Informed Urban Environments: Data-Integrated Design for Human and Ecology-Centred Perspectives (The Urban Book Series) 3031038029, 9783031038020

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The Urban Book Series

Ata Chokhachian Michael U. Hensel Katia Perini   Editors

Informed Urban Environments Data-Integrated Design for Human and Ecology-Centred Perspectives

The Urban Book Series Editorial Board Margarita Angelidou, Aristotle University of Thessaloniki, Thessaloniki, Greece Fatemeh Farnaz Arefian, The Bartlett Development Planning Unit, UCL, Silk Cities, London, UK Michael Batty, Centre for Advanced Spatial Analysis, UCL, London, UK Simin Davoudi, Planning & Landscape Department GURU, Newcastle University, Newcastle, UK Geoffrey DeVerteuil, School of Planning and Geography, Cardiff University, Cardiff, UK Jesús M. González Pérez, Department of Geography, University of the Balearic Islands, Palma (Mallorca), Spain Daniel B. Hess , Department of Urban and Regional Planning, University at Buffalo, State University, Buffalo, NY, USA Paul Jones, School of Architecture, Design and Planning, University of Sydney, Sydney, NSW, Australia Andrew Karvonen, Division of Urban and Regional Studies, KTH Royal Institute of Technology, Stockholm, Stockholms Län, Sweden Andrew Kirby, New College, Arizona State University, Phoenix, AZ, USA Karl Kropf, Department of Planning, Headington Campus, Oxford Brookes University, Oxford, UK Karen Lucas, Institute for Transport Studies, University of Leeds, Leeds, UK Marco Maretto, DICATeA, Department of Civil and Environmental Engineering, University of Parma, Parma, Italy Ali Modarres, Tacoma Urban Studies, University of Washington Tacoma, Tacoma, WA, USA Fabian Neuhaus, Faculty of Environmental Design, University of Calgary, Calgary, AB, Canada Steffen Nijhuis, Architecture and the Built Environment, Delft University of Technology, Delft, The Netherlands Vitor Manuel Aráujo de Oliveira , Porto University, Porto, Portugal Christopher Silver, College of Design, University of Florida, Gainesville, FL, USA Giuseppe Strappa, Facoltà di Architettura, Sapienza University of Rome, Rome, Roma, Italy

Igor Vojnovic, Department of Geography, Michigan State University, East Lansing, MI, USA Claudia Yamu, Department of Spatial Planning and Environment, University of Groningen, Groningen, Groningen, The Netherlands Qunshan Zhao, School of Social and Political Sciences, University of Glasgow, Glasgow, UK

The Urban Book Series is a resource for urban studies and geography research worldwide. It provides a unique and innovative resource for the latest developments in the field, nurturing a comprehensive and encompassing publication venue for urban studies, urban geography, planning and regional development. The series publishes peer-reviewed volumes related to urbanization, sustainability, urban environments, sustainable urbanism, governance, globalization, urban and sustainable development, spatial and area studies, urban management, transport systems, urban infrastructure, urban dynamics, green cities and urban landscapes. It also invites research which documents urbanization processes and urban dynamics on a national, regional and local level, welcoming case studies, as well as comparative and applied research. The series will appeal to urbanists, geographers, planners, engineers, architects, policy makers, and to all of those interested in a wide-ranging overview of contemporary urban studies and innovations in the field. It accepts monographs, edited volumes and textbooks. Indexed by Scopus.

More information about this series at https://link.springer.com/bookseries/14773

Ata Chokhachian · Michael U. Hensel · Katia Perini Editors

Informed Urban Environments Data-Integrated Design for Human and Ecology-Centred Perspectives

Editors Ata Chokhachian TUM School of Engineering and Design Technical University of Munich Munich, Germany Katia Perini Architecture and Design Department Polytechnic School University of Genoa Genoa, Italy

Michael U. Hensel Department for Digital Architecture and Planning Faculty of Architecture and Planning Vienna University of Technology Vienna, Wien, Austria

