, V. (2015), ‘A partial validation of the WHOQOL-OLD in a sample of older people in South Africa’, Global Health Action, 8, Article 28209. Van Leeuwen, K.M., Van Loon, M.S. and Van Nes, F.A. et al. (2019), ‘What does quality of life mean to older adults? A thematic synthesis’, PLOS ONE, 14, Article e0213263.

23. How urban environments affect quality of life in older socio-demographic groups: the role of physical activity behaviour Casper J.P. Zhang, Anthony Barnett, Wai-Kit Ming, Poh-Chin Lai, Ruby S.Y. Lee and Ester Cerin

INTRODUCTION The World Health Organization (WHO, 2020) has related quality of life (QOL) to the individual’s perception of their position in life in the context of the culture and value systems and the environments in which they live. It may be regarded as a health-related concept encompassing multiple domains such as physical and psychological health, social relationships and perception of the living environment. Given the ageing of the population, it is imperative to identify large-scale modifiable environmental factors that can help maintain good QOL, especially in older adults (United Nations, 2017). Socio-ecological models posit that individuals’ health and well-being are influenced by the interplay of individual, social and environmental factors (McLaren and Hawe, 2005), and that attributes of proximal environments – such as neighbourhoods – are more likely to influence older compared to younger adults’ daily living (WHO, 2007). The neighbourhood environment – encompassing physical and social characteristics – has particular importance for older adults because their predisposition to chronic diseases is likely to reduce their life space and impact their well-being and QOL. Individuals’ perceptions of their environment are primarily influenced by objective features, the measures of which are less biased than their subjective counterparts (Matthews, 1975). The effects of objective neighbourhood features on QOL are likely mediated by lifestyle behaviours and moderated by socio-demographic factors. However, relatively few studies have examined the potential moderators and mediators of environment–QOL associations. This chapter proposes a theoretical framework of the effects of the neighbourhood environment on health-related QOL encompassing health-related behaviours as mediators and individuals’ characteristics as moderators of the effects (Figure 23.1). Following a brief review of the current literature on the influence of neighbourhood attributes on older adults’ health-related QOL, data from the Active Lifestyle and the Environment in Chinese Seniors study conducted in Hong Kong is used to examine the extent to which gender moderators and physical activity (PA) mediates environment–QOL associations in older adults. The findings suggest that neighbourhoods may affect QOL through mechanisms other than PA, with the same applying to the moderating effects of gender on the associations between neighbourhood attributes and QOL. Implications for future research, practice and policy are discussed.

355

356  Handbook of quality of life research

Note: Solid lines denote hypothesised main effects; dashed lines denote moderating effects Source: The authors.

Figure 23.1

Theoretical model of the effects of the neighbourhood urban environment on health-related quality of life

OVERVIEW OF THE LITERATURE Databases (such as Scopus) were searched for published studies that quantitatively examined associations of any objective or perceived neighbourhood physical and/or social attributes with QOL and QOL-related constructs (for example, life satisfaction) among community-dwelling older adults. There were 51 eligible articles/studies published between January 1980 and July 2020 (Zhang, 2020), all but one being cross-sectional. The greatest number were conducted in Mainland China and the UK (eight studies each), followed by the US (six studies) and Hong Kong (four studies). Most studies examined only perceived environmental attributes, and only five studies employed objective neighbourhood spatial definitions. Commonly used scales for assessing QOL included versions of the Short Form (SF) Health Survey (Ware and Sherbourne, 1992), CASP (Hyde et al., 2003), WHOQOL-BREF (The WHOQOL Group, 1998) and EQ-5D (Hurst et al., 1997). Aspects of the neighbourhood environment can be generally categorised into physical/built or social attributes. Specific social aspects (for example, collective efficacy) were extensively examined in multiple studies, while specific physical attributes (for example, number of amenities) were only investigated in one or two studies (for details, see Zhang, 2020). One exception was the level of urbanisation, which was investigated in multiple studies. The most frequently studied social environmental aspects were: ● collective efficacy (19 studies; 37 per cent); ● personal/crime-related safety (15 studies; 29 per cent); and ● socio-economic status (SES) (eight studies; 16 per cent). The most frequently studied physical environmental attributes were: ● overall access to services (13 studies; 25 per cent);

How urban environments affect quality of life in older socio-demographic groups  357 ● access to specific services/destinations (public transport, parks, recreational destinations) (seven studies; 14 per cent); ● barriers to walking (six studies; 12 per cent); ● aesthetics (four studies; 8 per cent); ● traffic safety (six studies; 12 per cent); and ● residential density (five studies; 10 per cent). Social environmental attributes were assessed based on participants’ perceptions, except for several census-derived measures (for example, median household income), while physical attributes were assessed based on participants’ perceptions or objective approaches, such as geographic information systems (GIS) or in-field observations. Associations Between Attributes of the Neighbourhood Environment and QOL Among the social aspects of the neighbourhood environment, social capital and related constructs (collective efficacy and social connectedness) were most extensively studied. Residents with positive perceptions of neighbourhood social capital and neighbourhood safety reported higher QOL across different geographical contexts (Li, 2019; Ward et al., 2021). Most studies examining the relationship between neighbourhood SES and older adults’ QOL reported higher QOL in more advantaged neighbourhoods (Ward et al., 2021). Few studies investigating physical/built environmental correlates of QOL (other than urbanisation) used objective measures of the environment. Specific neighbourhood built environment attributes related to walking or other forms of PA have been investigated as potential correlates of QOL. For example: ● Parra et al. (2010) examined objective GIS-based neighbourhood destination densities and distance to destinations from home and found greater park density to be associated with better self-rated health; ● greater perceived destination accessibility was associated with better QOL (Yu et al., 2017); ● perceived pedestrian-friendly infrastructure and aesthetically pleasing surroundings were positively related to QOL (ibid.); ● higher levels of neighbourhood traffic safety were associated with better QOL (ibid.); and ● findings on the potential effect of street connectivity on older adults’ QOL were mixed (Engel et al., 2016; Yu et al., 2017). Moderation and Mediation of Environment–QOL Relationships Zhang (2020) examined 35 potential moderating effects of environment–QOL associations in 13 studies. The most frequently examined moderators were: ● gender; ● living arrangements; and ● urbanicity. Socio-demographic characteristics, environmental factors, health outcomes and social networks were found to be significant moderators in at least one study. For instance, a positive

358  Handbook of quality of life research association between collective efficacy and QOL was observed only among urban residents (Ward et al., 2021). Seven studies examined mediators of environment–QOL associations. All except one (Zhang, Zhang et al., 2018) identified partial mediating effects: ● Curl and Mason (2019) found that the frequency of within-neighbourhood walking partially mediated the positive associations of overall QOL with neighbourhood quality and personal safety; ● emotional attachment to parks acted as a partial mediator of the association between park quality and overall QOL (Chang et al., 2020); and ● sense of community explained in part the positive associations of QOL with public space and senior space densities and in full that between QOL and the proportion of older people in the neighbourhood (Zhang, Zhang et al., 2018). In summary, most of the studies reviewed focused on environment–QOL social rather than physical/built aspects of the environment. Only a quarter examined potential moderators of environment–QOL associations, and only a handful investigated the mechanisms underpinning the effects of the environment on QOL via mediation analysis. Most studies examined correlates of total QOL rather than QOL across its domains. It remains unclear which QOL domains can be potentially improved through environmental interventions to achieve better QOL in old age.

THE HONG KONG ALECS STUDY To address those knowledge gaps, the Hong Kong Active Lifestyle and the Environment in Chinese Seniors (ALECS) study has been analysed. It is an observational study designed to examine relationships between the neighbourhood environment, PA, QOL and depressive symptoms amongst Chinese community-dwelling adults aged 65 years and older (Cerin, Sit et al., 2016). With respect to QOL, Zhang et al. (2019) found that: ● living alone was associated with lower physical QOL among older adults residing in areas with large parks; ● psychological QOL was curvilinearly related to entertainment density, with a positive association observed for values of entertainment density below five destinations/km2 and a negative association observed for values ranging from five to ~16 destinations/km2; ● older adults living alone displayed higher levels of social QOL than those living with others if residing in neighbourhoods with some signs of crime/disorder or parks offering many activities; ● social QOL was negatively related to entertainment density but positively related to the presence of trees in nearby parks; ● older adults living alone reported higher social QOL than older adults living with others when residing in neighbourhoods with many pocket parks1 offering many activities; they experienced lower levels of this QOL domain if living in neighbourhoods with lots of greenery/natural sites;

How urban environments affect quality of life in older socio-demographic groups  359 ● environmental QOL was negatively related to street intersection density and presence of litter/decay; ● curvilinear associations were observed between residential and entertainment densities and environmental QOL, with the latter environmental attribute displaying a relationship similar to that of psychological QOL described above; and ● older adults living alone reported lower environmental QOL than those living with others if they had poor access to various services, parks or park amenities/features. The Hong Kong ALECS data is used to examine: ● gender as a moderator of previously reported environment–QOL associations and environment by living arrangement interactions on QOL (Zhang et al., 2019); ● PA as a mediator of previously reported environment–QOL associations; and ● PA as a mediator of the moderating effects of gender and living arrangements on the environment–QOL associations, if any. Study Design The ALECS study used a two-stage sampling method to recruit participants from tertiary planning units (TPUs), the smallest administrative units with census-level data in Hong Kong, stratified by high/low SES and walkability (a composite index representing the sum of z-scores on residential density, street intersection density and land use mix) to maximise the variation in environmental characteristics. Participants were subsequently recruited from Elderly Health Centres (operated by the Department of Health of Hong Kong) and elderly community centres located in 124 pre-selected TPUs (Zhang, Barnett et al., 2018). Eligibility criteria were being: ● ● ● ● ●

at least 65 years of age; Cantonese-speaking; not cognitively impaired; able to walk without assistance for at least 10 metres; and having resided in a pre-selected TPU for at least six months.

A sample of 909 older adults were included in this study (response rate: 71 per cent). Further details of the study design and participant recruitment have been reported in the study protocol and related publications (Cerin, Zhang et al., 2016, Zhang et al., 2019, Zhang, Barnett et al., 2018). Measures Exposures: neighbourhood attributes GIS and environmental audits were used to objectively assess the neighbourhood environmental attributes. GIS data were sourced from the Census and Statistics, Lands, and Planning Departments of the Hong Kong government. Participant residential buffers delineating individual neighbourhood boundaries were created by tracing from the participants’ residential addresses through their unique street network in all directions for 800 metres, a walkable distance considered appropriate for older adults living in high-density environments (Zhang et

360  Handbook of quality of life research al., 2019). GIS-based environmental attributes were computed for each participant’s residential buffer, such as: ● gross residential density; ● street intersection density; ● densities of various locations/destinations (for example, civic and institutional, entertainment); and ● park area. Environmental audits that were conducted by trained assessors quantified the environmental attributes for which information from the GIS database was unavailable, outdated or incomplete. The Environment in Asia Scan Tool-Hong Kong (EAST-HK) (Cerin, Chan et al., 2011) was used to assess 17 environmental features in each audited street segment, such as: ● ● ● ●

pedestrian infrastructure; aesthetics; personal and traffic safety; and various types of destinations.

The Public Open Space Tool (POST) (Broomhall et al., 2004) was used to assess several features of all public parks that intersected or were contained within a participant’s buffer. A 400-metre crow-fly buffer centred at each participant’s residence was used to identify areas for environmental audits. Details of neighbourhood attributes assessed by GIS and environmental audits are available online.2 Outcomes: QOL domains The 26-item WHOQOL-BREF (Hong Kong) was used to measure QOL via face-to-face interviews. The WHOQOL-BREF – developed based on the WHO’s definition of QOL – gauges four QOL domains: ● ● ● ●

physical health; psychological health; social relationships; and environment (The WHOQOL Group, 1998).

These four QOL domains are described as physical, psychological, social and environmental QOL, respectively. Mediator: physical activity Weekly frequency and minutes of total non-walking moderate-to-vigorous PA and within-neighbourhood walking were respectively assessed using the interviewer-administered, validated IPAQ-C – Short (Deng et al., 2008) and the NWQ-CS (Cerin, Barnett et al., 2011). The latter questionnaire asked participants to report weekly frequency and duration of walking within their neighbourhood (defined as an area up to a 15-minute walk from home) for transportation and recreational purposes separately.

How urban environments affect quality of life in older socio-demographic groups  361 Covariates and moderators The following covariates were included in all analyses: ● ● ● ● ● ● ● ●

age; gender; educational attainment; marital status; living arrangement; housing type; household car ownership; and the number of current diagnosed health problems.

Gender (men vs women) and living arrangement (living alone vs living with others) were also treated as potential moderators of the environment–QOL associations. Data Analysis Descriptive statistics were computed for all variables. Analyses were conducted in several steps with specific aims. Aim 1 First, we examined whether gender moderated: (1) the associations of objectively assessed neighbourhood environmental attributes with QOL domains; and (2) the environment by living arrangement interaction effect on QOL domains. These analyses were conducted using generalised additive mixed models (GAMMs) with Gaussian variance and identity link functions including appropriate two-way and three-way interaction terms estimating moderating effects. A difference of six units in Akaike information criterion (AIC) values between a simpler and more complex (that is, with one added interaction term) GAMM, with the latter model having a smaller AIC, was deemed to provide sufficient evidence of a moderating effect. The p-value of the added interaction term was also consulted to determine the presence of a significant interaction effect (p < 0.05). Moderating effects deemed significant were probed by estimating gender-specific or gender-by-living-arrangement-specific associations of environmental attributes with QOL domains. Aim 2 We then examined whether PA measures mediated environment–QOL associations. To accomplish this, we used the joint-significance test (Figure 23.2), according to which PA was determined to be a mediator of environment–QOL associations if the specific environmental attribute was significantly related to PA (α path coefficients: p < 0.05) and PA was significantly related to QOL after adjustment for the environmental attribute (β path coefficients: p < 0.05) (MacKinnon and Luecken, 2008). Aim 3 The same approach was used to address Aim 3. Specifically, for PA to be considered a mediator of the moderating effects of gender and/or living arrangements on these environment–QOL associations, the following had to be established: (1) a specific environmental attribute or its interaction terms with gender/living arrangements were significantly related to PA (α path

362  Handbook of quality of life research

Source: The authors.

Figure 23.2

Diagram of the main and moderated effects of neighbourhood environmental attributes on quality of life mediated by physical activity

coefficients: p < 0.05); and (2) PA was significantly related to QOL (β path coefficients: p < 0.05) after adjustment for that environmental attribute or its interaction terms with gender/ living arrangements. Mediation analyses were conducted only for neighbourhood environment attributes that showed significant (p < 0.05) main or interaction effects on QOL (θ path in Figure 23.2). Mediation analyses All mediation analyses were conducted in two stages. Stage 1 estimated the covariate-adjusted associations of the independent variables (that is, neighbourhood attributes and/or the interaction effects of neighbourhood attributes by gender and/or living arrangements) with PA variables (α regression coefficients – see Figure 23.2). In doing so, we employed two-part models for weekly frequency and minutes of non-walking PA and within-neighbourhood walking for recreation because these PA measures had more zero values than expected by a negative binomial distribution. GAMMs with binomial variance and logit link functions were used to model engagement vs non-engagement in these PA types, while GAMMs with negative binomial variance and logarithmic link functions were used to model non-zero weekly frequency and minutes of the same PA measures. GAMMs with negative binomial and logarithmic link

How urban environments affect quality of life in older socio-demographic groups  363 functions were used to model frequency and minutes of within-neighbourhood walking for transport. Stage 2 of the mediation analyses involved the estimation of the associations between PA variables and QOL domains adjusted for the independent variables of interest and other covariates (β path coefficients in Figure 23.2). These analyses also provided estimates of the direct (non-mediated) main and/or interaction effects of the independent variables of interest on QOL (θ’ path coefficients in Figure 23.2). This stage of the mediation analyses was undertaken only for PA variables that were significantly related to neighbourhood attributes or their interaction with gender or living arrangements. Separate sets of mediation analyses were performed for each PA variable due to collinearity. All analyses were conducted in R.

RESULTS Table 23.1 summarises the sample characteristics. Participants reported relatively high levels of QOL and very high levels of PA. Most environmental characteristics showed substantial variability across participants permitting robust estimation of relationships with PA and QOL. Table 23.1

Sample characteristics

Variables (Unit) [Theoretical Range]

%

Women

66.3

Educational attainment: No formal education

20.8

Primary school

35.5

Secondary school

30.5

Post-secondary school

13.2

Marital status:

 

Married or cohabiting

59.5

Widowed

32.7

Other

7.8

Housing:

 

Public and aided

43.1

Private/Purchased

51.3

Renting

5.6

Living alone

23.1

Household with car

28.5

Type of recruitment centre:

 

Elderly Health Centres

82.6

Elderly community centres

28.4

Neighbourhood type:

 

Low walkable, low SES

22.0

Low walkable, high SES

24.8

High walkable, low SES

28.3

High walkable, high SES

25.0

 

Mean (SD)

Median (IQR)†

Age (years)

76.5 (6.0)

–

Number of diagnosed health problems [0–10]

3.2 (2.0)

–

364  Handbook of quality of life research Mean (SD)

Median (IQR)†

Outcome: quality of life domains

 

 

 

Physical health (score) [4–20]

16.1 (2.4)

–

 

Psychological health (score) [4–20]

16.5 (2.1)

–

 

Social relationships (score) [4–20]

15.1 (2.1)

–

 

Environment (score) [4–20]

17.0 (2.0)

–

Mediators: physical activity

 

 

 

Non-walking physical activity (times/week)

4 (3)

6 (7)

 

Non-walking physical activity (min/week)

337 (493)

210 (420)

 

Within-neighbourhood walking for recreation (times/week)

3 (4)

0 (7)

 

Within-neighbourhood walking for recreation (min/week)

137 (220)

0 (210)

 

Within-neighbourhood walking for transport (times/week)

8 (8)

–

 

Within-neighbourhood walking for transport (min/week)

169 (205)

120 (180)

Environmental attributes

 

Based on extant GIS data aggregated by street-network residential buffers

   

 

Gross residential density (1000 households/km2)

14.3 (8.4)

12.9 (11.4)

 

Street intersection density (intersections/km2)

91.5 (40.0)

–

 

Civic and institutional density (destinations/km2)

69.7 (36.5)

–

 

Entertainment density (destinations/km2)

6.9 (5.2)

6.2 (6.2)

 

Park area (hectares)

9.5 (59.1)

2.0 (5.3)

   Based on environmental audit data aggregated by crow-fly residential buffers  

Prevalence of non-food retail and services (number)

15.9 (16.5)

11.0 (19.0)

 

Prevalence of food-related shops (number)

10.2 (8.6)

11.0 (19.0)

 

Prevalence of eating outlets (number)

13.6 (13.1)

9.0 (18.0)

 

Prevalence of destinations for socialising (number)

6.5 (6.2)

5.0 (7.0)

 

Prevalence of health clinics/services (number)

3.9 (4.2)

3.0 (4.0)

 

Number of parks (number)

2.7 (2.4)

2.0 (2.0)

 

Activity types in park (number)

1.8 (1.7)

2.0 (3.0)

 

Trees in park (score) [0–5]

2.1 (1.2)

–

 

Paths in park (score) [0–6]

1.7 (1.3)

2.0 (1.0)

 

Park aesthetics (score) [0–3]

2.4 (1.0)

–

 

Crowdedness (score) [0–100]

9.8 (8.8)

7.7 (12.5)

 

Greenery/natural sights (score) [0–100]

36.9 (16.7)

45.5 (25.6)

 

Signs of crime/social disorder (score) [0–100]

0.3 (0.9)

0.0 (0.0)

 

Litter/decay (score) [0–100]

22.9 (4.1)

–

 

Pollution (score) [0–100]

42.3 (33.2)

40.0 (61.2)

 

Number of street segments audited

21.4 (17.5)

16.0 (13.0)

Note: SES = socio-economic status; SD = standard deviation; IQR = interquartile range; GIS = geographic information systems. † computed for variables with skewness > |1.0|. ‘–’ = not applicable. Only environmental attributes relevant to this analysis are reported. Source: The authors.

Gender as a Moderator of Environment–QOL Associations and the Environment by Living Arrangements Interaction Effects on QOL A three-way interaction of gender, living arrangements and signs of crime/social disorder on psychological QOL indicated that this environmental attribute was not related to psychological QOL among older adults living with others (Figure 23.3). However, a positive association was found among women living alone (p = 0.033), and a negative association among men living

How urban environments affect quality of life in older socio-demographic groups  365 alone (p = 0.011). Gender also moderated the effects of neighbourhood entertainment density, crowdedness and pollution on social QOL, whereby negative associations were observed in men only. A negative relationship between crowdedness and environmental QOL was observed only in men (Figure 23.3).

Note: Only significant variables are presented. All estimates were adjusted for age, gender, educational attainment, household with car, marital status, housing type, living arrangement, area-level SES, type of recruitment centre and number of current diagnosed health problems. LO = living with others; LA = living alone; QoL = quality of life; PA = physical activity; b = regression coefficient; OR = odds ratio. * p < 0.05, ** p < 0.01, ***p < 0.001. † see Zhang et al., 2009. Source: The authors.

Figure 23.3

Associations between environmental attributes, physical activity and quality of life domain

Physical Activity as a Mediator of Environment–QOL Associations and the Moderating Effects of Gender and Living Arrangements on Those Associations The findings of the mediation and moderation analyses shown in Figure 23.3 are explained below, organised by QOL domain and mediation of associations vs moderating effects. PA as a mediator of environment–QOL associations The analysis showed the following: 1. Psychological QOL: PA did not mediate the curvilinear association of entertainment density with psychological QOL. 2. Social QOL: frequency of non-walking PA mediated the negative associations of street intersection density (b = –0.396; 95% Confidence Intervals (CI): –0.744, –0.047; p = 0.026) and entertainment density (b = –0.042; 95% CI: –0.068, –0.016; p = 0.002) with social QOL. Frequency of non-walking PA was negatively associated with street intersec-

366  Handbook of quality of life research

3.

4. 5.

6.

tion density (p = 0.037) and entertainment density (p = 0.003), and positively associated with social QOL (p = 0.008). Frequency of non-walking PA suppressed the positive association between trees in parks and social QOL (b = 0.132; 95% CI: 0.003, 0.261; p = 0.046) with trees in parks being negatively associated with frequency of non-walking PA. After adjustment for frequency of non-walking PA in the model of social QOL, the effect of trees in parks became more positive (b = 0.149; 95% CI: 0.021 0.277; p = 0.023 – see Figure 23.3). Environmental QOL: PA did not mediate the main effect of residential density. Frequency of non-walking PA partially mediated the association of intersection density with environmental QOL. The total effect of street intersection density on environmental QOL was negative (b = –0.73; 95% CI: –1.08, –0.38; p < 0.001) and its corresponding direct effect was weaker than the total effect (b = –0.70; 95% CI: –1.05, –0.35; p < 0.001), with frequency of non-walking PA being negatively associated with this environmental attribute and positively associated with this QOL domain (see Figure 23.3). Frequency of non-walking PA partially mediated the curvilinear association of entertainment density with environmental QOL (F = 3.706; p = 0.004), which was attenuated (F = 3.193; p = 0.010) after inclusion of frequency of non-walking PA as a mediator. As to litter/decay, its total marginally negative effect on environmental QOL (b = –0.035; 95% CI: –0.071, 0.001; p = 0.059) was suppressed by frequency of non-walking PA, which was positively associated with litter/decay as well as environmental QOL. The direct effect of litter/decay on environmental QOL not mediated by PA was stronger than the total effect and significantly negative (b = –0.040; 95% CI: –0.075, –0.004; p =0.028).

PA as a mediator of the moderating effects of gender and living arrangements on the environment–QOL associations The analysis showed the following: 1. Physical QOL: there was a two-way interaction effect between park area and living arrangement on physical QOL (p = 0.012) (see Figure 23.3). However, PA did not explain this moderating effect. 2. Psychological QOL: PA did not mediate the three-way interaction effect of signs of crime/disorder, gender and living arrangement, or the two-way interaction effect of living arrangements and number of activity types in parks on psychological QOL. 3. Environmental QOL: PA did not mediate the interaction effects of environmental attributes by gender or living arrangements on environmental QOL. Discussion An aim of the study was to extend previous analyses of living arrangements as a moderator of environment–QOL associations (Zhang et al., 2019) to the moderating effects of gender and gender by living arrangements. The extent to which PA explained these moderating effects and previously reported environment–QOL associations was also examined. Moderating effects of gender and living arrangements on environment–QOL associations The results provide some evidence of gender-specific environment–QOL associations and moderating effects of living arrangements on environment–QOL associations. The greater

How urban environments affect quality of life in older socio-demographic groups  367 level of vulnerability to environmental conditions observed in men compared with women may reflect the endorsement of traditional gender-specific roles amongst Chinese older generations who typically consider domestic chores as the domain of women, whilst non-household-related activities are the domain of men (Chou et al., 2004). Consequently, Chinese older men usually spend more time outside the home than women and, thus, have more opportunities to engage with their local community. Previous findings revealed that psychological QOL was higher amongst older adults living alone if they resided in areas with more signs of crime or social disorder (Zhang et al., 2019). Signs of crime/disorder are generally uncommon in Hong Kong, but are slightly more prevalent in destination-rich and low SES neighbourhoods (Cerin et al., 2013). Residents living alone in these areas might tend to spend a considerable amount of time outside their small apartments to take advantage of affordable opportunities for activities offered by their communities (Zhang et al., 2019). PA as a mediator of environment–QOL associations Previous analyses of the ALECS study found several objectively assessed neighbourhood characteristics to be related to specific QOL domains. These included residential density, street intersection density, entertainment density, trees in parks, and presence of litter/decay (Zhang et al., 2019). In line with the literature (Cerin et al., 2017), we hypothesised that the observed environment–QOL relationships may be in part mediated by PA because: ● medium-to-high density neighbourhoods provide more opportunities for walking for transport than low-density neighbourhoods; ● extreme levels of density, as compared to medium-to-high levels, may be associated with lower levels of PA and walking for transport because destinations and services in such neighbourhoods are too accessible; and ● walking for recreation and non-walking PA may show negative associations with density in an ultra-dense city such as Hong Kong due to high levels of pollution, crowding and noise (Fleming et al., 1987). Support was not found for PA being a mediator of the associations of residential density with environmental QOL and entertainment density with psychological QOL. Competing mechanisms other than PA may underpin the observed relationships, including engagement in a variety of social and cultural activities and exposure to environmental stressors – such as noise, pollution and over-crowdedness (ibid.). Weekly frequency of non-walking PA explained – in part or in full – the associations of entertainment density with environmental and social QOL. Frequency of non-walking PA was positively associated with both QOL domains but negatively related to entertainment density. The availability of entertainment destinations (cinemas or theatres) in the neighbourhood may promote engagement in sedentary leisure-time activities and, thus, reduce the time residents spent in more physically and socially engaging pursuits. Also, neighbourhoods with a larger number of entertainment venues tailored to younger and more affluent segments of the population may provide limited opportunities for older adults to engage in leisure pursuits, leading to feelings of social isolation and dissatisfaction with the local environment. The negative effects of street intersection density on social and environmental QOL were – in part or in full – explained by frequency of participation in non-walking PA, which was negatively related to this particular neighbourhood attribute. Areas with a dense street network

368  Handbook of quality of life research usually lack outdoor recreational spaces and are typified by excessive levels of traffic (Zhong et al., 2016). Older adults living in such areas may tend to spend more time at home and avoid engaging in leisure-time PA outdoors. That non-walking PA was only in part responsible for the association between street intersection and environmental QOL suggests that older adults’ dissatisfaction with the local environment might be also directly due to high traffic volumes, as also noted in another study (Engel et al., 2016). Two non-destination variables – trees in parks and presence of litter/decay – showed unexpected associations with frequency of non-walking PA. The negative association between the presence of trees in local parks and non-walking PA could be due to older adults spending less time on household activities and more time on socialising with others in the local parks. This would also explain why adjustment for non-walking PA led to the strengthening of the positive association between trees in local parks and social QOL. A similar argument could be used to explain the positive association between non-walking PA and litter/decay, with the latter leading to a reduction in time spent on sedentary or active pursuits in the neighbourhood and, in turn, an increase in time spent on household PA. Future investigations detailing the spatial context and domain of PA are needed to help clarify the underlying reasons for the observed findings. PA as a mediator of gender differences in QOL and moderating effects of gender and living arrangements on environment–QOL associations The findings suggest that the gender-specific environment–QOL associations (described earlier) were unlikely to be due to differences in participation in walking or other forms of PA. Further research needs to elucidate whether these gender-specific associations might be due to other health-related factors or behaviours (for example, social activity). Similarly, the three-way interaction effect of gender by living arrangements by signs of crime/social disorder on psychological QOL was not mediated by PA. The high levels of PA found in Hong Kong older people might have limited the statistical power to detect these mediating effects. It is also possible that the observed between-gender differences in associations are due to differences in preference for environmental stimuli (natural environments vs vibrant, crowded environments) or activity types (shopping vs socialising). These preferences, in turn, may determine individuals’ psychological responses to the neighbourhood environment. Acknowledging the complex interrelationships between individual-level characteristics and environmental factors, future research needs to add social behaviours and psychosocial factors to the list of possible mechanisms underpinning environment–QOL associations in older adults. It is noteworthy that frequency of non-walking PA was the only significant mediator of environment–QOL associations. Apart from benefiting physical health, regular participation in non-walking leisure-time PA can enhance psychological states and provide opportunities to connect with like-minded people (Vagetti et al., 2014). Although walking is a safe and affordable form of PA suited to older adults, and transport and recreational walking have been shown to be associated with improved QOL (Fisher and Li, 2004) and environmental attributes (Cerin et al., 2017), this study did not provide support for the mediating effect of walking. Possible reasons for the observed results include: ● the average levels of walking in Hong Kong for older adults are too high to impact on QOL;

How urban environments affect quality of life in older socio-demographic groups  369 ● the positive effects of walking on QOL may be stronger and more easily detected at low-to-moderate levels of walking; ● we examined within-neighbourhood rather than overall walking as a mediator of environment–QOL associations, whilst older adults with limited opportunities for walking in their neighbourhood may walk outside the neighbourhood.

IMPLICATIONS FOR FUTURE RESEARCH, PRACTICE AND POLICY The study reported in this chapter has examined: (1) the moderating effects of gender and living arrangements on the associations between objectively assessed neighbourhood attributes and QOL domains in Hong Kong older adults; and (2) the extent to which various types of self-reported PA explained environment–QOL associations and the moderating effects of gender and living arrangements on the same associations. Weekly frequency of non-walking PA was the only PA measure to partially mediate the main effects, but not the gender and living arrangement-moderated effect of the neighbourhood environment on QOL. An analysis of moderators and mediators allows the examination of the complex relationships between neighbourhood environment and QOL. However, as per the literature review, there is a dearth of research examining the interactions between individual characteristics and environmental attributes on older adults’ QOL. Also, only a handful of studies examined the potential mechanisms underpinning the observed environment–QOL relationships. More studies inclusive of moderation and mediation analyses are needed in this research field. The findings also suggest that other mechanisms may be responsible for the observed associations and moderating effects. These include: (1) engagement in activities other than PA that are potentially affected by characteristics of the neighbourhood environment (for example, socialising, cultural activities); and (2) psychological reactions to environmental stimuli arising from individual preferences. Future research may continue to investigate these areas to advance our understanding of environment–QOL relationships. That will help inform the establishment of more tailored environmental interventions and maximise the efficacious and efficient use of public health resources to improve QOL among older adults.

NOTES 1. A small park accessible to the general public. 2. See https://​www​.mdpi​.com/​1660​-4601/​16/​5/​876/​s1 (accessed 15 July 2021).

REFERENCES Broomhall, M.H., Giles-Corti, B. and Lange, A. (2004), Quality of Public Open Space Tool (POST), Perth: School of Population Health, The University of Western Australia. Cerin, E., Barnett, A. and Sit, C.H.P. et al. (2011), ‘Measuring walking within and outside the neighborhood in Chinese elders: reliability and validity’, BMC Public Health, 11, Article 851.

370  Handbook of quality of life research Cerin, E., Chan, K.W. and Macfarlane, D.J. et al. (2011), ‘Objective assessment of walking environments in ultra-dense cities: development and reliability of the Environment in Asia Scan Tool-Hong Kong version (EAST-HK)’, Health & Place, 17, 937–45. Cerin, E., Lee, K.Y. and Barnett, A. et al. (2013), ‘Walking for transportation in Hong Kong Chinese urban elders: a cross-sectional study on what destinations matter and when’, International Journal of Behavioral Nutrition and Physical Activity, 10, Article 78. Cerin, E., Nathan, A. and Van Cauwenberg, J. et al. (2017), ‘The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis’, International Journal of Behavioral Nutrition and Physical Activity, 14, Article 15. Cerin, E., Sit, C.H.P. and Zhang, C.J.P. et al. (2016), ‘Neighbourhood environment, physical activity, quality of life and depressive symptoms in Hong Kong older adults: a protocol for an observational study’, BMJ Open, 6, Article e010384. Cerin, E., Zhang, C.J.P. and Barnett, A. et al. (2016), ‘Associations of objectively-assessed neighborhood characteristics with older adults’ total physical activity and sedentary time in an ultra-dense urban environment: findings from the ALECS study’, Health & Place, 42, 1–10. Chang, P.J., Tsou, C.W. and Li, Y.S. (2020), ‘Urban-greenway factors’ influence on older adults’ psychological well-being: a case study of Taichung, Taiwan’, Urban Forestry and Urban Greening, 49, Article 126606. Chou, K.-L., Chow, N.W.S. and Chi, I. (2004), ‘Leisure participation amongst Hong Kong Chinese older adults’, Ageing and Society, 24, 617–29. Curl, A. and Mason, P. (2019), ‘Neighbourhood perceptions and older adults’ wellbeing: does walking explain the relationship in deprived urban communities?’, Transportation Research Part A: Policy and Practice, 123, 119–29. Deng, H.B., Macfarlane, D.J. and Thomas, G.N. et al. (2008), ‘Reliability and validity of the IPAQ-Chinese: the Guangzhou Biobank Cohort study’, Medicine and Science in Sports and Exercise, 40, 303–7. Engel, L., Chudyk, A.M. and Ashe, M.C. et al. (2016), ‘Older adults’ quality of life – exploring the role of the built environment and social cohesion in community-dwelling seniors on low income’, Social Science and Medicine, 164, 1–11. Fisher, K.J. and Li, F. (2004), ‘A community-based walking trial to improve neighborhood quality of life in older adults: a multilevel analysis’, Annals of Behavioral Medicine, 28, 186–94. Fleming, I., Baum, A. and Weiss, L. (1987), ‘Social density and perceived control as mediators of crowding stress in high-density residential neighborhoods’, Journal of Personality and Social Psychology, 52, 899–906. Hurst, N.P., Kind, P. and Ruta, D. et al. (1997), ‘Health-related quality of life in rheumatoid arthritis: validity, responsiveness and reliability of EuroQol (EQ-5D)’, British Journal of Rheumatology, 36, 551–9. Hyde, M., Wiggins, R.D., Higgs, P. and Blane, D.B. (2003), ‘A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19)’, Aging and Mental Health, 7, 186–94. Li, C.P. (2019), ‘Neighbour relationship, community cohesion, crime cognition, and quality of life of older Taiwanese’, Asian Journal of Gerontology and Geriatrics, 14, 69–75. MacKinnon, D.P. and Luecken, L.J. (2008), ‘How and for whom? Mediation and moderation in health psychology’, Health Psychology, 27(Suppl.), S99–S100. Matthews, W.H. (1975), ‘Objective and subjective judgements in environmental impact analysis’, Environmental Conservation, 2, 121–31. McLaren, L. and Hawe, P. (2005), ‘Ecological perspectives in health research’, Journal of Epidemiology and Community Health, 59, 6–14. Parra, D.C., Gomez, L.F. and Sarmiento, O.L. et al. (2010), ‘Perceived and objective neighborhood environment attributes and health related quality of life among the elderly in Bogotá, Colombia’, Social Science and Medicine, 70, 1070–76. The WHOQOL Group (1998), ‘Development of the World Health Organization WHOQOL-BREF quality of life assessment’, Psychological Medicine, 28, 551–8. United Nations, Department of Economic and Social Affairs, Population Division (2017), World Population Ageing 2017 – Highlights, New York: UN.

How urban environments affect quality of life in older socio-demographic groups  371 Vagetti, G.C., Barbosa Filho, V.C. and Moreira, N.B. et al. (2014), ‘Association between physical activity and quality of life in the elderly: a systematic review, 2000–2012’, Revista Brasileira de Psiquiatria, 36, 76–88. Ward, M., McGariggle, C.A., Carey, D. and Kenny, R.A. (2021), ‘Social capital and quality of life among urban and rural older adults: quantitative findings from the Irish Longitudinal Study on Ageing’, Applied Research in Quality of Life, 16, 1399–415. Ware, J.E. and Sherbourne, C.D. (1992), ‘The MOS 36-item Short-Form Health Survey (SF-36): I. Conceptual framework and item selection’, Medical Care, 30, 473–83. World Health Organization (WHO) (2007), Global Age-friendly Cities: A Guide, https://​www​.who​.int/​ publications/​i/​item/​9789241547307 (accessed 15 July 2021). World Health Organization (WHO) (2020), ‘WHOQOL: Measuring quality of life’, https://​www​.who​ .int/​healthinfo/​survey/​whoqol​-qualityoflife/​en/​ (accessed 15 July 2021). Yu, R., Cheung, O., Lau, K. and Woo, J. (2017), ‘Associations between perceived neighborhood walkability and walking time, wellbeing, and loneliness in community-dwelling older Chinese people in Hong Kong’, International Journal of Environmental Research and Public Health, 14, Article 1199. Zhang, C.J.P. (2020), Supplementary Documents, https://​drive​.google​.com/​drive/​folders/​ 1b4S0q2etwRktbG0UAt​-64G​-9L​_9wvIjZ (accessed 14 December 2023). Zhang, C.J.P., Barnett, A. and Johnston, J.M. et al. (2019), ‘Objectively-measured neighbourhood attributes as correlates and moderators of quality of life in older adults with different living arrangements: the ALECS cross-sectional study’, International Journal of Environmental Research and Public Health, 16, Article 876. Zhang, C.J.P., Barnett, A. and Sit, C.H.P. et al. (2018), ‘Cross-sectional associations of objectively assessed neighbourhood attributes with depressive symptoms in older adults of an ultra-dense urban environment: the Hong Kong ALECS study’, BMJ Open, 8, Article e020480. Zhang, J., Zhang, J., Zhou, M. and Yu, N.X. (2018), ‘Neighborhood characteristics and older adults’ well-being: the roles of sense of community and personal resilience’, Social Indicators Research, 137, 949–63. Zhong, J., Cai, X.-M. and Bloss, W.J. (2016), ‘Coupling dynamics and chemistry in the air pollution modelling of street canyons: a review’, Environmental Pollution, 214, 690–704.