ISSN 2365-757X ISSN 2365-7588 (electronic) The Urban Book Series ISBN 978-3-031-03802-0 ISBN 978-3-031-03803-7 (eBook) https://doi.org/10.1007/978-3-031-03803-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book presents an inter- and transdisciplinary, as well as transscalar approach to thinking, planning, designing and adaptation of urban environments. The goal is to integrate data and data-based processes across disciplines and domains to advance the way in which current and future urban demographic and environmental dynamics and challenges can be met, thereby facilitating a decisive move towards informed urban environments. The idea for this book emerged from numerous conversations in the context of the international Architecture and Environment symposia1 series in which the editors of this volume developed the outline for the content and approach presented here within. In recent decades, researchers and practitioners in many disciplines, including architecture and urban planning, design and governance, have begun to understand and investigate the role that data can play in informing the way the human, and more specifically, the urban environment are understood, materialized and transformed. In this context Big Data, data analytics, data-based methods and decision support have gained increasing importance across different domains of urban planning, design and governance. However, while this indicates an encouraging development, the general trend and related efforts are mainly domain-specific, thereby tending towards silofication instead of cross-domain and inter- and transdisciplinary approaches. The latter therefore remain insufficiently explored and establish the gap which this book seeks to address. While the chapters are characterized by specific thematic foci and trajectories the authors seek nevertheless to uncover productive links between different disciplines and domains, thereby establishing a basis for further research and work en route to informed urban environments. Munich, Germany Vienna, Austria Genoa, Italy

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Ata Chokhachian Michael U. Hensel Katia Perini

https://www.dap.tuwien.ac.at/article/60350a750e7cca44ed7d7470. v

Contents

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The Introduction to Informed Urban Environments . . . . . . . . . . . . . . Ata Chokhachian, Michael U. Hensel, and Katia Perini

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The Bigger Picture en Route to Informed Urban Environments . . . . Michael U. Hensel

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How We See Now: Traversing a Data-Mosaic . . . . . . . . . . . . . . . . . . . . Billie Faircloth, Christopher Connock, Ryan Welch, Kit Elsworth, and Elizabeth Escott

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The Role of Information Modelling and Computational Ontologies to Support the Design, Planning and Management of Urban Environments: Current Status and Future Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cédric Pruski and Defne Sunguro˘glu Hensel

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Urban Adaptation—Insights from Information Physics and Complex System Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rui A. P. Perdigão Decoding Cool Urban Forms: Using Open Data to Build a Dialogue Between Microclimate and Configurational Morphology in Urban Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ata Chokhachian and Aminreza Iranmanesh

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From Amsterdam to New Amsterdam to Amsterdam: How Urban Mobility Shapes Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Tom Benson, Fabio Duarte, and Carlo Ratti

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Urban Microclimate Spatiotemporal Mapping: A Method to Evaluate Thermal Comfort Availability in Urban Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Daniele Santucci

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Urban Ecosystems and Nature-Based Solutions: The Role of Data in Optimizing the Provision of Ecosystem Services . . . . . . . . 145 Katia Perini

10 Smart Urban Forestry: Is It the Future? . . . . . . . . . . . . . . . . . . . . . . . . 161 Stephan Pauleit, Natalie Gulsrud, Susanne Raum, Hannes Taubenböck, Tobias Leichtle, Sabrina Erlwein, Thomas Rötzer, Mohammad Rahman, and Astrid Moser-Reischl 11 Big Data and Decision Support in Rural and Urban Agriculture . . . 183 Defne Sunguro˘glu Hensel Correction to: From Amsterdam to New Amsterdam to Amsterdam: How Urban Mobility Shapes Cities . . . . . . . . . . . . . . . . . . . Tom Benson, Fabio Duarte, and Carlo Ratti

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Editors and Contributors

About the Editors Ata Chokhachian, (Dr. -Ing.) is a research scientist, educator, and advisor in the domains of building technology and urban climate, developing and employing computational decision-making processes, tools, and workflows for architects and urban planners. Since 2015, he has been appointed as a research associate at the chair of Building Technology and Climate Responsive Design, as well as chair for Architecture Informatics at the Technical University of Munich. In 2022, he defended his Ph.D. dissertation in developing experimental and simulation-based tools to quantify outdoor thermal comfort conditions in urban environments. In the summer of 2019, he was appointed as a visiting research fellow at the Sustainable Design Lab at the Massachusetts Institute of Technology. In January 2020, he co-founded Climateflux, a company offering platforms for data-driven and computational workflows for acquiring climatic knowledge. Michael U. Hensel is an architect and partner in the architectural practices OCEAN net (www.ocean-net.org) and OCEAN Architecture|Environment (www.ocean-a-e. com). He taught at the Architectural Association School of Architecture in London from 1993 to 2009. From 2011 to 2018, he was the director of the Research Centre for Architecture and Tectonics at the Oslo School of Architecture and Design. Since 2018, he is a professor at the Faculty of Architecture and Planning at Vienna University of Technology and heads the research department for Digital Architecture and Planning (www.dap.tuwien.ac.at). Katia Perini is assistant professor at the Architecture and Design Department, Polytechnic School of the University of Genoa (Italy). Main research interests: effects and performances of nature-based solutions in the field of environmental and economic