24. Neighbourhood characteristics linked to quality of life among vulnerable older adults1 Noah J. Webster and Toni C. Antonucci

INTRODUCTION Neighbourhoods are critical in promoting quality of life (QOL), especially among older adults. A major reason is that, second only to their own personal housing unit, neighbourhoods surrounding older adults’ homes are the spatial context in which they spend most of their time. This is due in part to shrinking life space experienced in later life, which is linked with age-related functional limitations and mobility challenges (Loo et al., 2017). We expect that the salience of neighbourhoods for older adults’ QOL will continue to grow due to the intersecting and interrelated demographic trends of global ageing – that is, the increasing size of the population of older adults (United Nations, 2019), and the decreasing number of older adults with an adult child living nearby (Ryan et al., 2012). Furthermore, longer life expectancies will lead to greater heterogeneity within this age group on many dimensions (for example, variation in health, preferred activities), which will also likely result in differences in how and to what extent neighbourhoods influence QOL. Older adults living alone is more common in the US than in many other countries (Ausubel, 2020), which could result in neighbourhoods playing a greater role in shaping the QOL of this population. This chapter examines the characteristics of neighbourhoods associated with QOL/ well-being among older adults who are geographically disconnected from family – that is, their home is not located conveniently to where their family lives. It explores how this relationship varies across key sub-groups of older adults and how it is moderated by age and marital status to help contribute to a greater understanding of how the spatial context of the neighbourhood influences QOL in later life. The chapter begins with a brief overview of related literature, followed by discussion of an underutilized theory in research on geographic context and QOL – the convoy model of social relations. Findings from a nationally representative sample of adults aged 70 years and older in the US are presented. Finally, there is an overview of needed research at the intersection of environmental context, social relations and QOL.

AN OVERVIEW Neighbourhood Characteristics and QOL in Later Life Chapter 23 in this Handbook provided a succinct review of research focused on how the neighbourhood environment – both objective and subjective aspects – can influence the QOL/ well-being of older adults. Recent systematic reviews by Yen and colleagues (2009) and Padeiro and colleagues (2022) provide thorough summaries as well. Select findings regarding 372

Neighbourhood characteristics linked to quality of life among vulnerable older adults  373 neighbourhood context and older adults salient to the study presented in this chapter include the following: ● greater perceived safety is associated with better QOL (Parra et al., 2010; Sun et al., 2012); ● greater access to transportation is associated with better QOL (Bowling et al., 2003); ● open areas that promote or facilitate walking is linked to better QOL (Sugiyama et al., 2009); and ● poor weather conditions and climate change disproportionately impact older adults’ QOL (Filiberto et al., 2009; McDermott-Levy et al., 2019; Rantanen et al., 2015). Living Apart/Far from Family in Later Life Given our focus in this chapter on the QOL of older adults living apart from family, a brief background on the prevalence of this unique social context in the US is provided. According to Choi and colleagues (2020), in 2013 in the US, just under a quarter (23.8 per cent) of adults with one or more adult children (i.e., children aged 25 years and older) live 30 or more miles from their nearest adult child, with 6.6 per cent of those residing 500 or more miles from their nearest adult child. Among those who are not partnered, these numbers are slightly lower, with 19.2 per cent of adults living 30 or more miles from their nearest adult child, with 5.5 per cent living 500 or more miles. Further, only 38.6 per cent of adults co-resided or lived less than 30 miles from all of their adult children. This number is slightly higher for those who are not partnered (46.6 per cent). These findings suggest a sizeable number of older adults do not live in close geographic proximity to their adult children. Ryan and colleagues (2012), using US nationally representative survey data, projected a substantial increase in the number of older adults without a child living nearby. The increasing size of this group of older adults not living near family is important because close family plays an important role in promoting QOL among older adults through provision of emotional and instrumental support. Access to close family, in particular, has been linked with older adults’ ability to live independently – that is, better functional health (Webster et al., 2015). Among this large and growing group of older adults living apart from family, we argue that neighbourhood context may play a more salient role in terms of their QOL. Stahl et al. (2017) examined the well-being of older adults at the intersection of both access to family and neighbourhood context. They found that living alone compared to living with a family member was more strongly associated with depressive symptomatology when residing in a neighbourhood with low social quality – that is, a neighbourhood where people are less likely to help each other, are less trusted and less close knit. That study provided an essential foundation to the research reported in this chapter. Most research focused on older adults’ geographic proximity to family members uses objective measurements of estimated time to travel or distance. This is useful, but we argue that a subjective approach is also needed for two reasons: (1) the objective measures, while standardized, may vary in meaning and impact across geographic contexts (for example, rural, suburban, urban areas); and (2) subjective measurement of the geographic closeness of one’s home to where family members live (for example, convenience of one’s home to family) may be a more holistic measure given its likelihood of incorporating other related dimensions, such as care needs and preferences for how far or close one wants to live compared to other family members.

374  Handbook of quality of life research

THEORETICAL FOUNDATIONS Multiple theoretical frameworks have provided greater understanding of how the spaces and places where older adults spend time impact their QOL/well-being. For example, Lawton’s (1983) person–environment fit model, which is discussed in depth in Chapter 21 of this Handbook, has provided critical insights. In this chapter we do not aim to review or critique the rich theoretical work developed and refined within the field of environmental gerontology, with Wahl (2015) providing a thorough and succinct review of major theories in the field. Rather, we aim to highlight a theory less often used to understand links between the environment and the QOL of older adults and suggest additional expansions to the theory. One model that has received little attention in the literature on environmental gerontology is the convoy model of social relations (Antonucci et al., 2011; Kahn and Antonucci, 1980). This might be due to the model being centrally focused on social relationships rather than explicitly on the role of the environment. We provide a brief overview of the model as well as refer to a recent expansion of the model to explicitly articulate the role that the environment may play, together with social relations to influence well-being. Last, we argue for further adaptation of the expanded model to consider how social context may be an important moderator of the link between environmental resources and QOL/well-being. The Convoy Model of Social Relations According to the convoy model, people are surrounded by a group of close and important people that influence the individual’s well-being over time. This group of people is often referred to as a person’s social network or social convoy. A person’s social relationships are described in terms of three constructs: ● convoy structure (for example, size, contact frequency) and composition (for example, proportion of family versus non-family, average age); ● convoy support and function (for example, emotional, instrumental exchanges of support); and ● convoy quality (for example, positive and negative relationship quality). Structure and composition influence support and function, which influences convoy quality and then ultimately the well-being of the individual. Each of these dimensions of social relations are influenced by both personal (for example, age, gender) and situational (roles such as being a spouse/partner) characteristics. An early empirical testing of the model (Antonucci and Akiyama, 1987) is one of the most often cited works in the field of gerontology (Ferraro and Schafer, 2008). Expanded Version of Convoy Model to Incorporate Environmental Context When the convoy model was developed, situational context was broadly conceptualized. Over time, researchers have examined the association between a range of situational contexts and social relations. For example, the health context of the individual or the sick role has been examined in terms of its influence on social relations and how this can impact a person’s well-being later in life (Antonucci et al., 2019). Webster et al. (2017) have called for research grounded in the convoy model to explicitly incorporate environmental context. They argue

Neighbourhood characteristics linked to quality of life among vulnerable older adults  375 that the environment has been a largely underexplored situational context in research focused on social relations and, in particular, in research focused on applying the convoy model. Webster et al. (2017) hypothesized that social relations – specifically network structure and composition – would mediate the association between environmental resources – which could be built (for example, nearby parks, stores) or social (for example, more people of similar age, more people who have lived in the neighbourhood longer) – and QOL/well-being. They argue that these resources provide essential spaces and opportunities for social interactions among nearby residents. Those interactions could potentially and eventually develop into closer relationships, resulting in larger individual social networks, more frequent contact with neighbours and a network that includes a mix of friends to complement already potentially emotionally close family members. These social relations, then, are expected to promote positive well-being (for example, Huxhold et al., 2020; Rafnsson et al., 2015). In terms of the social resources embedded within one’s neighbourhood, Kubzansky et al. (2005) found that older adults living in neighbourhoods with a larger population of older adults reported better well-being. In terms of built environmental resources, recent work by Finlay et al. (2019) shows how these physical places – often characterized as ‘third places’ in neighbourhoods (such as coffee shops, malls, bookstores) – were closed during the COVID-19 pandemic. This had particularly strong negative impacts on the well-being of older adults. The concept of third places and their role in promotion of social interaction is also examined in Chapter 9. Webster et al. (2017) also postulated that the effect of environmental resources on social network structure would be more profound in earlier life compared to later life. This is due, they argue, to the expectation that social networks among younger adults are developing to a greater extent compared to older adults. In Chapter 9, the empirical research presented by Veeroja and colleagues provides a test of this hypothesis. They found evidence to the contrary. Specifically, they found that the social environment was more important for social interaction among older compared to younger adults. Webster et al. (2022) also tested this hypothesis with a focus on green infrastructure, which lies at the intersection of the built and natural environment. They found that older adults perceived a nearby landscape intervention to have a greater positive effect on how often they interact with their neighbourhoods compared to younger adults. Both studies together suggest that environmental context may have a more profound impact on social relations later in life. This is plausible given the potentially greater amount of time older adults spend in their neighbourhoods compared to younger adults who may spend more/equal time in other settings, such as schools and workplaces. A second hypothesis outlined by Webster et al. (2017) in their proposed expansion of the convoy model is that social relations moderate the association between environmental context, specifically stressors (for example, objective crime rates or feeling unsafe in one’s neighbourhood) and QOL/well-being. These stressors generally have a negative impact on well-being. However, among those with more positive social relations, it is hypothesized that the relationship between environmental stressors and well-being will be reduced or be weaker compared to those with access to more positive social relationships. This relationship has been less explored in the literature. In a related study, focused on educational attainment rather than environmental context, Antonucci et al. (2003) examined how social relations moderate the association between education and health. They found that men with larger social networks and who perceived more support to be available from an adult child had better health compared to those with smaller networks and those who perceived less

376  Handbook of quality of life research support. Given the classic finding that lower levels of education are associated with poorer health, the ability of social relations to modify this association has significant implications. Additional Adaptations Needed for the Expanded Model Figure 24.1 shows the model proposed by Webster et al. (2017), which expands the convoy model of social relations to explicitly include the environmental context. In the figure, the paths depicted in black designate an additional adaptation that we argue is needed.

Note: The model presented, including the paths in grey, is Webster and colleagues’ (2017) proposed expansion to the convoy model of social relations to explicitly include environmental context. The paths depicted in black are an additional adaptation proposed to specify that the effect of environmental resources on QOL/well-being may be moderated by social relations. Source: The authors.

Figure 24.1

Expanded convoy model of social relations with additional adaptation for neighbourhood resources

In the proposed expansion of the convoy model, Webster et al. (2017) only considered that the effect of neighbourhood stressors on QOL/well-being would be moderated by social relations. They did not consider that the effect of neighbourhood resources would be moderated. Their proposition about stressors is grounded in the substitution hypothesis – that is, lack of one resource (for example, neighbourhood safety) is substituted by another (for example, social support) to offset any potential detrimental impact on an outcome, in this case QOL/ well-being. Here we hypothesize that social relations may moderate not just environmental stressors by buffering their negative impact on QOL/well-being, but may also amplify the presence of environmental resources. For example, among those with a larger social network, access to parks in one’s neighbourhood may be more strongly associated with better QOL/ well-being when compared to those with smaller social networks. People with larger networks might be more likely to benefit from access to parks by meeting or taking walks with network

Neighbourhood characteristics linked to quality of life among vulnerable older adults  377 members and thereby enhancing their QOL/well-being. The inclusion of such a hypothesis in the model, we argue, would allow for a more complete understanding of and accounting for the role that social relationships together with environments play in shaping QOL/well-being. Investigations aiming to test the hypotheses we articulate can take two forms – across-group and within-group: (1) an across-group focus may compare how environmental resources interact with specific social contexts (for example, larger versus smaller social networks, farther versus closer proximity to family) to influence QOL/well-being; (2) a within-group approach focuses on identifying heterogeneity within a social context and seeks to examine within this group how environmental resources are related to QOL/well-being. Both approaches are useful and necessary. The across-group approach can identify disparities and guide allocation of resources to address the disparity. The within-group approach, on the other hand, can guide the development of context specific interventions.

PRESENT STUDY In the study presented here we take a within-group approach. Specifically, we examine characteristics of neighbourhoods associated with QOL/well-being among older adults geographically disconnected from family – that is, their home is not located conveniently to where their family lives. The aim is to identify heterogeneity within this type of social context in terms of the role that environmental resources play in shaping QOL/well-being. Additionally, we examine the moderating role of age and marital status, both articulated in the original convoy model. We focus on age in this sample of older adults to explore age heterogeneity within this group, which is often considered homogeneous compared to younger age groups. A focus on age within samples of older adults, we argue, is needed, given longer life expectancies, which makes an early 70-year-old very different from a mid-90-year-old or 100-year-old. Those sub-groups may differ in terms of functional or cognitive abilities as well as needs and preferences for things in the environment. The focus on marital status specifically compares those married or living with a partner to those who are not, given that this may further contextualize how living apart from family impacts what aspects in the environment are most salient for QOL/well-being. For example, for an older adult living far from family who is also not married or living with a partner, the neighbourhood environment may play an even greater role in QOL/well-being compared to those who are married or living with a partner. The Data Data for this study come from the University of Michigan’s Survey of Consumer Attitudes (SCA) (Curtin, 1982; Curtin et al., 2005). Since 1978, the SCA has been administered monthly via telephone. Each month, a nationally representative sample of approximately 500 households in the coterminous US are selected via random digit dialling to participate. Within households, an adult aged 18 or older is selected to participate. Approximately 60 per cent of the households surveyed each month are new cases, and 40 per cent are re-interviews from a previous month.

378  Handbook of quality of life research The SCA regularly includes supplemental questions to the core survey instrument on consumer attitudes. For the 24 months from July 2009 to June 2011, a senior living supplemental questionnaire was administered as part of the monthly survey to new respondents aged 70 and older. Included in the supplement were questions about well-being, which were used in analyses presented in this chapter, and detailed below. During the ten months from September 2010 to June 2011, additional questions were asked about the neighbourhood environmental context of older adults’ current living situation – for example, convenience of home to social and built environmental resources and satisfaction with specific aspects of the neighbourhood. During those ten months, 629 older adults aged 70 years and older participated in the supplemental questionnaire. These repeated cross-sectional samples were pooled together into one sample. Due to our focus on older adults who feel geographically disconnected from family, we selected from this sample those who responded ‘not at all’ (9.1 per cent), ‘not very’ (11.9 per cent) or ‘somewhat’ (25.8 per cent) when asked: ‘How convenient is your home to where your family lives?’ This resulted in a final analytic sample size of n = 294. Survey respondent characteristics The sample mean age was 77 years and ranged from 70 to 96 years. Fifty-six per cent were female, only 7 per cent were members of racial/ethnic minority groups, the median education level was some college and just over half (53 per cent) were married or living with a partner. The Measures QOL/well-being To address QOL/well-being, three separate measures were used: ● life satisfaction was measured with the single item, ‘Overall how satisfied are you with life these days?’, asked on a five-point Likert scale ranging from 1 = very dissatisfied to 5 = very satisfied; ● worry about having a lack of independence was measured with a single item in which respondents were asked, ‘How worried are you about having a lack of independence?’, ranging from 1 = not worried at all to 4 = worry a lot; and ● loneliness was measured with the single item, ‘How often do you feel lonely?’, ranging from 1 = never to 4 = very often. Neighbourhood characteristics Neighbourhood characteristics were measured in two ways: ● First, respondents were asked how conveniently their home is located to the following on a four-point scale ranging from 1 = not at all convenient to 4 = very convenient: ● where friends live; ● stores (grocery or drug stores); and ● exercise facilities. ● Second, respondents were asked to rate their satisfaction (1 = not at all satisfied to 4 = very satisfied) with the following three aspects of the environment in which they live: ● availability of public transportation; ● safety of their neighbourhood; and ● climate and weather.

Neighbourhood characteristics linked to quality of life among vulnerable older adults  379 Moderators We focused on two moderators of the relationship between neighbourhood characteristics and QOL/well-being – age and marital status: (1) for age, the sample was divided into two age groups: those aged 70–79 (n = 200; 68 per cent); and those aged 80 years and older (n = 94; 32 per cent); (2) for marital status, the sample was divided into those married or living with a partner (n = 156; 53 per cent) and those who were not (that is, those who were separated, divorced, widowed or never married (n = 138; 47 per cent)). Covariates In analysing the data, we controlled for the two moderators described above as well as for gender and educational attainment. Analysis Analysing the data involved the following steps: 1. First, we examined main effects of neighbourhood characteristics by conducting three regression models, one for each QOL/well-being outcome. 2. Next, we created interaction terms between both age and marital status with each of the six neighbourhood characteristics. 3. We then added each interaction one at a time into the models predicting each QOL/ well-being outcome and examined the impact of the interaction on the overall explained variance (adjusted R-square) of the model. If the addition of the interaction term resulted in a statistically significant (p-value < 0.05) increase in the adjusted R-square, it was considered to be a statistically significant interaction. 4. For significant interactions, we then examined the neighbourhood characteristics–QOL/ well-being link separately within each of the two moderating variable categories (for example, we compared those aged 70–79 years to those 80+ years). Summary of Results In the following section, we present a brief summary of the results, starting with descriptive findings, followed by main effects and then interaction/moderating effects. Descriptives The mean, standard deviation and range for each neighbourhood characteristic and the three well-being outcomes are presented in Table 24.1. Key findings include the following: ● On average, respondents live in neighbourhoods conveniently situated to both social and built environment resources, including their friends, stores and places where they can exercise. The mean on all three of these indicators was between ‘somewhat’ and ‘very convenient’. ● For satisfaction with specific neighbourhood resources, there was greater variability, with respondents being least satisfied with public transportation (mean between ‘not very’ and ‘somewhat satisfied’) and more satisfied with neighbourhood safety and the climate where they live (means between ‘somewhat’ and ‘very satisfied’).

380  Handbook of quality of life research ● For QOL/well-being, the sample was on average between ‘somewhat’ and ‘very satisfied’ with their daily life these days. In terms of worrying about independence, most respondents reported worrying some. They also reported, on average, ‘sometimes’ feeling lonely. Table 24.1

Sample descriptive statistics

Neighbourhood Characteristic

Mean (SD)

Range

Convenience of home to…

 

 

Friends

3.3 (0.8)

1–4

Stores

3.5 (0.7)

1–4

Exercise facilities

3.2 (1.0)

1–4

Satisfaction with…

 

 

Public transportation

2.6 (1.1)

1–4

Safety

3.7 (0.5)

1–4

Climate

3.4 (1.7)

1–4

Well-being:

 

 

Life satisfaction

4.5 (0.8)

1–5

Worry about independence

1.9 (0.9)

1–4

Loneliness

2.0 (0.8)

1–4

Source: The authors.

Main effects The main effects were also revealing. We found that: ● greater satisfaction with neighbourhood safety and the climate were both significantly associated with greater life satisfaction; ● greater satisfaction with the availability of public transportation and the climate were both significantly associated with less worry about having a lack of independence; and ● none of the community resources were associated with loneliness. Moderating effect of age We also examined the moderating effect of age on QOL/well-being. Among those aged 80 years and older, greater satisfaction with neighbourhood safety was significantly associated with greater life satisfaction. In contrast, among those aged 70–79 years, there was no association between neighbourhood safety and life satisfaction. Moderating effect of marital status Similarly, we examined the moderating effect of marital status on QOL/well-being. Among those who were not married or living with a partner, living in an area that was more conveniently located to stores (such as a grocery store, pharmacy) was significantly associated with less loneliness. In contrast, among those who were married/living with a partner, convenience of one’s home to stores was not associated with loneliness. Despite finding a statistically significant interaction between convenience of home to exercise facilities and marital status when predicting loneliness, the association between convenience of home to exercise facilities and loneliness was not significant among either those who were and were not married or living with a partner. A more detailed examination of these findings is intriguing and indicates that the associations were in opposite directions.