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sustainability in (of) urban areas and building/urban design. She obtained the EU Ph.D. label in 2012 at the University of Genoa. She was a visiting student at the Delft University of Technology and visiting scholar at Columbia University (NY, USA, Fulbright grant) and at the Technische Universität München (TUM, DAAD award). Over 80 publications (peer-reviewed journals, books, etc.). https://orcid.org/ 0000-0003-0415-8246.

Contributors Tom Benson Senseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA Ata Chokhachian Chair of Building Technology and Climate Responsive Design, School of Engineering and Design, Technical University of Munich, Munich, Germany Christopher Connock KieranTimberlake, Philadelphia, PA, USA Fabio Duarte Senseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA Kit Elsworth KieranTimberlake, Philadelphia, PA, USA Sabrina Erlwein School of Life Sciences, Chair for Strategic Landscape Planning and Management, Technical University of Munich, Freising, Germany Elizabeth Escott KieranTimberlake, Philadelphia, PA, USA Billie Faircloth KieranTimberlake, University of Pennsylvania, Philadelphia, PA, USA Natalie Gulsrud Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg C, Denmark Defne Sunguro˘glu Hensel Architecture Internationalization School, Southeast University, Nanjing, Jiangsu Province, China

Demonstration

Michael U. Hensel Department of Digital Architecture and Planning, Faculty of Architecture and Planning, Vienna University of Technology, Vienna, Austria Aminreza Iranmanesh Faculty of Architecture and Fine Arts, Final International University, Girne, North Cyprus Tobias Leichtle German Aerospace Center (DLR), Earth Observation Center (EOC), Weßling, Germany Astrid Moser-Reischl School of Life Sciences, Chair of Forest Growth and Yield Science, Technical University of Munich, Freising, Germany

Editors and Contributors

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Stephan Pauleit School of Life Sciences, Chair for Strategic Landscape Planning and Management, Technical University of Munich, Freising, Germany Rui A. P. Perdigão Meteoceanics Institute for Complex System Science, Vienna, Austria; Universidade de Lisboa, Lisbon, Portugal Katia Perini Università Degli Studi Di Genova, Genova, Italy Cédric Pruski Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg Mohammad Rahman School of Life Sciences, Chair for Strategic Landscape Planning and Management, Technical University of Munich, Freising, Germany Carlo Ratti Senseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA Susanne Raum School of Life Sciences, Chair for Strategic Landscape Planning and Management, Technical University of Munich, Freising, Germany Thomas Rötzer School of Life Sciences, Chair of Forest Growth and Yield Science, Technical University of Munich, Freising, Germany Daniele Santucci Climateflux, Munich, Germany Hannes Taubenböck German Aerospace Center (DLR), Earth Observation Center (EOC), Weßling, Germany; Department of Remote Sensing, University of Würzburg, Würzburg, Germany Ryan Welch KieranTimberlake, Philadelphia, PA, USA

Chapter 1

The Introduction to Informed Urban Environments Ata Chokhachian , Michael U. Hensel , and Katia Perini

Abstract This chapter introduces the notion of Informed Urban Environments based on the application of data integrated methods for human- and ecology-centred perspectives to urban planning, design and adaptation. The aim is to initiate a databased inter- and trans-disciplinary discourse in sustainable urban development that is multi-domain and transscalar in character. Moreover, the chapter sheds light on the structure of the book and introduces the different thematic contributions address the topics of environment, information and urban aspects. Keywords Sustainable urban development · Interdisciplinary · Multi-domain · Transscalar · Big data · Data-based methods