Neighbourhood characteristics linked to quality of life among vulnerable older adults  381 Among those who were not married/living with a partner, living in an area that was more conveniently located to exercise facilities was associated with less loneliness (b = –0.13). In contrast, among those married/living with a partner, convenience of home to exercise facilities was associated with reports of more loneliness (b = 0.17).

DISCUSSION OF FINDINGS In this section, we briefly summarize and discuss the findings, starting with main effects, followed by results from the moderation analyses. We then conclude with a discussion of limitations of the present study and provide recommendations for future research directions in this area. Main Effects In terms of significant main effects, the findings are consistent with the previous literature. This suggests similarities among general populations of older adults and this study’s sub-sample of older adults in the US who report living apart from family. We did not necessarily find evidence though that the environment quantitatively plays a larger role in the QOL of this sub-group. Rather, we think it is more nuanced, with specific aspects of the environment playing a stronger role in influencing QOL among this sub-group of potentially vulnerable older adults. Safety As with the findings reported by Parra et al. (2010) and Sun et al. (2012), we found that perceptions of neighbourhood safety play a salient role in older adults’ QOL, specifically life satisfaction. In fact, safety had the strongest association of any of the six neighbourhood predictors examined. Climate While studies have examined the effect of weather and climate on QOL and specifically older adults’ QOL, little work has been done on satisfaction with the climate where one lives and QOL. We found that greater satisfaction with the climate where one lives was associated with two of the three QOL/well-being outcomes we examined: greater life satisfaction and less worry about a lack of independence. This is consistent with Rantanen and colleagues’ (2015) findings indicating that weather was cited as the primary reason older adults do not go outside. Restricting time outside can likely lead to social isolation as well as impact the ability to perform activities of daily living such as shopping for groceries, both of which can detrimentally impact QOL. Public transportation Also, consistent with previous literature (for example, Sugiyama et al., 2009), and not surprising, was the finding that greater satisfaction with public transportation where one lives was associated with less worry about lack of independence. In fact, transportation had the strongest association of any of the six neighbourhood characteristics with this outcome. This suggests

382  Handbook of quality of life research salience among this sub-group. They are likely to have less access to family members to help with transportation needs and thus need to rely more on public transportation. Loneliness None of the six neighbourhood characteristics were associated with loneliness. This is remarkable given the subsample and social context of focus (that is, older adults living not at all convenient to where family lives). Relatedly, it was also unexpected that the convenience of home to friends, stores and exercise facilities were all not associated with any of the three well-being outcomes examined. This is surprising because it is well known that these social and built environmental resources can facilitate social interaction either directly or indirectly. The lack of association between these ‘convenience’ measures and QOL/well-being could be due to measurement (that is, asking the degree to which each is conveniently located to where one lives). Being near each of these neighbourhood resources does not capture or convey satisfaction with preference to use, or reflect actual utilization/engagement with the resource, all of which would likely drive an association with QOL/well-being. Access is important, but likely only part of the story. Moderating Effects Safety by age When examining the moderating effect of age on the association between neighbourhood characteristics and well-being, we found only one significant interaction, with safety predicting life satisfaction. This interaction provides greater nuance to the main effect discussed earlier. We found that only among those aged 80 years and older was greater satisfaction with neighbourhood safety associated with greater life satisfaction. In contrast, among those between the ages of 70 and 79, safety was not associated with life satisfaction. This suggests that among those with the combination of the unique social context of focus in our study (that is, living apart from family) and being in the oldest age group, feeling safe is an important driver of QOL. This could be in part linked to greater likelihood of mobility limitations and home confinement among those in this age group. Marital status by convenience of home to store Even though there were no main effects found for neighbourhood characteristics on loneliness, we did find two significant interactions. In particular, we want to highlight the finding linked to convenience of home to stores, given that the other interaction (with exercise facilities) did not result in a significant association among either of the two groups examined. Specifically, we found that among those who were not married or living with a partner, living more conveniently to stores was significantly associated with less loneliness. In contrast, among those who were married/living with a partner, convenience of home to stores was not associated with loneliness. Given the outcome (loneliness), this finding suggests that for those who both live apart from family and are not married/living with a partner, nearby stores can serve as a critical spatial facilitator of social engagement. This is consistent with previous literature focused on the importance of ‘third places’ in neighbourhoods that have the potential to facilitate social interaction even if that is not their primary function (for example, Finlay et al., 2019; Kubzansky et al., 2005). As both the number of older adults living apart from family and those who are

Neighbourhood characteristics linked to quality of life among vulnerable older adults  383 not married/living with a partner are projected to grow in the future, it is critical to ensure the presence of third places such as grocery stores, coffee shops and recreational facilities such as parks and gyms in neighbourhoods with increasing numbers of older adults. Limitations and Future Directions There are four primary limitations to the study presented. We discuss each and in doing so provide recommendations for future research in this area. Within-group focus In the study presented, we focused exclusively on older adults in a unique social context (that is, their home being not at all conveniently located to where family members live). This was done intentionally to identify which neighbourhood characteristics are associated with QOL among this unique and growing sub-group of older adults. While this approach is needed to develop interventions specific to this social context, it only tells half the story. It helps to guide development of interventions to improve QOL among this specific sub-group. Future studies are needed to compare older adults whose home is more versus less conveniently located to where family lives as well as to examine the frequency and quality of the contact more explicitly. Such studies will complement the results presented in this chapter and help determine which group is in greater need of resources. Lack of focus on mechanisms In the study reported here, we did not seek to identify mechanisms that drive the associations between neighbourhood characteristics and QOL. Future research is needed to link both access and satisfaction with preference for and actual use of the resources examined. Such research could guide the redesigning or repurposing of existing resources if found to be available to older adults, but underutilized. For example, there has been a recent trend in the US of repurposing malls into areas for exercise (Meyersohn, 2023). Lack of focus on distinguishing rural versus urban contexts As noted earlier in the chapter, a potential limitation of using objective indicators of distance of one’s home to family (for example, time to travel, actual distance) is that these measures could have different meanings in rural and urban contexts. As we noted prior though, a benefit of using a perceived measure of this construct is that it can provide a holistic view, which may in turn accurately reflect both rural and urban living experiences. In the study presented in this chapter, we did not explicitly examine, or control for, rurality or urbanicity. Empirical research though is needed to test whether variation in this additional geographic context has an impact on perceptions of convenience of home to family. Also, research is needed to examine whether this context together with convenience of home to family moderates the association between neighbourhood characteristics and QOL. It is likely that environmental solutions to promote QOL will vary in type and amount depending on whether the geographic context is rural, urban or somewhere in between. Focus only on subjective/self-report measures In the study presented here, we utilized data that included only self-reports of neighbourhood characteristics (location convenience and satisfaction). Future research is needed to test the

384  Handbook of quality of life research theoretical framework discussed in this chapter using the integrated approach to studying QOL (see Chapters 2 and 7). This would include linking objective measures of what resources exist in respondents’ neighbourhoods (see Chapters 5 and 26 for discussions of objective environmental measures) with self-reported data collected from surveys. Doing so would provide a more comprehensive understanding of the role that neighbourhoods together with social relations play in shaping QOL among older adults.

CONCLUSION This chapter has shone a spotlight on older adults’ QOL, with a specific focus on those who feel they live far from family in the US – a population projected to grow. It also provided an overview of a theoretical framework – the convoy model of social relations – which is underutilized in studies of environmental context and QOL. Specifically, in the research presented in this chapter, we built upon the expansion of the convoy model of social relations that has been proposed by Webster et al. (2017) and which includes additional hypotheses that allow for testing links between environmentally situated resources, social relations/context and QOL. The study presented in this chapter is a first step in testing the expanded convoy model. We found overall that neighbourhoods have a critical role to play in the QOL of older adults living apart from family. Further, we showed that this sub-group of older adults is not homogeneous, and other factors such as age and marital status further contextualize how and to what extent their neighbourhood environment and its attributes can contribute to or hinder QOL.

NOTE 1. This work was supported by a grant from the National Investment Center for the Seniors Housing and Care Industry (Toni Antonucci, PI) and the US National Institutes of Health (K01AG062754; Noah Webster, PI).

REFERENCES Antonucci, T.C., Ajrouch, K.J. and Janevic, M.R. (2003), ‘The effect of social relations with children on the education–health link in men and women aged 40 and over’, Social Science and Medicine, 56, 949–60. Antonucci, T.C., Ajrouch, K.J., Webster, N.J. and Zahodne, L.B. (2019), ‘Social relations across the life span: scientific advances, emerging issues, and future challenges’, Annual Review of Developmental Psychology, 1, 313–36. Antonucci, T.C. and Akiyama, H. (1987), ‘Social networks in adult life and a preliminary examination of the convoy model’, Journal of Gerontology, 42, 519–27. Antonucci, T.C., Birditt, K.S. and Ajrouch, K. (2011), ‘Convoys of social relations: past, present, and future’, in K.L. Fingerman, J. Smith and C. Berg (eds), Handbook of Life-span Development, New York: Springer, pp. 161–82. Ausubel, J. (2020, 10 March), ‘Older people are more likely to live alone in the US than elsewhere in the world’, Pew Research Center, https://​pewrsr​.ch/​2TV01ao (accessed 15 May 2023).

Neighbourhood characteristics linked to quality of life among vulnerable older adults  385 Bowling, A., Gabriel, Z. and Dykes, J. et al. (2003), ‘Let’s ask them: a national survey of definitions of quality of life and its enhancement among people aged 65 and over’, The International Journal of Aging and Human Development, 56, 269–306. Choi, H., Schoeni, R.F. and Wiemers, E.E. et al. (2020), ‘Spatial distance between parents and adult children in the United States’, Journal of Marriage and Family, 82, 822–40. Curtin, R.T. (1982), ‘Indicators of consumer behavior: the University of Michigan Surveys of Consumers’, Public Opinion Quarterly, 46, 340–52. Curtin, R., Presser, S. and Singer, E. (2005), ‘Changes in telephone survey nonresponse over the past quarter century’, Public Opinion Quarterly, 69, 87–98. Ferraro, K.F. and Schafer, M.H. (2008), ‘Gerontology’s greatest hits’, The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 63(Suppl.), S3–S6. Filiberto, D., Wethington, E. and Pillemer, K. et al. (2009), ‘Older people and climate change: vulnerability and health effects’, Generations, 33, 19–25. Finlay, J., Esposito, M. and Kim, M.H. et al. (2019), ‘Closure of “third places”? Exploring potential consequences for collective health and wellbeing’, Health and Place, 60, Article 102225. Huxhold, O., Fiori, K.L., Webster, N.J. and Antonucci, T.C. (2020), ‘The strength of weaker ties: an underexplored resource for maintaining emotional well-being in later life’, The Journals of Gerontology: Series B, 75, 1433–42. Kahn, R.L. and Antonucci, T.C. (1980), ‘Convoys over the life course: attachment, roles, and social support’, in P.B. Baltes and O. Brim (eds), Life Span Development and Behavior, Volume 3, New York: Academic Press, pp. 253–67. Kubzansky, L.D., Subramanian, S.V. and Kawachi, I. et al. (2005), ‘Neighbourhood contextual influences on depressive symptoms in the elderly’, American Journal of Epidemiology, 162, 253–60. Lawton, M.P. (1983), ‘Environment and other determinants of well-being in older people’, The Gerontologist, 23, 349–57. Loo, B.P., Mahendran, R., Katagiri, K. and Lam, W.W. (2017), ‘Walking, neighbourhood environment and quality of life among older people’, Current Opinion in Environmental Sustainability, 25, 8–13. McDermott-Levy, R., Kolanowski, A.M., Fick, D.M. and Mann, M.E. (2019), ‘Addressing the health risks of climate change in older adults’, Journal of Gerontological Nursing, 4, 21–9. Meyersohn, N. (2023, 15 May), ‘America’s fastest growing sport is coming to your mall’, CNN, https://​ www​.cnn​.com/​2023/​05/​13/​business/​pickleball​-malls​-retail​-bed​-bath​-beyond/​index​.html (accessed 17 May 2023). Padeiro, M., de São José, J. and Amado, C. et al. (2022), ‘Neighbourhood attributes and well-being among older adults in urban areas: a mixed-methods systematic review’, Research on Aging, 44, 351–68. Parra, D.C., Gomez, L.F. and Sarmiento, O.L. et al. (2010), ‘Perceived and objective neighbourhood environment attributes and health related quality of life among the elderly in Bogotá, Colombia’, Social Science and Medicine, 70, 1070–76. Rafnsson, S.B., Shankar, A. and Steptoe, A. (2015), ‘Longitudinal influences of social network characteristics on subjective well-being of older adults: findings from the ELSA study’, Journal of Aging and Health, 27, 919–34. Rantanen, T., Äyräväinen, I. and Eronen, J. et al. (2015), ‘The effect of an outdoor activities’ intervention delivered by older volunteers on the quality of life of older people with severe mobility limitations: a randomized controlled trial’, Aging Clinical and Experimental Research, 27, 161–9. Ryan, L.H., Smith, J., Antonucci, T.C. and Jackson, J.S. (2012), ‘Cohort differences in the availability of informal caregivers: are the Boomers at risk?’, The Gerontologist, 52, 177–88. Stahl, S.T., Beach, S.R., Musa, D. and Schulz, R. (2017), ‘Living alone and depression: the modifying role of the perceived neighbourhood environment’, Aging & Mental Health, 21, 1065–71. Sugiyama, T., Thompson, C.W. and Alves, S. (2009), ‘Associations between neighbourhood open space attributes and quality of life for older people in Britain’, Environment and Behavior, 41, 3–21. Sun, V.K., Stijacic Cenzer, I. and Kao, H. et al. (2012), ‘How safe is your neighbourhood? Perceived neighbourhood safety and functional decline in older adults’, Journal of General Internal Medicine, 27, 541–7. United Nations, Department of Economic and Social Affairs, Population Division (2019), World Population Ageing 2019: Highlights (ST/ESA/SER.A/430). New York: United Nations.

386  Handbook of quality of life research Wahl, H.W. (2015), ‘Theories of environmental influences on aging and behavior’, in N. Pachana (ed.), Encyclopedia of Geropsychology, Singapore: Springer, pp. 1–8. Webster, N.J., Ajrouch, K.J., Wan, W. and Antonucci, T.C. (2017), ‘Environmental context and social relationships: a relational perspective on health disparities’, in A.S. Dick and U. Müller (eds), Advancing Developmental Science: Philosophy, Theory, and Method, New York: Psychology Press, pp. 210–21. Webster, N.J., Antonucci, T.C., Ajrouch, K.J. and Abdulrahim, S. (2015), ‘Social networks and health among older adults in Lebanon: the mediating role of support and trust’, Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70, 155–66. Webster, N., Sampson, N. and Nassauer, J. (2022), ‘Impact of neighbourhood green infrastructure on access to social capital across the life course’, Innovation in Aging, 6(Suppl. 1), S619. Yen, I.H., Michael, Y.L. and Perdue, L. (2009), ‘Neighbourhood environment in studies of health of older adults: a systematic review’, American Journal of Preventive Medicine, 37, 455–63.

25. Conceptualising contextual variation in older adults’ quality of life cross-nationally: challenges and opportunities Christine A. Mair

INTRODUCTION The world has experienced a likely irreversible transition, wherein for the first time in human history, older adults (65+ years) will outnumber children (18 years or younger) (National Institute on Aging, 2012). This transition has already slowly occurred in Europe and the US and is happening rapidly in Latin America and Asia. Therefore, the next century will bring unprecedented challenges in promoting quality of life (QOL), particularly among ageing populations. Accomplishing the task of promoting QOL among expanding ageing populations cross-nationally is further complicated by a multitude of factors known to impact QOL that vary substantially across geographic contexts such as countries and regions. This includes cultural, economic and other factors such as normative beliefs about caregiving, policies to support older adults, resource availability, transportation, infrastructure and older adults’ comfort and safety. Although the ability of scholars to study these multilevel geographic factors is improving due to wider availability of data, greater theoretical work is needed to conceptualise and operationalise geographic context in terms of its relation to social, cultural, economic and political characteristics. Currently, geographic data linkages are limited to pre-existing divides along country, regional and local political designations that typically fail to reflect more deeply rooted local identities that transcend formally drawn boundaries and naturally occurring physical boundaries (such as rivers, train tracks, mountains, deserts, etc.). Two examples in the US at the macro-regional level would be the Mississippi Valley and the Appalachian region, both of which have distinct cultural identities, histories, socio-economic challenges and geographic characteristics – yet transcend formal and sometimes arbitrary state and federal census boundaries. Some progress has been made to align geographic data in more meaningful directions at micro- and meso-regional levels – an example is the socio-spatial neighbourhood estimation method (Cutchin et al., 2011) – and could be replicated at macro levels of scale (country, region). The current lack of meaningful geographic distinctions in regional identifiers not only obscures valuable nuance, it also creates additional error by grouping dissimilar people and communities together across arbitrary boundaries. This could result in washing out effects that would otherwise be observed with accurate regional distinctions. To truly understand QOL challenges among ageing populations and how they are shaped by and vary across spatial contexts, more nuanced forms of macro- and micro-regional identification are needed to link and compare data (see Chapter 6). While health geographers have started to address these issues, greater consensus is needed on how to approach the issue of context conceptually in order to apply it to additional disciplines, such as gerontology. 387

388  Handbook of quality of life research The chapter begins with a review of the current gaps in contextual concepts and data availability. Following that conceptual foundation, empirical data is presented from an analysis of the World Values Survey that demonstrates contextual variation in older adults’ QOL across countries, across regions within countries and across states within the US. Yet, this example raises more questions than it answers. To address these questions, a new approach is proposed for the creation of standardised regional identifiers that are constructed with macro and micro data from multiple disciplines, including geography, history, policy and sociology. As data availability increases, the chapter discusses new opportunities and challenges for QOL research that can lead to the development of more refined and nuanced conceptual and empirical tools for understanding how to promote well-being among older adults – a rapidly expanding and potentially vulnerable population cross-nationally.