1.1 Informed Urban Environments 21st century development and transformation of urban environments is strongly characterised by processes of rapid urbanization through land use change (Ojima et al. 1994; Lambin and Geist 2006; Darrel Jenerett and Potere 2010; Egidi et al. 2021) and densification of cities (Burgess 2000; Harrison et al. 2021). Mengmeng et al. pointed out that “globally, urban areas are growing at a faster rate than their population, potentially reducing environmental sustainability due to undesirable land take in (semi) natural and agricultural lands “ (Mengmeng et al., 2022). Rapid urbanisation and construction are the key drivers of environmental transformation (Hardy et al. 2001). A. Chokhachian (B) Chair of Building Technology and Climate Responsive Design, School of Engineering and Design, Technical University of Munich, Munich, Germany e-mail: [email protected] M. U. Hensel Department of Digital Architecture and Planning, Faculty of Architecture and Planning, Vienna University of Technology, Vienna, Austria e-mail: [email protected] K. Perini Università degli studi di Genova, Genova, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_1

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While increased building activity is necessary to respond to the current and projected future growth of the human population, land cover and land use change associated with urbanization and construction cause considerable negative impact on climate (Dale 1997; Dirmeyer et al. 2010), environment, ecosystems (Metzger et al. 2006), and human health and well-being (Moore et al. 2003; Cox et al. 2018). Limiting the negative impact of rapid urbanization and developing informed and sustainable approaches constitutes therefore a key challenge for urban planning, design, and governance. Data, data science and data-related methods play a fast-increasing role in sustainable development (UN Big Data for Sustainable Development), in knowledge fields and subject areas related to urban environments (Batty 2013a, b; Bettencourt 2014; Kitchin 2018), and more specifically in sustainable urban development (Kharrazi et al. 2016; Angelidou et al. 2018; Adams et al. 2021). Over recent decades an increasing amount of literature has emerged that elaborates the role of data in relation to diverse domains and topics related to the city. This includes literature focused on specific disciplines and domains, as well as on inter- and transdisciplinary approaches. Still, there exists a gap related to more extensive integrative cross-domain approaches. This book seeks to provide a step in this direction. While each chapter pursues a specific theme actual and potential linkages between disciplines or domains are, wherever possible, foregrounded and discussed. Increasing digitization and Big Data, data-based methods, multi-modal data acquisition, Geographic Information Systems (GIS) (Huxhold 1991), Internet of Things (IoT) and Artificial Intelligence (AI) enhance the ability to generate and utilize data to advance the understanding of the relation between rapid urbanisation and sustainability problems (Kharrazi et al. 2016; Angelidou et al. 2018; Adams et al. 2021). Data-intensive science deals with the collection and analysis of massive amounts of data, originating from observations, monitoring, scientific simulations, etc. Data science is a relatively new integrative discipline that involves Big Data, data statistics, data mining techniques, databases, and distributed systems (van der Aalst 2016). However, it is already in use in various areas of urban analysis, leading to new knowledge and informing planning policies and processes. Moreover, data science initiatives have been introduced in academic programs, private practices and public agencies that focus on urban issues (Duarte and de Souza 2020). Big Data and databased methods have gained ground in both urban planning and in data-driven design and construction (Deutsch 2012; Bier and Knight 2014). Buildings, cities and urban green systems are digitally documented with LiDAR and photogrammetry (Tucci et al. 2017; Wang et al. 2018) and sustainable urban planning and development is frequently supported by geospatial information (Borgogno-Mondino and Lessio 2020). Data-driven computational design and analytical methods and simulations are increasingly integrated in urban planning and architectural design (Hensel and Sørensen 2014; Chokhachian et al. 2020; Sunguro˘glu Hensel et al. 2022). Recent studies show that data-driven urban and architectural design can help reduce negative impact of construction and related human activities (Perini et al. 2017; Chokhachian et al. 2018).

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This book portrays data-related approaches to the planning and design of urban environments with focus on the selected interrelated themes including urban adaptation, urban morphology, urban mobility, urban microclimate, urban ecosystems, urban forests, and urban agriculture. These selected themes are related to current research on cities and urbanization based on Big Data, data science related methods, data-driven design and analyses. Recent advances in these focal areas facilitate an improved understanding of linked urban issues through multi-domain and transscalar approaches to urban planning and architectural design (Sunguro˘glu Hensel et al. 2022) that enable an interdisciplinary approach to sharing, exchanging and integrating data-integrated methods. Emphasis is placed on keywords correlations within each chapter, enabling the reader to comprehend the state-of-the-art in one field of specialization and how different sources of information and data in urban planning and design can come together to inform multi-domain and trans-scalar computational workflows (Fig. 1.1). The selection of the keywords is based on bibliometric analysis and discussions with the authors of the chapters presented in this book (see note on keyword selection at the end of this chapter).