GLOBAL AGEING AS A MULTIDISCIPLINARY QOL CHALLENGE As discussed above, demographically, the world is experiencing an unprecedented age transition, which has implications for QOL, with older adults set to outnumber children (National Institute on Aging, 2012). Across the Western world, countries are now classified as ‘super aged’. The demographic, socio-cultural, health-related forces behind this historic shift are lower fertility and longer life expectancy. Across Latin America and Asia, countries are experiencing this transition extremely rapidly. The impact of this shift on QOL risk factors for older adults will vary by geographic context and related factors such as resources, culture and policies. The speed of the transition will cause distinct challenges – socially, culturally and economically. As the COVID-19 pandemic has demonstrated, no country currently has adequate resources to support an influx of vulnerable older adults in need of medical, financial and family support. This challenge transcends many disciplines and presents QOL challenges not only in the field of gerontology, but also in all other disciplines centred on promoting QOL. Therefore, global ageing populations are a multidisciplinary challenge and opportunity for QOL researchers from a variety of academic backgrounds. Although gerontologists and demographers have long expressed concern about this age transition, the upcoming challenges are not insurmountable. We have at our disposal decades of research about how to promote older adults’ QOL. This includes work from: ● sociology on how to support families caring for ageing family members; ● public policy about best practices for retirement and healthcare; and ● epidemiology about how to promote high-quality end-of-life experiences. The gerontological pursuit is, by definition, a multidisciplinary effort to promote QOL, particularly in the later years of life. To address the upcoming, likely irreversible, shift toward ageing populations, it will be crucial to have input from as many health-related and QOL-focused disciplines as possible. Increased Availability of and Necessity for Contextual Data One of the greatest opportunities in modern research on QOL is the ever-expanding availability of contextual or objective data at multiple levels, including the individual, household,

Conceptualising contextual variation in older adults’ quality of life cross-nationally  389 neighbourhood, city, state/province, national and global levels. Considering the total geographic global spread of ageing populations, contextual data is a necessary component of making accurate, useful and practical recommendations for promoting QOL. Contextual data can take a range of forms and can be used more qualitatively to enrich the discussion of results, or more quantitatively to link to existing data – such as individual-level survey data – and become part of the statistical analysis of ageing and QOL. Scholars might analyse survey data but also review separate data on larger patterns to assist in interpreting the results. For example, a study of childless older adults might analyse not only individual-level survey data, but also review national statistics on fertility to discuss alongside the individual-level analysis findings. For linked data, an identifier variable must be used to merge individual-level data with country-level data. For example, the same individual-level study of 30 countries could then formally link national statistics as a ‘level 2’ via the name of each country or a country identifier number. The same logic is often used for neighbourhood-level data, wherein individual-level survey responses on subjective assessments of neighbourhood conditions can be linked to objectively measured crime statistics in that geographic area through the use of a linking variable (for example, address, census tract number, neighbourhood name, etc.). Linking data via an identifier expands the options for analysis beyond the individual level using surveys. It enables accounting for social structures such as community-level or country-level data on crime, solidarity, cultural values, demographic trends, public policy, transportation options, wealth, public spending, public opinion or any other topic of interest. Another advantage of data linkages is the ability to compare more objective indicators of environmental context (for example, crime rate from formal report data) with subjective indicators provided by respondents in survey data (using, for example, Likert-scale questions assessing respondents’ perceptions of crime in their area). With the proliferation of available data (often at minimal cost) in the Internet age, studies using linked contextual data have exploded in number over the last decade. These numerous opportunities allow researchers to examine previously unexplored questions about the influence of environmental context factors on older adults’ QOL. However, the endless analysis options it presents has also led to a lack of coherent consensus on how to conceptualise context. Furthermore, the measurement level at which this ‘wealth’ of contextual data is available is limited by the level of data collection and presentation. For example, in the US, rich data exists at different geographic levels corresponding to the decennial census data collection (US Census, 2021a). Census blocks are the smallest available area of measurement and correspond roughly to a city block but can be larger if there is lower population density. Blocks are aggregated into block groups of 500–3000 households and assigned a unique identifier. Block groups can be further aggregated into tracts, of which there may be several per county. Globally, data exists at the country level (for example, data collected and made available by international organisations such as the United Nations and World Health Organization). However, whether these are useful or meaningful geographic levels at which to link is highly debatable. Contextual Analyses of QOL in Gerontological Research Previous research indicates that older adults’ QOL is impacted by geographic context and by the many characteristics associated with geographic context – for example, physical infrastructure, social environment, resource availability and walkability (for a review, see Burgard

390  Handbook of quality of life research et al., 2021). A majority of the existing work on contextual influences on QOL among older adults includes comparisons by continent (for example, Asia, Europe), within continent (for example, Southern versus Northern Europe), across countries (within a continent or across continents), within a single country (rural versus urban) or within a metropolitan area (such as neighbourhoods within a city). Geography and gerontology have yet to fuse forces in a powerful way to address spatial and contextual health issues for older adults’ QOL, but research to do so has grown in recent years. Common indicators used to assess QOL in gerontology include life satisfaction, self-rated health, happiness or trust in others as a form of social QOL. Examples of such research include the following: ● Kino et al. (2021) note the lack of correlation between health and happiness in Japan and South Korea and explore cultural and economic explanations for this pattern. ● Although not focused on older adults, Burns and Crisp (2021) similarly note variation in well-being predictors across regions. ● Li et al. (2015) report differences in life satisfaction among Chinese older adults in rural versus urban areas, with urban older adults expressing higher life satisfaction. ● A recent study by Wilkinson et al. (2017) offers an overview of the potential for using contextual data for ageing and health research. Their research reviews issues of data linkage, conceptually driven choices of contextual data and available options for data linkages via geocoding in specific datasets. The study focuses on the US-based Health and Retirement Study (HRS, 2021), noting that a failure to incorporate more local context may underestimate inequalities, but the authors are limited to the geographic options available in the HRS, such as US Census boundaries. Secondary data sources for a contextual analysis of older adults’ QOL on a global scale are somewhat limited. One of the most promising is the Gateway to Global Aging (Lee et al., 2021), which includes about 20 separate cross-national ageing surveys designed to be linked together to create harmonised cross-national comparisons in over 40 countries (20+ countries in the European Union, plus Israel, as well as the US, Mexico, England, Costa Rica, South Korea, Japan, Republic of Ireland, China, India and Malaysia). These linkable surveys are focused on older adults’ QOL in terms of physical, mental, economic and social health, and are modelled after the HRS. One of these linked surveys is the Survey of Health, Ageing and Retirement in Europe (SHARE, 2021), which includes an ever-expanding list of at least 20 EU countries. SHARE also includes within-country regional divisions referred to as nomenclature of territorial units for statistics (NUTS), which are only available in Europe and are not available for all regions, especially those with smaller populations (Eurostat, 2021). In addition, the HRS has the option to obtain protected zip code data for analysing census-defined boundaries. Other US datasets – such as the National Health and Nutrition Examination Survey (NHANES) – also have the ability to link to census identifiers. Other countries have their own versions of these nationally representative datasets on older adults that can be linked to data at the more local province, state and tract level. Within a given city, there are some examples of datasets that may have neighbourhood linkages to investigate access to resources. For example, the author has had the privilege of working with the Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS, 2021) dataset based in Baltimore City, Maryland, and has started work to link the

Conceptualising contextual variation in older adults’ quality of life cross-nationally  391 individual-level survey data with neighbourhood-level objective indicators for a more contextual view of ageing adults’ health inequities in the city. In addition to these more gerontology-specific examples, additional datasets exist with greater age range that could be used for ageing QOL research by limiting the dataset to older adults. Two examples include the International Social Survey Programme (ISSP, 2021) and the World Values Survey (WVS; Haerpfer et al., 2022).

THE WORLD VALUES SURVEY DATASET One of the most thorough, detailed and inclusive datasets available cross-nationally is the WVS. Although not designed to study ageing and QOL, this dataset includes over 120 countries and represents 94.5 per cent of the global population via national random probability sampling designs with country samples ranging from 1000 to 3200 respondents aged 18 and older from all inhabited continents around the globe (Haerpfer et al., 2022). The survey is focused on topics such as social, political, economic and religious and cultural values of people in the world to assess socio-economic development and well-being over time and across countries. With data spanning 40 years (1981–2021) and over 600 indicators, it is one of the most comprehensive cross-national social science datasets available in the world. This dataset is also unique because it includes currently available options for regional identification as well, including the International Organization for Standardization (ISO) codes or countries and their subdivisions (ISO 3166; see ISO, 2021) as well as US Census codes for states and regions (Federal Information Processing Standards, FIPS; US Census, 2021b). To showcase the many data options available in the WVS as well as data limitations and challenges present even in this impressive dataset, a brief descriptive analysis of a handful of measures related to QOL and ageing by country and region follows. Data Overview The author accessed the most recently available wave of data at the time of writing this chapter (Wave 7), via the WVS website, and retained only those respondents aged 50 and older to focus on older adults cross-nationally. Wave 7 data collection began in 2017 and was interrupted by the COVID-19 pandemic (Haerpfer et al., 2022). At the time of this analysis, Wave 7 includes 77 countries. For the analysis, two to three countries per World Bank global region were randomly selected to yield a sample of 16 countries for the analysis. Of those 16, only one country experienced data collection during the COVID-19 pandemic (Canada) and two countries had partial final data collection in March 2020 (Ethiopia and Zimbabwe) at the time of this analysis. Countries were further divided into urban and rural areas to assess potential differences within country (Table 25.1). In addition, 18 states from the US (data collection April and May of 2017) were selected by choosing states with the largest sample sizes in each US Census geographic region (US Census, 2021a). Due to small sample sizes after stratifying by states (Table 25.2), the data were not further stratified by urban and rural location. Although in previous years, scholars would need to link any additional country-level data ‘by hand’ from outside sources such as the World Bank, recent WVS waves now include some commonly used country-level indicators on economic, social and physical well-being.

Ethiopia

Total

124

 

N

(5) Latin America & Caribbean

Chile

Total

384

 

 

 

N

 

 

44.40

19746.44  

GINI

GDP

60

Life expectancy 80.04

Source: The author.

Mexico

 

 

 

324

 

 

169

53815.37  

45.40

74.99

619

 

 

 

72

 

 

 

450

 

 

193

 

 

 

41

 

 

83

53601.00  

31.90

80.89

809

Greece

 

 

 

726

 

 

152

 

 

 

92

 

 

174

 

 

 

446

 

 

459

Russia

 

 

235

27043.94  

37.50

72.66

727

 

 

 

492

 

 

121

 

 

 

771

 

 

221

49756.31  

34.40

82.75

1097

China

 

 

 

876

 

 

71

 

 

 

166

 

 

 

 

 

527 667

 

 

35

Japan

 

 

67

 

 

 

738

Rural Urban

41429.29  

32.90

84.21

805

 

 

 

318

Rural Urban

24226.15  

31.80

78.88

353

Rural Urban Total

16116.70  

38.50

76.70

1194

Rural Urban Total

Australia

Lebanon Rural Urban Total

14717.35  

29.50

70.45

237

(7) East Asia & Pacific

10881.17  

41.40

78.54

Rural Urban Total

 

 

 

1284 892

Iraq

(4) Middle East & North Africa Rural Urban Total

United States Rural Urban Total

49031.38  

33.80

81.95

1743

Rural Urban Total

30314.57  

33.10

81.79

620

Rural Urban Total

Germany

Canada

(3) North America Rural Urban Total

4690.48  

33.50

67.11

244

(6) Europe & Central Asia

4753.73  

32.40

72.32

234

Rural Urban Total

2835.95  

Rural Urban Total

 

 

2219.71  

44.30

GDP

 

 

 

35.00

61.20

236

GINI

 

308

 

38

Pakistan

Rural Urban Total

Bangladesh

(2) South Asia

Rural Urban Total

Life expectancy 66.24

86

Rural Urban Total

Zimbabwe

(1) Sub-Saharan Africa

 

Cross-national sample characteristics by country and rural/urban context (WVS, Wave 7, 2017–20)

 

Table 25.1

392  Handbook of quality of life research

Conceptualising contextual variation in older adults’ quality of life cross-nationally  393 Table 25.2  

West

 

Pacific

US sample sizes by region and state (WVS, Wave 7, 2017–20) Midwest Mountain

W.N. Central

E.N. Central

 

 

 

 

 

WA

CA

CO

AZ

MO

WI

IL

MI

 

 

N

24

91

23

25

21

24

41

29

 

 

New England

W.S. Central

 

Northeast

 

Mid-Atlantic

South

 

OH

NJ

NY

PA

MA

TX

FL

GA

NC

VA

N

36

24

37

39

25

64

72

17

30

23

S. Atlantic

Source: The author.

Analysis conducted by the author included commonly used measures that help contextualise cross-national comparisons. These included: ● life expectancy (total years at birth; World Bank, 2018); ● the GINI index of economic inequality (latest available, World Bank, 2012–19; World Bank, 2019a) for which higher values indicate more inequality within a country); and ● gross domestic product (GDP) per capita purchasing power parity (PPP) (constant 2017 international dollars; World Bank, 2019b). These characteristics, along with total sample size per country and rural/urban sample sizes, are presented in Table 25.1. Measures To explore potential geographic contextual variation in QOL at multiple geographic scales (countries and rural/urban region within countries as well as states within the US), the author also included four variables from the WVS that have the potential to assess physical, mental and social well-being with all items recoded so that higher values indicate greater QOL. These included: ● ● ● ●

life satisfaction (scale of 1–10); self-rated health (0–3); happiness (0–3); and trust in neighbours (0–3).

For the purposes of descriptive analysis, the analysis focused on the highest category of self-rated health (‘very good’), happiness (‘very happy’) and trust (‘completely trust’). Analysis Figure 25.1 compares average life satisfaction (1–10) by country, by rural/urban residence (Figure 25.1A) and by region and states within the US (Figure 25.1B). Figure 25.2 displays the percentage in each country (and rural/urban region) that report ‘very good’ self-rated health (Figure 25.2A); being ‘very happy’ (Figure 25.2B); and ‘completely trust’ their neighbours (Figure 25.2C). Figure 25.3 presents the same data by US state.

394  Handbook of quality of life research

Source: The author.

Figure 25.1

Older adults’ life satisfaction cross-nationally and in the US (WVS, Wave 7, 2017–20)

Life satisfaction across multiple contexts Figure 25.1A displays variation by rural and urban region, with most countries illustrating higher life satisfaction in urban areas and some (for example, Iraq) showing higher average life satisfaction in rural areas. At the country level, the lowest average life satisfaction is observed in Ethiopia, Zimbabwe, Iraq, Greece and Russia, which likely reflects socio-economic disparities across countries (for example, lower GDP and life expectancy; Table 25.1). In Figure 25.2B, the US South and East North Central Midwest have slightly lower life satisfaction, although the range within the selected US states is clearly narrower than that observed across countries.

Conceptualising contextual variation in older adults’ quality of life cross-nationally  395

Source: The author.

Figure 25.2

Older adults’ health, happiness and trust cross-nationally (WVS, Wave 7, 2017–20)

QOL by country and rural/urban context Figure 25.2 compares additional QOL related measures cross-nationally and demonstrates

396  Handbook of quality of life research a lack of correlation across indicators. For example, Mexico, Greece and Japan display a lack of consistency between self-rated health (Figure 25.2A) and happiness (Figure 25.2B). Mexico and Japan report a lower percentage of older adults who report ‘very good’ health, but a higher percentage who are ‘very happy’. On the other hand, Greece has the opposite pattern, with higher percentages of older adults reporting ‘very good’ health but lower percentages who are ‘very happy’. Trust in neighbours (Figure 25.2C) – an indicator of local social QOL – does not have a clear correlation with either health or happiness. Iraq is an interesting example because it has the highest percentage of older adults who ‘completely trust’ their neighbours (Figure 25.2C), yet has one of the lowest percentages of older adults in ‘very good’ health (Figure 25.2A). Other examples include very low trust for neighbours in Japan and Australia, despite high health and happiness. About half the countries indicate a substantial gap in trust in rural versus urban areas, typically with older adults in rural areas indicating higher trust, although older adults in rural Germany report lower trust than those in urban Germany. These examples of variation underscore the complexity and multidimensional nature of QOL indicators. Sometimes the QOL outcomes are uncorrelated with each other and also there is a wide range of unexplained variation across the outcomes as well as within outcomes when compared across multiple scales of geographic context. QOL by US regions and states Figure 25.3 displays health, happiness and trust by US region and state. Health and happiness are generally correlated state by state, with lower levels of health (Figure 25.3A) and happiness (Figure 25.3B) experienced among older adults in the South (especially Georgia). Michigan has the lowest percentage of older adults reporting ‘very good’ health as well as lower levels of trust. Interestingly, Massachusetts reports the highest percentage of older adults in ‘very good’ health (Figure 25.3A), yet it is markedly lower in both happiness (Figure 25.3B) and trust (Figure 25.3C). Missouri is unique with the highest percentage of older adults who are ‘very happy’ (almost half; Figure 25.3B), as well as the highest percentage who ‘completely trust’ neighbours (Figure 25.3C), but is about average in self-rated health (Figure 25.3A). Older adults in Florida are relatively healthy and happy (Figure 25.3A and 25.3B), but report lower levels of trust in neighbours (Figure 25.3C). Pennsylvania is happy and reports high trust, but it is lower in health, while Illinois appears to score high on all measures. While this evident heterogeneity in outcomes is likely driven by individual-level differences (for example, within-community variation among individuals regarding health, happiness, health behaviours, cultures), there may also be regional contexts that shape responses to these questions, especially in terms of trust. Discussion The above brief descriptive analysis is not intended to identify contextual predictors of QOL, but rather to highlight an example of what can and cannot be deduced from currently available data about older adults’ QOL. This large, comprehensive, cross-national dataset can be broken down by age groups to focus only on older adults or compare them to younger adults. It can also be stratified by country, rural/urban region, and states and provinces within each country. However, sample sizes become small quickly even with simple stratifications by age and rural/ urban region or state (Tables 25.1 and 25.2). Although the Gateway to Global Aging dataset

Conceptualising contextual variation in older adults’ quality of life cross-nationally  397

Source: The author.

Figure 25.3

Older adults’ health, happiness and trust in the United States (WVS, Wave 7, 2017–20)

has larger sample sizes, it suffers from the same limitations in terms of the lack of data on within-country regional differences.

398  Handbook of quality of life research In the WVS example, heterogeneity is clearly visible, most of which likely exists at the individual level, and some of which can be observed at large global regional levels and country levels as well as within country regions, states and rural/urban locations. This heterogeneity would likely be even greater with larger sample sizes, and it may also be that the somewhat arbitrary divisions between countries and states can skew commonalities that transcend those formal boundaries. In other words, we have increasing amounts of data, but we often cannot connect it to meaningful contextual levels that would be useful to understand how risks to older adults’ QOL vary across geographic contexts at multiple scales

CHALLENGES Missing Link(ing) Variables Despite successes in identifying factors linked with older adults’ QOL cross-nationally, analysis using contextual data presents distinct conceptual and empirical challenges. If the goal is to examine context in contextual analysis, researchers are only able to scratch the surface with the conceptual and analytic tools that are currently available. Although we have data identifiers at the country, regional, state/province and local political levels (for example, census and zip code in the US), many of these delineations are arbitrary. They do not provide linkable information on more macro concepts, such as regional identity, or more micro details, such as local resources in one’s perceived neighbourhood area. At this point we are limited to a finite set of pre-determined standardised identifiers that are often divorced from any practical reality that may influence QOL. The geographic, spatial and health literature has acknowledged these concerns and a lot of recent work in the social geographic literature has focused on how to measure space. This work has included grappling with related questions such as how to move away from arbitrary indicators, and what options might exist with the proliferation of modern data sources in light of the uncertain geographic context problem (see Kwan, 2012; Shmool et al., 2018; Zhao et al., 2018). For example, Lee and Rogers (2019) directly address what they refer to as the unit question, which is how to choose the appropriate sub-level to analyse within a given nation. Kwan (2018) similarly highlights the challenges of using contextual data and urges a movement away from standard arrangements of data in space–time. As Fowler et al. (2020) note, there exists a contextual fallacy, wherein commonly used US Census geographies simply cannot appropriately measure context. These authors suggest some straightforward alternatives for reworking division on US data that account for how populations cluster demographically. Mennis and Yoo (2018) propose a stronger emphasis on microenvironment profiles, particularly in health research. Petrović et al. (2020) even argue for a freedom from the ‘tyranny’ of neighbourhood and propose the use of microgeographic data to map context, urging for a move away from simply using the currently available convenient indicators toward theory-driven data exploration. Curtis and Curtis (2019) identify these types of contextual concerns as the next frontier in health geography. Yet, the lack of a standardised identifier continues to be an issue – especially with studies each constructing their own alternative, which fuels a reproducibility and replicability crisis in studies of geography and QOL (Kedron et al., 2021).