Fig. 1.1 Chapter topics and keywords relations within and across chapters

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The first section of the book (current chapter) addresses principal aspects concerning the actual or potential role of data in sustainable development, planning and design, in practice, and the role of computational ontologies in turning data into knowledge to support tasks ranging from information retrieval to decision support. In the second chapter Michael Hensel discusses a possible route for developing informed urban environments as an effective approach towards addressing complex sustainability problems especially for urban areas. This is based on a proposed linkage of the fields of sustainability science, urban science, and architectural science, based on shared inter- and transdisciplinary, as well as data-integrated approach. The aim is to arrive at a framework that is both instrumental and problem oriented. Focus is placed on reviewing main features, discourses and data-related approaches in sustainability science, urban science, and architectural science to identify potential connecting points and overlaps between these fields. In the third chapter Billie Faircloth et al. posit that a mosaic of data could be a basic informational construct that imperfectly describes the interface between individuals, buildings, cities, and ecology. In this context they ask: What is the datamosaic, and to what degree can architects and urban planners see, know, and interact with it? They pursue an understanding of a datamosaic as a multi-domain data complex connecting design to other environmental science disciplines, as part of a sustained effort to characterize social, ecological, and technical interactions. Following this they establish three general observations of the architectural profession’s use of data: (1) designers collect, parse, and structure original data sets through the lens of architecture, often for deciding or exploring design options; (2) designers who collaborate on inter- or transdisciplinary teams become aware of datasets and databases pertaining to domains such as urban ecology, environmental management, and public health to expand the system boundaries of buildings and connect actions to sustainable outcomes; (3) research teams are presented with an opportunity to assemble data across domains, encountering the potential for an all-encompassing mosaic of data. Projects of the design firm KieranTimberlake highlight ways of working with the datamosaic based on data-driven and research-driven design paradigms. In the fourth chapter Cédric Pruski and Defne Sunguro˘glu Hensel examine how the understanding of urban environment has undergone profound changes accelerated by the advent of information science and big data, and the ever-increasing quantity of data produced by smart devices located in urban areas and remote sensing. This development has been accompanied by advanced information and communication technologies designed to take advantage of this data deluge. Focus is placed on the role of computational ontologies, which have been proposed to turn this data into knowledge to support a variety of tasks ranging from information retrieval to decision support. This is based on a review on recent approaches for information modelling in urban environments that implement computational ontologies, the problems these approaches intend to solve and their current limitations. Based on this analysis, a roadmap of future research is drawn that needs to meet the environmental and ecological challenges arising from urbanization.

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Section two of the book collects chapters on a series of key topics including urban adaptation, urban morphology, urban mobility, urban microclimate, urban ecosystems, urban forests, and urban agriculture. The chapter on Urban Adaptation entitled “Urban Adaptation—Insights from Information physics and Complex System Dynamics” by Rui Perdigao reframes urban adaptation through frontier coevolutionary information physics and complex system dynamic approaches. This is done from an interdisciplinary perspective articulating frontier natural, social and technical sciences into a novel mathematical lingua franca that articulates manifold disciplines. In this context the understanding of urban adaptation shifts from rethinking to reframing adaptation as system dynamic coevolution, thereby reshaping concepts, strengthening procedures, empowering choices to turn insights into actions, to derive a novel interdisciplinary framework for addressing urban socio-environmental dynamics linked with coevolutionary Earth System Dynamics. This is done with the aim to enable data-based and process-based system dynamic understanding, design, analytics and decision support, based on information physics, nonlinear data analytics and model design, and with expert knowledge. The chapter on Urban Morphology entitled “Decoding Cool Urban Forms: Using Open Data to Build a Dialogue Between Microclimate and Configurational Morphology in Urban Environments” by Ata Chokhachian and Aminreza Iranmanesh, introduces and applies a methodology to use historical, spatial, and temporal datasets from open data sources processed by Space Syntax superimposed by simulation data on urban microclimate dataset to find correlating patterns on how urban morphology has shaped the cities and the microenvironments over time. A case study Munich illustrates the typologies that can be utilized in planning and developing design strategies to address microclimate and accessibility in cities. The chapter on Urban Mobility entitled “From Amsterdam to New Amsterdam: how urban mobility shapes cities” by Tom Benson, Fabio Duarte and Carlo Ratti examines mobility innovations that improve urban living. Amsterdam and New Amsterdam (New York City) serve as case studies that highlight the evolution of transit from ecological, social, and economic frameworks, and that show how new digital tools and technologies can offer the potential to enhance urban living, envisioning the city as a real-time city. In the chapter on Urban Microclimate entitled “Urban microclimate spatiotemporal mapping: a method to evaluate thermal comfort availability in urban ecosystems” Daniele Santucci explains the need for quantifying microclimatic conditions in urban space and presents a methodology that is demonstrated on the case study of the Boston Back Bay Area. In this context a factor was developed that indicates spatiotemporal outdoor comfort availability based on a simulation workflow generating datasets and related maps. This serves four purposes: (1) quantification of outdoor comfort availability at the pedestrian level with high spatiotemporal resolution; (2) comparison of different scenarios and neighbourhoods; (3) providing evidence of the influence of comfort; and (4) informing action on improving outdoor thermal comfort in urban ecosystems.