Conceptualising contextual variation in older adults’ quality of life cross-nationally  399 Proposal: Standardised Interdisciplinary Contextual Identifiers To address limitations of current geographic identifiers, the initiation of a multidisciplinary effort is needed. This would involve combining historical, political, socio-cultural, economic, geographic and demographic data to generate multiple levels of standardised regional identifiers that represent real divisions in macro regions within countries as well as more micro divisions within towns and cities. These identifiers can be used across disciplines – and especially in gerontology. Such an effort would be time-intensive, require interdisciplinary cooperation and would be designed to be repeated at a set time interval to adjust for changes occurring as real-life boundaries in regions and localities inevitably shift over time. This task should ideally be undertaken with global coordination to enable use of the same methodologies and measures of QOL to ultimately facilitate more rigorous cross-national comparisons. For example, in the US, this proposed new contextual identifier would not follow state or census boundaries, but rather would pull together data on large and small geographic formations (such as mountains, rivers, plains, coasts), infrastructural divisions (such as highway proximity and access, divisions created by roadways and railways), socio-historical divides (such as racial/ethnic regional distinctions), cultural identities (obtained through survey and demographic data), resource availability (proximity to healthcare options) and local area characteristics (such as in the neighbourhood). Health geographers have recently proposed some practical solutions to the problem of inaccurate spatial boundaries. This includes use of more detailed geographic information systems (GIS) mapping alongside survey data (Shmool et al., 2018), microgeographic data (Mennis and Yoo, 2018; Petrović et al., 2020), high-resolution geographic data, and daily activity data (Kwan, 2018; Mennis and Yoo, 2018). Yet, the uncertain geographic context problem (Kwan, 2012; Shmool et al., 2018; Zhao et al., 2018) is present in a multitude of social and health science disciplines, including but not limited to geography. Therefore, the solution will require conceptual and methodological coordination across disciplines to help ensure standardisation and replicability across studies, regions and countries. This approach would mean a commitment to move away from the use of current standard regional identifiers (for example, FIPS, NUTS, ISO, census, etc.) for data linkages. These identifiers, while convenient, are geographically-spatially-culturally arbitrary and can perpetuate inaccurate conclusions about social groups. With an array of data from multiple disciplines, scholars could begin to overlay information and start to map real-life national, regional and local distinctions that may provide insight into QOL and the heterogeneity observed in key components that comprise QOL such as life satisfaction, health, happiness and trust. Although we should not try to distil the complexity of context into an oversimplified framework, a standardised approach that integrates perspectives from many disciplines is critical to the advancement of interdisciplinary research fields such as QOL. Many disciplines have already cultivated deep knowledge on different aspects of this puzzle that could potentially be combined to formulate a method for creating standardised identifiers that represent real-life macro- and micro-regional geographic distinctions that shape QOL risks and opportunities.

400  Handbook of quality of life research Contextual Conceptualisations and Ageing Experiences as a QOL Research Opportunity Implementation of the approach outlined above would offer the potential to expand our options for maximising data in new and unique ways that are also, ideally, more realistic. For example, gerontologists would be able to view the specific risks and resources available to an ageing individual within their own accessible and identifiable space, rather than in their state or census tract. We could further compare similar types of regions across countries in attempting to isolate sources of heterogeneity that may be the result of cultural or policy differences. Such an approach would benefit from recent innovations within the field of health geography and integrate them into an interdisciplinary framework. The resulting new contextual measurement options could be used to guide development of community- and region-specific health interventions designed to promote QOL among older adults in those areas. In summary, there remains vast unexplained heterogeneity in comparative QOL research – especially in gerontology. There is also a lack of replicability and application of new geographic tools due to their complexity and narrow disciplinary focus. To address these issues, there is an urgent need to depart from arbitrary contextual units of analysis that obscure real-world risk factors to poor QOL. The arriving explosion in ageing populations worldwide brings a unique opportunity to combine efforts from multiple disciplines to formulate a standardised identifier for assessing context and QOL.

REFERENCES Burgard, S., Montez, J.K., Ailshire, J. and Hummer, R.A. (2021), ‘Aging policy from a multilayered geographic and life course perspective’, Public Policy and Aging Report, 31, 3–6. Burns, R. and Crisp, D. (2021), ‘Examining the complexity of wellbeing profiles in a large cross-national community sample’, International Journal of Wellbeing, 11, 24–43. Curtis, A. and Curtis, J. (2019), ‘The next frontier in health geography: context and implications for interventions’, International Journal of Environmental Research and Public Health, 16, 1457–59. Cutchin, M.P., Eschbach, K., Mair, C.A. and Goodwin, J.S. (2011), ‘The socio-spatial neighborhood estimation method: an approach to operationalizing the neighborhood concept’, Health & Place, 17, 1113–21. Eurostat (2021), ‘History of NUTS’, https://​ec​.europa​.eu/​eurostat/​web/​nuts/​history (accessed 1 February 2022). Fowler, C.S., Frey, N. and Folch, D.C. et al. (2020), ‘Who are the people in my neighborhood? The contextual fallacy of measuring individual context with census geographies’, Geographical Analysis, 52, 155–68. Haerpfer, C., Inglehart, R. and Moreno, A. et al. (eds) (2022), World Values Survey: Round Seven – Country-Pooled Datafile Version 5.0, Madrid and Vienna: JD Systems Institute and WVSA Secretariat. Health and Retirement Study (HRS) (2021), HRS Data Portal, https://​hrsdata​.isr​.umich​.edu/​ (accessed 1 February 2022). Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) (2021), ‘Healthy Aging in Neighborhoods of Diversity Across the Life Span’, https://​handls​.nih​.gov/​(accessed 1 February 2022). International Organization for Standardization (ISO) (2021), ‘ISO 3166: Country codes’, https://​www​ .iso​.org/​iso​-3166​-country​-codes​.html (accessed 1 February 2022). International Social Survey Programme (ISSP) (2021), ‘The International Social Survey Programme’, www​.issp​.org (accessed 1 February 2022).

Conceptualising contextual variation in older adults’ quality of life cross-nationally  401 Kedron, P., Frazier, A.E. and Trgovac, A.B. et al. (2021), ‘Reproducibility and replicability in geographical analysis’, Geographical Analysis, 53, 135–47. Kino, S., Jang, S.-N. and Kawachi, I. (2021), ‘Healthy but unhappy? Cross-national comparative analysis of depressive symptoms in Japanese vs. Korean elders’, Archives of Gerontology and Geriatrics, 95, Article 104426. Kwan, M.-P. (2012), ‘How GIS can help address the uncertain geographic context problem in social science research’, Annals of GIS, 18, 245–55. Kwan, M.-P. (2018), ‘The limits of the neighborhood effect: contextual uncertainties in geographic, environmental health, and social science research’, Annals of the American Association of Geographers, 108, 1482–90. Lee, D.W. and Rogers, M. (2019), ‘Measuring geographic distribution for political research’, Political Analysis, 14, 1–18. Lee, J., Phillips, D. and Wilkens, J. (2021), ‘Gateway to Global Aging data: resources for cross-national comparisons of family, social environment, and healthy aging’, Journals of Gerontology: Social Sciences, 7(Suppl. 1), S5–S16. Li, C., Chi, I. and Zhang, X. et al. (2015), ‘Urban and rural factors associated with life satisfaction among older Chinese adults’, Aging and Mental Health, 19, 947–54. Mennis, J. and Yoo, E.E. (2018), ‘Geographic information science and the analysis of place and health’, Transactions in GIS, 22, 842–54. National Institute on Aging (2012), Global Health and Aging, https://​www​.nia​.nih​.gov/​sites/​default/​ files/​d7/​nia​-who​_report​_booklet​_oct​-2011​_a4​_​_1​-12​-12​_5​.pdf (accessed 1 February 2022). Petrović, A., Manley, D. and Van Ham, M. (2020), ‘Freedom from the tyranny of neighbourhood: rethinking sociospatial context effects’, Progress in Human Geography, 44, 1103–23. Shmool, J.L.C., Johnson, I.L. and Dodson, Z.M. et al. (2018), ‘Developing a GIS-based online survey instrument to elicit perceived neighborhood geographies to address the uncertain geographic context problem’, The Professional Geographer, 70, 423–33. Survey of Health, Ageing and Retirement in Europe (SHARE) (2021), ‘Welcome to SHARE’, https://​ share​-eric​.eu/​(accessed 1 February 2022). US Census (2021a), ‘Census regions and divisions of the United States’, https://​www2​.census​.gov/​geo/​ pdfs/​maps​-data/​maps/​reference/​us​_regdiv​.pdf (accessed 1 February 2022). US Census (2021b), ‘American National Standards Institute (ANSI) and Federal Information Processing Series (FIPS)’, https://​www​.census​.gov/​library/​reference/​code​-lists/​ansi​.html (accessed 1 February 2022). Wilkinson, L., Ferraro, K.F. and Kemp, B.R. (2017), ‘Contextualization of survey data: what do we gain and does it matter?’, Research in Human Development, 14, 234–52. World Bank (2018), ‘Life expectancy at birth, total (years)’, https://​data​.worldbank​.org/​indicator/​SP​ .DYN​.LE00​.IN (accessed 1 February 2022). World Bank (2019a), ‘Gini index (World Bank Estimate)’, https://​genderdata​.worldbank​.org/​indicators/​ si​-pov​-gini/​(accessed 1 February 2022). World Bank (2019b), ‘GDP per capita, PPP’, https://​data​.worldbank​.org/​indicator/​NY​.GDP​.PCAP​.PP​ .KD (accessed 1 February 2022). Zhao, P., Kwan, M.-P. and Zhou, S. (2018), ‘The uncertain geographic context problem in the analysis of the relationships between obesity and the built environment in Guangzhou’, International Journal of Environmental Research and Public Health, 15, Article 308.

PART VII A LOOK AHEAD

26. Overview and future directions Robert W. Marans, Robert J. Stimson and Noah J. Webster

WHAT THE HANDBOOK HAS ACHIEVED Interest in quality of life (QOL) may be traced back to ancient times, but research into QOL and related issues has proliferated since the 1960s, with hundreds of studies appearing in copious literature. QOL and related issues have also attracted significant policy interest, including how various interventions might enhance people’s QOL and their well-being. So diverse is the literature on QOL that, for the purposes of this Handbook, we have chosen to concentrate on discussing QOL explicitly in a place or space context, taking account of the environmental situation in which people live. The Handbook set out to provide the reader with an overview of research investigating QOL and related issues. As outlined in the introductory chapter, QOL seems to defy a precise definition, it being a diverse and complex topic attracting researchers from a wide range of disciplines who brought numerous perspectives on how to go about investigating QOL. The chapters in Part I provided an overview of QOL and related issues such as well-being and happiness, and suggest how QOL research can inform public policy and planning. They also introduce the reader to measurement approaches and empirical investigations that are evident in the study of QOL. One of the chapters explicitly focused on approaches used to study happiness. Another discussed how principles in environmental psychology are being used to understand the impact of the environment on people’s QOL. The chapters in Part II offered a more detailed review of theoretical perspectives and methodological approaches used by researchers across many disciplines to investigate QOL/ well-being. Methodological approaches – particularly in the context of place and spatial dimensions of QOL – include the use of: (1) objective measures of what is real, ‘on the ground’, or gleaned from official records; and (2) subjective measures reflecting people’s responses to or perceptions of those places and their QOL, which involves the use of survey research methods to generate primary data about people and households. These objective and subjective approaches may be combined through an integrated approach to undertaking QOL research, which has generated a range of conceptual models to measure QOL and investigate the complex linkages between subjective and objective variables in explaining QOL outcomes. In Parts III and IV, the chapters examined QOL issues with respect to a range of explicit situational or environmental contexts, such as big cities (including a high-density Asian urban setting, Hong Kong, in one of four case studies mentioned in Chapter 10), a suburb or neighbourhood within a city, new towns, and rural and small town settings. Additionally, one chapter examined how interregional migration and amenity factors might relate to QOL. The chapters used case studies to illustrate how the investigations were undertaken. The chapters in Part V focused explicitly on discussing how people’s QOL might be affected by, and potentially enhanced by, the provision of and access to open space, and through adopting nature-based solutions in planning and designing urban spaces. 403

404  Handbook of quality of life research In Part VI of the Handbook, the chapters focused explicitly on the QOL/well-being of older people, the fastest-growing age demographic globally. The chapters do that with respect to a number of situational settings. There was also some discussion of how health and other interventions might enhance older adults’ QOL. An important issue in QOL research is how policy, planning and design might enhance people’s QOL and the well-being of people and communities, an issue considered explicitly in Chapter 1 as well as in several of the chapters throughout the Handbook.

LIMITATIONS In selecting the topics for and the contributors to the Handbook, we readily admit that our coverage is not as comprehensive as we would have liked, in large part because of our own biases as well as page limitations. For example, there is a vast literature covering happiness research that goes beyond what was presented in Chapter 3 (see for example, Frey, 2011; Lyubomirsky, 2008). This literature has recently been expanded through the World Happiness Reports, which examined the roles played by physical as well as social environments in understanding happiness in different parts of the world (see Castelli et al., 2023; Helliwell et al., 2020). Similarly, the Handbook does not fully address the vast literature on community indicators (see, for example, Gahin and Paterson, 2003; Phillips, 2005; Sirgy et al., 2009), nor does it consider in detail the links between the socio-spatial aspects of sustainability and QOL (see Martinez et al., 2021). Clearly, readers will find other limitations in what we assembled for the Handbook. However, we have ensured that the contributors come from a range of disciplinary backgrounds and have published research investigating QOL. We believe the chapters offer a wide sampling of current work related to QOL from a place and space perspective.

LOOKING AHEAD We suspect that over the coming years, the above omissions will be examined by others in a new and updated Handbook about QOL, focusing on the contributions of place and space. In doing so, we anticipate that material presented in this volume will serve as a foundation for those future initiatives, primarily in terms of research designs and methodologies. It is clearly evident that there will be much more to write about research approaches that integrate or use both subjective and objective measurements of QOL. Operationalizing integrated model frameworks to investigate QOL is certainly being enhanced with the advancement of geographic information systems (GIS), a tool discussed in Chapter 2 and elsewhere in the Handbook. There is likely to be more widespread adoption of new technologies that enhance or add new dimensions to QOL research designs. For instance, we are likely to see more studies that use objective measures of health outcomes or bio-measures (such as C-reactive protein, blood pressure) that are collected from participants and can be linked with their survey data (see Sakshaug et al., 2015), as well as objective data about their environment. Additionally, with regard to research designs, we anticipate that more QOL studies will take a longitudinal perspective where QOL is assessed in a particular place or geographic area at different points in time as that place and environmental elements within it change. These

Overview and future directions  405 changes may occur either through natural maturation or by designed interventions. Ideally, such studies would consider changing QOL assessments among populations with similar socio-demographic characteristics (that is, age groups, household income levels, ethnicities, etc.), but also follow the same participants over time. The latter would require combining a representative sample of those living within a geographic area with a panel of individual participants whose QOL and their contextual environments would be assessed at multiple time points. Overcoming the current paucity of longitudinal studies of QOL will require substantial levels of funding as longitudinal research designs are complex, require large sample sizes and continuity of multidisciplinary research teams. As shown in Chapter 13 and suggested in Marans et al. (2021), future research designs are likely to take a sequential approach to understanding QOL. That is, subject populations will become engaged early in the research process, either through focus group sessions or through more informal discussions. This early involvement should become a common initial step prior to more standardized forms of data collection through social surveys and environmental measurements. As the digital world continues to grow, it is likely that web-based surveys will become the standard for measuring people’s QOL experiences. Two primary advantages of web-based surveys over both face-to-face interviews and mail or drop-off questionnaires is that they require a shorter time frame for conducting the research and can be completed at a lower cost (see Couper, 2008). Similarly, they are appropriate during shock events – such as the COVID-19 pandemic – for reasons of safety. On the other hand, web-based surveys become problematic in places where illiteracy is high and Internet access low. Yet, literacy rates and Internet usage have dramatically increased during the past decade and are likely to continue improving (Ritchie et al., 2023; UNESCO Institute for Statistics, 2022). Finally, web-based surveys are advantageous in that the turnaround time to administer questionnaires and report findings is comparatively shorter than for surveys conducted by other means, and therefore may become more relevant to the interests of policymakers or other users of the data. In earlier studies, which involved face-to-face-questionnaires, supplemental environmental measurement was important. This would involve trained interviewers observing and measuring attributes of the environment in or around respondents’ dwellings. With web-based surveys, this form of measurement is not possible. Nonetheless, there are other approaches to observing and measuring environmental attributes associated with a participant’s place of residence. Often, early survey work involving consideration of place and space relied on aerial photography or detailed maps to obtain measures of the environment associated with the participants’ residence. Measurements included attributes such as vegetation coverage, size of property and distances between the residence and nearest non-residential facilities including schools, parks, grocery stores, and so forth. The availability of GIS and use of satellite imagery through Google Earth are now considered standard approaches for such measurements (see, for example, Clarke et al., 2010; Harding et al., 2020). More recently, drone technologies have also been used for gathering environmental data ranging from natural landscapes and ecosystems to building clusters and traffic counts. As technology improves, and as the cost of drones becomes less expensive, it is likely that this approach to gathering and monitoring changes in environmental settings will increase.

406  Handbook of quality of life research Similarly, other technologies involving sensors have been used in connection with social surveys in an exploratory manner (see English et al., 2022). With funding from the US National Science Foundation, a team from the University of Chicago installed more than 140 sensor nodes in neighbourhoods throughout the city of Chicago to collect micro-environmental conditions such as weather, air quality, noise levels from vehicle traffic and human activity, and traffic flow (Catlett et al., 2017). The environmental data collected were then linked to survey data covering populations in those neighbourhoods to examine relationships between participants’ behavioural patterns and health problems. In discussing their work, the researchers conclude that beyond findings from their Chicago-based project, advances in sensing and computer technologies have the potential to inform the study of any behavioural and social phenomena where environmental context matters (English et al., 2022, p. 191). Clearly, the potential for these technologies in conducting QOL research and in examining the importance of place and spatial phenomena is great.

FINAL WORD In this concluding chapter, we have re-emphasized the importance of understanding the diversity and complexity of QOL and the approaches that researchers have used in its measurement and investigation. The Handbook explicitly set out to demonstrate how studying QOL has been addressed from a place and space perspective, taking account of the explicit environmental situations in which people live. We have offered a glimpse into the future in terms of research designs and prospects for future measurement with respect to people’s evaluations and assessments of QOL and its components, including objective measures of the built and natural environment. We believe that these represent new and important opportunities for scholars with interests in public policy, the environmental design professions and others concerned about the well-being of humankind.

REFERENCES Castelli, C., d’Hombres, B. and de Dominicis, L. et al. (2023), ‘What makes cities happy? Factors contributing to life satisfaction in European cities’, European Urban and Regional Studies, 30, 319–42. Catlett, C.E., Beckman, P.H., Sankaran, R. and Galvin, K.K. (2017), ‘Array of things: a scientific research instrument in the public way: platform design and early lessons learned’, in Association for Computing Machinery (ACM), Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, New York: ACM, pp. 26–33. Clarke, P., Ailshire, J. and Melendez, R. et al. (2010), ‘Using Google Earth to conduct a neighborhood audit: reliability of a virtual audit instrument’, Health & Place, 16, 1224–9. Couper, M.P. (2008), Designing Effective Web Surveys, New York: Cambridge University Press. English, N., Zhao, C. and Brown, K. et al. (2022), ‘Making sense of sensor data: how local environmental conditions add value to social research’, Social Science Computer Review, 40, 179–94. Frey, B.S. (2011), ‘Happy people live longer’, Science, 331, 542–3. Gahin, R. and Paterson, C. (2003), ‘Community indicators: past, present, and future’, National Civic Review, 90, 347–61. Harding, A.B., Glynn, N.W. and Studenski, S.A. (2020), ‘Interrater reliability of historical virtual audits using archived Google street view imagery’, Journal of Aging and Physical Activity, 29, 63–70.

Overview and future directions  407 Helliwell, J.F., Layard, R., Sachs, J.D. and De Neve, J.-E. (2020), ‘Environments for happiness: an overview’, in J.F. Helliwell, R. Layard, J.D. Sachs and J.-E. Neve (eds), World Happiness Report 2020, pp.  3–12, https://​worldhappiness​.report/​ed/​2020. Lyubomirsky, S. (2008), The How of Happiness: A Scientific Approach to Getting the Life you Want, New York: Penguin. Marans, R.W., Gerber, E. and Morenoff, J. (2021), ‘Detroit Area Studies (DAS)’, in F. Maggino (ed.), Encyclopedia of Quality of Life and Well-Being Research, 2nd edition, Cham: Springer, pp. 1–6. Martinez, J., Mikkelsen, C.A. and Phillips, R. (2021), Handbook of Quality of Life and Sustainability, Cham: Springer. Phillips, R. (ed.) (2005), Community Indicators Measuring Systems, Burlington, VT: Ashgate. Ritchie, H., Mathieu, E., Roser, M. and Ortiz-Ospina, E. (2023), ‘Internet’, Our World in Data, https://​ ourworldindata​.org/​internet. Sakshaug, J.W., Ofstedal, M.B., Guyer, H. and Beebe, T.J. (2015), ‘The collection of biospecimens in health surveys’, in T.P. Johnson (ed.), Handbook of Health Survey Methods, Hoboken, NJ: Wiley, pp. 383–19. Sirgy, J.M., Phillips, R. and Rahtz, D. (eds) (2009), Community Quality-of-Life Indicators: Best Cases IV, New York: Springer. UNESCO Institute for Statistics (UIS) (2022), ‘Literacy rate, adult total (% of people ages 15 and above)’, https://​data​.worldbank​.org/​indicator/​SE​.ADT​.LITR​.ZS​?view​=​chart.

Index

12 questions of the UK General Health Questionnaire (GHQ-12) 29–30 access to urban services and facilities 96 adaptation models 86–7 affordances for connectedness 316 age heterogeneity 377 ageing in place 323, 340 age moderating effect on QOL/well-being 380 agent-based models 88–9 aggregated observed indicator 218 air pollution 295, 310 see also environmental degradation air quality on QOL: Hong Kong case study context GIS 112 hybrid model 112 measurement of air quality 111–12 COVID-19 pandemic, during 125 discussion limitations 124 overview 123 health impact 124–5 methodology 112 air quality and other data 113–14 research hypotheses 113 statistical analysis 114–16 one-way analysis of variance base model (model 1) 114, 115 extended version of base model (model 2) 114, 115 K-means clustering (model 3) 114–16 PM2.5 data 113, 125 regression analysis in K-means regression (model 3) 121–3 ordinal logistic regression (model 1) 117 ordinal logistic regression (model 2) 117–21 results descriptive analysis 116–17 regression analysis 117–23 Akerlof, G.A. 28 Albers, T. 308 Albouy, D. 269 ALECS study see Hong Kong Active Lifestyle and the Environment in Chinese Seniors (ALECS) study Allin, P. 75 aloneness vs. loneliness 340

amenities 8–9 effects on migration 264–5 distance as deterrent to movement of people (model 1) 265 human-created amenities (model 3) 266 natural amenities (model 2) 265–6 natural and human-created amenities (model 4) 267 nominal wage (model 5) 267 real wages (model 6) 267, 268 unemployment rates (model 7) 267, 268 human 263, 269 location-specific amenity effects 268 measurement: standard scores 262–3 migration 341 natural 262–3, 269 trade-off approach between wages and 269 Amicone, G. 47 Andrews, F.M. 14 Anglo communities 234 Antonucci, T.C. 375 Applied Research in Quality of Life (journal) 2, 7 Aristizabal, S. 313 Arrow, K.J. 28 Aslam, A. 26 Atalik, G. 100 attention restoration theory 279 Ayalon, L. 349 Bakolis, I. 46 Ballas, D. 26 Barker, R.G. 326 Baron, R.M. 134, 191 barriers to neighborhood use 283–4 Barrington-Leigh, C. 29 Bauer, R.A. 62 Bharadwaj, P. 54 big city Brisbane-south East Queensland see Brisbane-south East Queensland region QOL study Detroit region see Detroit QOL study Hong Kong see Hong Kong QOL study Istanbul see Istanbul QOL study bio-ecological model 324 biophilia 316 Blomquist, G.C. 70, 100 Bonaiuto, M. 187, 193, 308 bottom-up model 84–5

408

Index  409 Brereton, F. 226 Brisbane-south East Queensland region QOL study 148, 162 attributes that may impact people’s QOL 155 issues investigated 154 methodology 153–4 overall life satisfaction 155 Brodeur, A. 40 Bronfenbrenner, U. 323, 325 Brown, B. 188 Brown, L.A. 88, 88 built environment 168 Bureau of Labor Statistics (BLS) 264 Burns, R. 390 Büter, K. 330 Campbell, A. 6, 19, 83, 106, 107, 147, 201, 206 Campbell, N.M. 342, 348 Carley, M. 79 Carlino, G. 260 Carp, F.M. and Carp, A. 325 CCRCs see continuing care retirement communities (CCRCs) Centro Interuniversitario di Ricerca in Psicologia Ambientale1 (CIRPA) projects 46, 48 Charles II (King) 53 China new programme for QOL research on new towns background 207–8 methodology 208–9 new research initiative, overview 208 proposed long-term programme 210–11 results 209–10 summary of findings 210 Choi, H. 373 Christensen, A. 234 chronosystem 324 Cicerchia, A. 70, 101 cities agencies undertaking rankings of Economist Intelligence Unit 72 Mercer Quality of Living Ranking 73 Monocle magazine 71–2 ratings and ranking of cities and QOL 71 Clark, D. 260 Clean Air Act (1956) 54 climate 381 cluster analysis 68 coastal resilience 308 Cohen, J. 191 community well-being and resilience in rural region 244–5 conceptual framework 246 methods used in collecting data 248–50

research questions and hypotheses 247–8 importance over time (RQ2) 251–2 levels over time (RQ1) 250–51 methods used in collecting data measures 249–50 sample 248–9 statistical tests and modelling 250 relating levels and importance over time (RQ3) 252–3 research questions and hypotheses 247–8 satisfaction level and importance of dimensions combined (RQ4) 253–4 social aspects 255 Western Downs region 245–6 commute satisfaction 173 compact city model 165 compact city to SWB, pathways from 165, 174–7 emotional responses 168 health 168 implications for policy 177 job satisfaction 167 leisure satisfaction 167 life domains 166–8 residential well-being 167–8 social relationships 167 travel satisfaction 166–7 comparative fit index (CFI) 221 Comprehensive Quality of Life Scale (ComQol) 15, 18 computer-assisted telephone interview (CATI) 131, 248 ComQol see Comprehensive Quality of Life Scale (ComQol) confirmatory factorial analysis (CFA) model 218–22 configural model between groups 220–21 distance from town centres and housing density 222 PREQI factor and parcel means and standard deviations 219–20 SEM models 222 connectedness to nature 316 continuing care retirement communities (CCRCs) belonging needs 343–4 care environment independent living 345–6 measures of 350–51 P-CAT 350 person-centred care 345 Person-centred Climate Questionnaire 350–51 total institution 345 defined 340

410  Handbook of quality of life research measures of QOL specifically designed for older adults OPQLQ 346–7 WHOQOL-OLD 347 physical environment Campbell questionnaire 348 person–environment fit 342 QOL measures focusing on 347–9 SCEAM 348–9 service quality 348 physiological needs 342 privacy and security needs 343 QOL of older adults 339–41 autonomy 341 care environment 345–6 impaired physical functionality 340 loneliness vs. aloneness 340 migration patterns types 341 physical environment 342 social environment 344–5 social interactions 342 Successful Social Space Attribute Model and importance of design 342–4 security needs 343 social environment community belonging 345 egocentric measures 349 loneliness 344 QOL measures focused on 349–50 SC-5PT 350 sense of community index 350 social relations 344–5 sociocentric networks 349 social interactions 342 Converse, P.E. 6, 19 convoy model expanded additional adaptations needed 376–7 to incorporate environmental context 374–6 of social relations 374 Cornwell, T. 193 Corrado, L. 26 Costanza, R. 5, 17, 18 Coughlin, R.E. 68 COVID-19 air quality on QOL: Hong Kong case study 125 migration 269–70 creative class 9 Crisp, D. 390 CRM see cumulative risk model (CRM) cross-national: older adults’ QOL 387–8

challenges contextual conceptualisations and ageing experiences as QOL research opportunity 400 missing link(ing) variables 398 standardised interdisciplinary contextual identifiers 399 global ageing as multidisciplinary QOL challenge 388 contextual data 388–9 gerontological research, contextual analyses in 389–91 World Values Survey (WVS) dataset analysis 393–6 data overview 391–3 discussion 396–8 measures 393 Culyer, A.J. 63 Cummins, R.A. 14, 15, 18, 87, 155 cumulative risk model (CRM) CIRPA project, in 48 physical factors and psycho-social conditions 47 positive psychology framework 48 risk factors 47–8 Curl, A. 357 Curtis, A. 398 Curtis, J. 398 Custers, M.H.G. 311 Dalla, R.L. 234, 235 Darçin, M. 295 Dasgupta, P.S. 28 dementia-friendly design 325, 327 Detroit Area Study 296 Detroit Metro Area Communities Study (DMACS) 162 Detroit QOL study 148, 149, 162 analysis, feedback and ongoing work findings, overview of 153 interrelationships, examining 152 methodology 149–50 objective information from secondary sources 151–2 questionnaires face-to-face interviews 150–51 mail questionnaires 151 Dictionary of Human Geography QOL defined 4 well-being defined 5 Diener, E. 6, 14, 61, 79, 87 distance-decay models 259 domain satisfactions direct, indirect and total effects of compactness 174–6

Index  411 SWB and 169–70 domains of life approach 6, 14–15, 19, 80–81, 159–60 domestic wastewater treatment systems 310 Dong, D. 113 drop-off questionnaires 405 Duesenberry, J.S. 28 Duncan, O.D. 13 ecological momentary assessment (EMA) 46 effective functioning 276, 278, 279, 281, 289, 311 elevators 261 emotional distress 310 emotional well-being 166, 168 ENABLE-AGE project 328 Encyclopedia of Quality of Life and Well-being Research 2 environmental affordances 276–7 environmental context 81–2 environmental degradation 304–6 air pollution 295 defined 294 individual characteristics neighbourhood satisfaction 299–303 QOL 303–4 negative impacts on human well-being 295 neighbourhood satisfaction and QOL 295 correlation analysis 299–301 descriptive statistics 297–9 hierarchical regression analysis I 299–303 hierarchical regression analysis II 303–4 individual characteristics effects 299–304 methodology 296–7 perception of environmental degradation 296 respondent characteristics 297 results 297–304 subjective quality of life 296–7 survey 296 perception of 296 wildlife habitat loss 305 environmental factors 2, 13, 48, 56, 112, 182, 325, 329, 355, 357, 368 environmental gerontology 323–5 environmental preferences 276 environmental psychology direct and indirect (mediated) effects (principle 2) 50–51 examples 51–2 mediation model 51 principles in environment–person transactions 45 direct and indirect (mediated) effects (principle 2) 50–53

short- and long-term exposure, with immediate and chronic effects: principle 3 53–5 valence, generalisability, set (principle 1) 46–50 short- and long-term exposure, with immediate and chronic effects (principle 3) 53–5 Great Smog of London 53–4 implications 55–6 long-term impacts 54 recent research 54–5 valence, generalisability, set (principle 1) cumulative risk model 47–8 examples 46–7 moderation model 49–50 planning, implications for 50 simple case 46 Environment in Asia Scan Tool-Hong Kong (EAST-HK) 360 Evans, S. 94 Evelyn, J. 53 experienced neighbourhood perceived neighbourhood social support 186 physical activity 186–7 place attachment 185–6 residential satisfaction 185 satisfaction 185 satisfaction with distance to amenities 186 social involvement 186 exploratory factor analyses (EFA) 281–2 face-to-face interviews 150–51, 156, 296, 360, 405 ‘felicific calculus’ measurement system 2 Fernandez, L.E. 88, 89 Ferrer-i-Carbonell, A. 295 Fik, T. 269 Finlay, J. 375 Fisher, A.T. 82 fixed factor method 222 Fleche, S. 40 Fleming, R. 331 Florida, R. 9 ‘Fordist’ economy 230 forest bathing 311 forest bathing phenomenon 51 Fornara, F. 215, 218 Forsyth, A. 74 Fowler, C.S. 398 Frankl, V.E. 5 Frank, L.D. 74 Fried, M. 183, 185 full information maximum likelihood (FIML) 222

412  Handbook of quality of life research Gaussian variance 361 gender moderating effects and living arrangements on environment 366–7 moderator of environment, as 364–5 PA as mediator of moderating effects of gender and living arrangements on environment 366 generalised additive mixed models (GAMMs) 360 geocoding 23, 390 geographic information systems (GIS) 112, 218, 399 analysis 132 enhancing modelling using 23 tools findings, summary of 105–6 implications 106 model 104–5 structural equation modelling 103–4 study methodology 102 testing hypotheses 102–3 geography of happiness in UK 26–7 data GHQ-12 29–31 household income 30, 32 empirical results gender and employment status differences 33–4 regression modelling 34–40 GHQ-12 29–30 methodology 30–32 Geography of Social Well-Being in the United States: An Introduction to Territorial Social Indicators, The 62 gerontology 387 Gibson, J.J. 276 Gibson, L. 129 GIS see geographic information systems (GIS) Gitlin, L.N. 323 Global Liveability Ranking 72 Godschalk, D. 205 Goffman, E. 345 Golledge, R.G. 87 Google Earth 405 Gowdy, J.M. 295 Great Smog of London 53–4 green exercise 313–14 Greenley, J.R. 18 green social spaces 283 green spaces 313 Gross, B.M. 62, 63 gross migration 259 Groves, W.B. 97

Hagerty, M.R. 148 Hall, J.F. 147 Hand, D.J. 75 happiness described 6 internal and external factors 7 research on 6 socio-spatial inequalities perspective see socio-spatial inequalities perspective: happiness Hausman test 30 Hayes, A.F. 282 health 168 health outcomes, prevention against risks to 311 Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) 390 hedonic approach 16 hedonic price equations 70 HEW Indicators (1966) report 61 HEW Trends (1966) report 61 hierarchical linear regression analysis 191 hierarchical regression model 236 Hillman, A. 202 home environment conceptualization docility hypothesis 325 P–E interchange 325–6 described 323 hospital vs. 333–4 older adults hospitals and 332 role of home environment 327–9 see also older adults homeliness 343 homophily 98 Hong Kong air quality on QOL see air quality on QOL: Hong Kong case study ALECS see Hong Kong Active Lifestyle and the Environment in Chinese Seniors (ALECS) study QOL study see Hong Kong QOL study Hong Kong Active Lifestyle and the Environment in Chinese Seniors (ALECS) study 358–9 data analysis aim 1 361 aim 2 361 aim 3 361–2 mediation analyses 362–3 measures covariates and moderators 361 exposures: neighbourhood attributes 359–60 mediator: physical activity 360 outcomes: QOL domains 360

Index  413 study design 359 Hong Kong QOL study 148, 162 data analysis 158 findings, overview of aspects of QOL, satisfaction with 159–60 effect of sets of urban attributes 161 living domains, satisfaction levels with 160 multivariate analysis of satisfaction 160–61 overall QOL, satisfaction with 158 methodology anomie and social capital 158 Public Opinion Programme 157 subjective and objective measures used 159 survey questionnaire 157–8 QOUL survey questionnaire 157 Hong Kong Quality of Urban Life (QOUL) project 157 hormonal system 311 hospital environment conceptualization behaviour setting 326–7 interior design 327 spatial design of floor plan 327 theory of affordances 327 home vs. 333–4 older adults homes and 332 and role of hospital environment 329–30 see also older adults household income 29 housing developments 215 human amenities 263 Huxley, P. 94 hybrid model 112 Ifcher, J. 40 immigration migration see migration NIDs 232–3 immunological system 311 independent living 345–6 individual characteristics and environmental degradation on neighbourhood satisfaction 299–303 on quality of life 303–4 informal public gathering places 129 in-migrants 258 in-migration 233, 238–9 Innes de Neufville, J. 13 Institute for Scientific Information (ISI) database 2

instorative processes attitudes and behaviour, positive changes in 314–15 nature and other species, increased connection with 315 physical and mental health 313–14 social cohesion 314 integrative approach 15–16 fulfilling human needs in relation to subjective well-being 17–18 six-dimensional framework 17 subjective satisfaction approach 16–17 see also subjective and objective measures integration in QOL/well-being Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 295 International Physical Activity Questionnaire (IPAQ) 188 International Social Survey Programme (ISSP) 391 International Society for Quality-of-Life Studies (ISQOLS) 2, 147 inverse hyperbolic sine (IHS) transformation 31 ISR University of Michigan in the US 80, 99, 101 Istanbul QOL study 148, 162 findings, overview of 156–7 issues investigated 156 methodology 155–6 Ivory, V.C. 142 Jacobson, M.Z. 53 job satisfaction 167, 176 ‘jobs followed people’ 260 Johnson, L.B. 61 joint-significance test 361 Kahana, E. 325 Kahneman, D. 86 Kapteyn, A. 28 Kemp, D. 96 Kenny, D.A. 134, 191 Kino, S. 390 K-means clustering (Model 3) 114–16 Kubzansky, L.D. 375 Kwan, M.-P. 398 Kweon, B.-S. 153 labour market implications 27 Land, K.C. 63 Latin America see urban layout on perceived residential quality in Costa Rican suburb Lawton, M.P. 342, 344, 374 Layard, R. 28 Lazarsfeld, P.F. 98

414  Handbook of quality of life research Lee, D.W. 398 leisure satisfaction 167 Lenzi, C. 8 levels of scale 80, 81, 84 Levkovich, I. 349 Ley, A. 73, 74 Ley, D. 9 Li, C. 390 life domains 166–8 life satisfaction 5, 80, 81, 85, 86, 166, 278 likelihood ratio test (LRT) 222 Lippert, A.M. 52 liveability defined 73 sustainable development and 74 loneliness vs. aloneness 340 in older adults’ QOL 382 Lora, E. 16 Lowe, C.T. 157 Lowry, I. 263 Luechinger, S. 295 Luttmer, E.F.P. 31 mail questionnaires 151, 405 Marans, R.W. 9, 16, 20, 22, 23, 61, 63, 73, 75, 96, 106, 107, 149, 153, 206, 405 marital status 380–83 Marquardt, G. 323 Maslow, A. 18 Mason, P. 357 McCall, S. 6 McCrea, R. 16, 75, 80, 84–8, 94, 96–8, 100, 102, 103, 105–7, 246 McKay, H.D. 97 McVeigh, T. 62 measurement of air quality 111–12 amenities 262–3 ‘felicific calculus’ system 2 numerical and modelling social well-being absolute and relative indicators 66 modelling framework 66–7 meat packing industries 232–5, 239 mediated path structural model 104–5 mediating analysis of satisfaction and use 285 nearby building-dominated spaces 287–9 nearby green social spaces 287 nearby open lawns 287 perceived barriers to use 289 quality of public spaces, satisfaction with 289 results 285–9 mediating effects 73, 285, 358, 368 mediation analysis 134, 138

Meersman, S. 188 Melbourne study discussion 142–3 limitations 143 methodology analytic strategy 133–5 covariates 134 dependent variables 134 objective built environment measures 132–3 perceived or subjective measures 131–2 sample 130–31 results descriptive statistics and correlations for social and built environment 135–7 frequency of social interactions 138 mediation analysis 138 objective built environment measures 138 social interaction satisfaction 138–41 socio-demographic characteristics of sample 135 Mennis, J. 398 Mercer Quality of Living Ranking 73 Merriam-Webster Dictionary QOL defined 4 Merschdorf, H. 23 Merton, R.K. 98 metropolitan areas Brisbane-south East Queensland see Brisbane-south East Queensland region QOL study Detroit region see Detroit QOL study Hong Kong see Hong Kong QOL study Istanbul see Istanbul QOL study Oslo see Oslo case study Michalos, A.C. 83, 86, 99 Michelson, W. 188 migration 8–9, 205, 207, 210 see also US metropolitan areas, migration across Mills, E. 260 Miraftab, F. 233 mobility 258 modelling frameworks operationalising integrative approach 18–19 GIS 23 models domain satisfaction and life satisfaction 19, 20 domain satisfaction and QOL 19–21 objective conditions, subjective responses and neighbourhood satisfaction 20, 21

Index  415 testing hypotheses 22–3 models adaptation 86–7 bottom-up 84–5 mood bias 85–6 residential relocation 87–8 subjective importance 87 subjective judgement 86 top-down 85 moderation model 49–50 Mohai, P. 22 Molinario, E. 314 monozygotic twins 52 mood bias models 85–6 Moore, E.G. 87, 88 Moore, W.E. 62 Moser, C. 83 Mouratidis, K. 172, 173 Mulligan, G. 260, 269 multiple regression analysis 134–5, 158 Murphy, C. 260 National Health and Nutrition Examination Survey (NHANES) 390 National New-type Urbanization Plan (China) 207 natural amenities 262–3 natural environments 96 nature-based solutions (NBS) 315–16 conceptual framework 308–9 components 309 harm reduction 309–11 QOL instoration 313–15 QOL restoration 311–13 defined 308 harm reduction 309–10 air pollution 310 health and well-being, prevention against risks to 311 noise pollution 310–11 water quality 310 QOL instoration attitudes and behaviour, positive changes in 314–15 nature and other species, increased connection with 315 physical and mental health 313–14 social cohesion 314 QOL restoration affective restoration 312 cognitive restoration 312–13 physiological and stress recovery 311–12 NBS see nature-based solutions (NBS)

nearby defined 282 neighbourhood 192–4, 218 amenities 184–5 attributes 359–60 built environment see neighbourhood social and built environment characteristics and QOL in later life see neighbourhood characteristics and QOL in later life experienced 185–8 main study mediation analyses 191 method 190 predicting overall QOL 190–91 personal neighbourhood mediating contextual neighbourhood 193 physical 183–5, 187 preliminary study analysis 189–90 demographics 188 experienced neighbourhood 188 method 188–9 physical neighbourhood 187 QOL 188 QOL and 182–3 experienced neighbourhood 185–8 initial model 183 main study 190–91 physical neighbourhood 183–5, 187 preliminary study 187–90 urban neighbourhoods 191–2 urban 191–2 neighbourhood characteristics and QOL in later life 372–3 findings limitations and future directions 383–4 main effects 381–2 moderating effects 382–3 living apart/far from family 373 study, present analysis 379 data 377–8 heterogeneity 377 measures 378–9 results, summary of 379–81 sample descriptive statistics 380 theoretical foundations additional adaptations needed for expanded model 376–7 convoy model of social relations 374 expanded version of convoy model to incorporate environmental context 374–6 neighbourhood environment 355–6

416  Handbook of quality of life research ALECS study see Hong Kong Active Lifestyle and the Environment in Chinese Seniors (ALECS) study physical environmental attributes 356–7 QOL and associations between attributes of 357 moderation and mediation of environment 357–8 social environmental 356–7 neighbourhood physical elements see physical neighbourhood neighbourhood safety 357, 379–82 neighbourhood satisfaction 173, 185, 289–90 with distance to amenities 186 environmental degradation and QOL 295 correlation analysis 299–301 descriptive statistics 297–9 hierarchical regression analysis I 299–303 hierarchical regression analysis II 303–4 individual characteristics effects 299–304 methodology 296–7 perception of environmental degradation 296 respondent characteristics 297 results 297–304 subjective quality of life 296–7 survey 296 green social spaces, use 283 nearby nature, use and well-being 279–80 and barriers to use: two-way associations 283–4 data analyses and findings 282–3 EFA 281, 282 measures 281–82 mediating roles of satisfaction and use 285–9 perceived nearby nature 284–5 sense of peacefulness and effective functioning 281 study sample 281 and outdoor settings bi-directional associations 278 environmental affordances 276–7 multidimensionality 277–8 nature settings and people’s needs 276 needs and preferences, meeting 275–6 neighbourhood satisfaction and use of outdoor spaces 277 sense of well-being 278–9 proximity to nature 279 with quality of public space 283 residential satisfaction 185

neighbourhood social and built environment 128–9, 143 Melbourne study see Melbourne study research overview 128–30 neighbourhood social capital 357 neighbourhood social interaction socio-emotional selectivity theory 129–30 urban planning context 129 neighbourhood use 277, 278 net migration 259 new immigration destinations (NIDs) 232–3 Newton, P. 73, 74 new towns 199, 211–12 Asia, studies in 202–3 literature about QOL 203–4 new programme for QOL in China background 207–8 methodology 208–9 new research initiative, overview 208 proposed long-term programme 210–11 results 209–10 summary of findings 210 research on residents’ lives expectations of moving to new towns 205–6 new towns and new town residents over time 204–5 North Carolina work 204 physical attributes contributing to community satisfaction and health 206 youth and older adult populations 205 UK studies 200 US studies 200–201 Western Countries 201–2 Ng, E.C. 82 Nistico, H. 87 Nixon 62 noise pollution 310–11 Noll, H. 62 nomenclature of territorial units for statistics (NUTS) 390 non-housing developments 215 Nygren, C. 329 objective approach to QOL/well-being 74–5 attributes of neighbourhoods 14 indicators of quality of urban life agencies undertaking rankings of cities 71–3 issues 73 ratings and ranking of cities and QOL 71 weighted objective indicators 70 individual attributes 14

Index  417 liveability and QOL 73–4 numerical measurement and modelling social well-being 66–7 absolute and relative indicators 66 modelling framework 66–7 social indicators see social indicators objective environmental factors of air quality 113, 114, 120–21 objective indicators 6, 14–16, 19, 64, 70, 75, 100, 106, 125, 148, 157, 383, 389, 391 older adults 332 CCRCs see continuing care retirement communities (CCRCs) cross-national: QOL see cross-national: older adults’ QOL environmental gerontology relevant for behavioural and architectural science 324–5 home environment and hospitals 334 P–E belonging and P–E agency 328–9 private 327–8 surveillance zones 328 hospital environment empirical study 1: exploring overall relevance with dementia patients 330–31 empirical study 2: physical features in promoting or hindering activity for dementia patients 331–2 and home 332 role of 329–30 Older People Quality of Life Questionnaire (OPQLQ) 346–7 Oliver, N. 81 Omar, D.B. 203 Omnibus F-tests 250 online survey 131 optimal centrality theory 95 ordinary least square (OLS) regression 158 Orru, K. 295 Oslo case study analytical method commute satisfaction and job satisfaction 171–2 structural equation modelling 171 data sources 169 methodology analytical method 171–2 data sources 169 variables 169–71 model commute satisfaction 173 endogenous variables 172 neighbourhood satisfaction 173

self-reported health 173 socio-demographic variable 172–3 results 173–6 variables 169–71 descriptive statistics 170–71 distance to city centre 171 neighbourhood density 171 SWB and domain satisfactions 169–70 Oswald, F. 326, 328, 329 out-migrants 258 oxytocin 312 PA see physical activity (PA) Pacione, M. 17, 81, 94 Padeiro, M. 372 Parra, D.C. 357, 381 participant-generated photo-grouping 276 path analysis 102–6, 171, 251 pathways, compact city to SWB see compact city to SWB, pathways from P–E belonging 326 ‘people followed jobs’ 260 perceived residential environment quality indicators (PREQI) 215–16, 218 factor and parcel means and standard deviations 219–20 instrument 218 Perloff, H. 62 personality traits 83 person-centred care 345 Person-centred Care Assessment Tool (P-CAT) 350 Person-centred Climate Questionnaire Patient Version 350 Staff Version 351 person-environment fit model 342, 374 person-environment (P-E) interchanges 325–6 Perucca, G. 8 Petrović, A. 398 physical activity (PA) 355, 360 ALECS study see Hong Kong Active Lifestyle and the Environment in Chinese Seniors (ALECS) study mediator of environment–QOL associations 356–68 mediator of gender differences 368–9 mediator of moderating effects of gender and living arrangements on environment 366 sample characteristics 363–4 see also gender; neighbourhood environment physical neighbourhood 183–4, 187 affluence 184 amenity distance 184–5 appearance 184

418  Handbook of quality of life research busyness 184 physiological and stress recovery 311–12 Pinquart, M. 129 place identity 326 place liveability 74 see also community well-being and resilience in rural region place-making for QOL 323 place satisfaction see community well-being and resilience in rural region Plane, D. 263 planned residential environments 205 Poortinga, W. 188 population mobility 258 Porell, F. 263 Powell, A. 16 Preacher, K.J. 282 PREQI see perceived residential environment quality indicators (PREQI) ‘pressure–state–response’ (PSR) framework 69 primary data 79, 83 see also subjective QOL: survey methods principal components analysis (PCA) 67–8, 158, 189–91 or factor analysis 67–8 proximity to nature 279 psychological well-being 47, 50, 54, 125, 128, 185, 207 Public Open Space Tool (POST) 360 public transportation 381–2 Purandare, N. 331 Qinba Mountain Area (QMA) resettlement programme 207–8 QOL see quality of life (QOL) QOL associations living arrangements interaction effects on QOL, and environment by 364 PA as mediator of environment 367–8 as mediator of moderating effects of gender and living arrangements on environment 365–9 QOL/well-being integrative approach see integrative approach objective approach attributes of neighbourhoods 14 individual attributes 14 social indicators 13–14 subjective and objective measures see subjective and objective measures integration in QOL/well-being subjective approach described 14 domains of life approach 14–15

global evaluation of life 14–15 QOUL project see Hong Kong Quality of Urban Life (QOUL) project quality of life (QOL) 2–4 air quality: Hong Kong case study see air quality on QOL: Hong Kong case study amenity, migration and regional development 8–9 CCRCs, in see continuing care retirement communities (CCRCs) cross-national: older adults’ QOL see cross-national: older adults’ QOL definition 4 Detroit study see Detroit QOL study enhancing people’s QOL and community well-being 8 happiness see happiness Hong Kong study see Hong Kong QOL study multidimensionality 5 NBS and see nature-based solutions (NBS) neighbourhood see neighbourhood neighbourhood satisfaction and environmental degradation 295 correlation analysis 299–301 descriptive statistics 297–9 hierarchical regression analysis I 299–303 hierarchical regression analysis II 303–4 individual characteristics effects 299–304 methodology 296–7 perception of environmental degradation 296 respondent characteristics 297 results 297–304 subjective quality of life 296–7 survey 296 new towns and see new towns objective approach to see objective approach to QOL/well-being place and space context 7 policy, planning and 7–9 ratings and ranking of cities and 71 small town see small town subjective and objective indicators 4–5 subjective and objective measures integration in QOL/well-being see subjective and objective measures integration in QOL/well-being well-being see well-being see also QOL/well-being Quality of Life Questionnaire 18 quality of urban life (QOUL) 7, 148–9, 215

Index  419 Ramos, A.K. 234 Rangel, A. 295 Rantanen, T. 381 Recent Trends in the United States (1933) report 61 relative deprivation 28 relative income hypothesis (RIH) 28 relative social status 28 Republic, The (Plato) 2 residential neighbourhoods green outdoor spaces 290 see also neighbourhood satisfaction residential quality see urban layout on perceived residential quality in Costa Rican suburb residential relocation models behavioural approach 88 functionalist approach 87–8 residential well-being 167–8 restorative mechanisms affective restoration 312 cognitive restoration 312–13 physiological and stress recovery 311–12 Reuben, A. 52 Roback, J. 260 Rodgers, W. 6, 19, 106, 107, 206 Rogers, J. 258 Rogers, M. 398 Rogerson, P. 263 Rogerson, R.J. 9, 70, 75 Rojas, M. 28 Rosen, S. 260, 269 Rossi, P.H. 13, 87 rural areas see community well-being and resilience in rural region Ryan, L.H. 373 safety age in older adults’ QOL, by 382 in older adults’ QOL 381 salutogenic effects 309 Sampson, R.J. 97 Schieman, S. 188 Schwirian, K.P. 70 Seamon, D. 186 self-reported health 173 sense of community index 350 Service Quality (SERVQUAL) framework 347–8 Shaw, C.R. 97 Sheffield Care Environment Assessment Matrix (SCEAM) 348–9 Sheldon, E.B. 62 simultaneous regression analysis 191 Sirgy, M.J. 100, 193 situational/environmental context 2, 3, 5 small town case study 235–8

communities 232, 239 counter-urbanisation 230 definition 231 disadvantages 232 growth Anglo communities 234 community satisfaction and social ties 234 impacts 233–5 labour in-migration 233, 238–9 NIDs 232–3 social and cultural issues 234 in-migration 238–9 methodology analytic procedures 236 sample 235–6 nature 230–31 QOL 231–2, 238–9 results of case study correlation analysis 237 hierarchical regression analysis 238 partial correlations 237 satisfaction levels 236–7 rural gentrification 230 technology 239 Smith, D.M. 13, 62–8 social cohesion 314 social cohesion (SC-5PT) 350 social connectedness 278, 281 social disorganisation theory 97 social distance 28 social indicators 8 categories of objective social well-being used 64–6 defined 62–3 development encompassing QOL objective social indicators of well-being 147 subjective indicators 147–8 empirical applications of ‘Society at a Glance’ report by OECD 68–9 studies in US 67–8 Great Society 61–2 territorial 63–4 social interactions frequency of 134, 138 satisfaction 134, 138–41 social relationships 167 social welfare and income distribution 28 Society at a Glance report by OECD 68 sociocentric networks 349 socio-ecological models 355 socio-emotional selectivity theory 129–30 socio-spatial inequalities perspective: happiness geography see geography of happiness in UK

420  Handbook of quality of life research labour market implications 27 objective and the subjective approach 26 policy implications 40 well-being and, perspectives on economics 27 geographical studies 27–8 research 28–9 socio-spatial interdependencies 28 welfare economics research 28–9 socio-spatial neighbourhood estimation method 387 Sorensen, S. 129 Southeast Queensland see Brisbane-south East Queensland region QOL study spatial behaviour 258 spatial microsimulation technique 67 Stahl, S.T. 373 standard score additive model 67 StataCorp 135 status homophily 98 step-wise multiple regression analysis 68 Stimson, R.J. 16, 23, 61, 63, 73, 75, 87, 154, 157 stress recovery 311–12 structural equation modelling 102–4 sub-culture theory 98 subjective approach described 14 domains of life approach 14–15 global evaluation of life 14–15 subjective assessments/evaluations of QOL 14–15, 79–80 domains of life approach 80–81 QOL/well-being 4–6 situational/environmental context 81–2 survey research methods to collect data on subjective QOL 83–4 subjective importance models 87 subjective indicators 147–8 subjective judgement models 86 subjective and objective measures integration in QOL/well-being 94, 107 empirically investigating objective characteristics of urban environment 100–101 subjective evaluations of urban environment 99–100 environmental setting, theories relating to access to services and facilities 96 natural environments 96 optimal centrality theory 95 urban density and overloading 96 general theories 98–9 indicators 101–4 social environment, theories relating to social disorganisation theory 97

sub-culture theory 98 subjective perception of air quality 113, 114, 120–21 subjective QOL: survey methods assessment see subjective assessments/ evaluations of QOL data generation 83 modelling approaches agent-based modelling 88–9 broad conceptual model framework 84–8 systematic collection of subjective QOL/ well-being 83–4 subjective QOL/well-being see subjective and objective measures integration in QOL/ well-being subjective satisfaction approach 16–17 subjective social indicators 84 subjective well-being (SWB) 5–6, 27, 79, 80, 82–4, 165, 278 compact city and 166–8 direct, indirect and total effects of compactness 174, 176 domain satisfactions and 169–70 fulfilling human needs in relation to 17–18 Successful Social Space Attribute Model 342–4 Suh, E.M. 14, 61, 79 Sun, V.K. 381 surveillance zone 342 survey methods see subjective QOL: survey methods Survey of Health, Ageing and Retirement in Europe (SHARE) 390 territorial social indicators 14, 63–4 tertiary planning units (TPUs) 359 theory of homeostasis 87 ‘The Urban Mind’ app 46 top-down model 85 total institution 345 Toward a Social Report (1966) report 61–2 trade-off approach 269 Tranmer, M. 26 travel satisfaction 166–7 Türkoğlu, H. 156 Turksever, A.N.E. 100 UK Town and Country Planning Association (TCPA) 199 University of Michigan’s ISR 101 UN Millennium Ecosystem Assessment Board 294 urban amenities 8–9, 14 urban consumption experiences 9

Index  421 urban layout on perceived residential quality in Costa Rican suburb 214–15 context 216 findings latent factors 222–4 regression estimates 224–5 Flores canton map 217 housing developments 225–6 methodology and analysis CFA 218–22 invariance test 222 PREQI instrument 218 sample 218 SEM 218 PREQI 215–16, 218 study area Flores canton 216–17 residential development pattern 217 US Bureau of Economic Analysis (BEA) 264 US Clean Air Act (1970) 295 US metropolitan areas, migration across 268–70 amenities effects on migration 264–8 location-specific amenity effects 268 measurement: standard scores 262–3 continued importance 257–62 contributions to study of migration early 259 recent 260–61 COVID-19 269–70 data 264 described 258–9 distance-decay models 259 endogeneity problems 259 inter-metropolitan flows 269 model 263–4 origin–destination flows 258–9 population mobility 258 spatial behaviour 258 utility function 28 value homophily 98 Van den Berg, A.E. 311 Van den Berg, P. 129 Van Herwaarden, F.G. 28 Veblen, T. 28 Veenhoven, R. 6, 81 Veeroja, P. 375 virtual reality (VR) 31 Wahl, H.-W. 323, 326, 328, 374 Wald chi-squared tests 250

walkability 74 water quality 310 Webster, N.J. 374–6, 384 welfare economics research 28–9 well-being 5–6 attention restoration theory 279 community well-being and resilience in rural region see community well-being and resilience in rural region defined 5 effective functioning 279 enhancing people’s QOL and community well-being 8 objective approach to QOL/well-being see objective approach to QOL/well-being prevention against risks to 311 QOL vs. 6 sense of peacefulness 279 subjective and objective measures integration in QOL/well-being see subjective and objective measures integration in QOL/well-being SWB see subjective well-being (SWB) see also QOL/well-being Welsch, H. 295 wildlife habitat loss 295, 305 see also environmental degradation Wilkins, E.T. 54 Wilkinson, L. 390 Withey, S.B. 14 World Happiness Report 27 World Health Organization Quality of Life-OLD (WHOQOL-OLD) 347 World Values Survey (WVS) dataset analysis 393 country and rural/urban context, QOL by 395–6 life satisfaction across multiple contexts 394 US regions and states, QOL by 396 data overview 391–3 discussion 396–8 measures 393 US sample sizes by region and state 393 Yen, I.H. 372 Yoo, E.E. 398 Zehner, R.B. 200 Zhang, C.J.P. 357, 358 Zhou, J. 202 Zumbo, B.D. 99