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The chapter on Urban Ecosystems entitled “Urban Ecosystems and Nature-based Solutions: the role of data in optimizing the provision of ecosystem services” by Katia Perini focuses on challenges regarding ecosystem health, human wellbeing, and environmental quality in urban environments. Healthy urban ecosystems can provide numerous ecosystem services, that is benefits provided by nature to humans. Nature-based solutions can enhance the ecosystem services in cities and should therefore be planned and designed. However, a systematic approach to optimizing ecosystem service provision is frequently overlooked. Data collection and modelling allow for the evaluation of biotic and abiotic interactions, as well as the effects of anthropogenic activities and modifications. The chapter focuses on the role of related data in an integrated design process that foregrounds the performance of nature-based solutions to improve urban ecosystem health. In the chapter on Urban Forest entitled “Smart Urban Forestry: is it the future?” Stephan Pauleit et al. show that the stock of urban trees, is a major component of urban green spaces, which can contribute significantly to urban sustainability and climate change adaptation. Urban forest governance and management play a key role in the way these contributions are realized. This chapter portrays several new technologies that can offer new potentials for ‘smart’ urban forestry, including remote sensing, modelling and citizen science. Focus is placed also on tools for e-participation and communication with the public as means to improve the involvement of multiple stakeholders in the planning and management of urban forests to the benefit of a culturally diverse society. In the chapter on Urban Agriculture entitled “Big Data and Decision Support in Rural and Urban Agriculture” Defne Sunguro˘glu Hensel outlines the fundamental challenges faced by 21st century agriculture. This entails an examination of the role of Big Data and data-based methods and decision support in rural and urban agriculture to support exchange of insights and approaches in food production between rural and urban contexts. This is done with the aim to highlight present overlaps, exchanges, and research foci, and to identify research gaps in developing novel solutions to the existing challenges. Finally, a research gap is identified, and a novel field of research is introduced that is entitled ecological prototypes. The latter synthesize food production, ecosystem restoration and construction and necessitates targeted development of knowledge and data-based decision support. Any selection of topics and themes will always be incomplete, and this book does not aim for a comprehensive thematic approach. Instead, what is intended is to initiate dialogues and linkages between different domains and disciplines en route to informed urban environments.

1.2 Note on Keyword Selection As part of the work on this book we sought to identify relevant keywords through a bibliometric analysis of circa 36,000 references related to the keywords “data”, “urban” and “environment”. Bibliometric analysis is a quantitative method for

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studying bibliometric material (Pritchard 1969; Broadus 1987). The purpose of this method is to observe and evaluate the emergence, trajectory of development, and knowledge structure of specific topics. This is done through the application of quantitative analysis and statistics of a set of bibliographic documents. For the purpose of the bibliometric analysis VOSViewer (Version 1.6.16), a software tool for constructing and visualizing bibliometric networks and the cooccurrence relations between terms extracted from the scientific literature, was used. The process comprised of (1) importing the information on 36,372 articles, (2) calculating the co-occurrence of author keywords, (3) clustering and visualizing the terms by co-occurrences, and (4) exporting network maps. Scopus was used as the primary literature database. The terms “Urban”, “Environment” and “Data” were searched in the titles and abstracts of all papers from 1952 to 2021, resulting in 36,372 articles for twelve subject areas ordered by descending number of articles: (1) environmental science, (2) engineering, (3) social sciences, (4) computer science, (5) agricultural and environmental sciences, (6) energy, (7) business, management, and accounting, (8) psychology, (9) decision sciences, (10) multidisciplinary, (11) economics, econometrics, and finance, and (12) health professions. Bibliometric analysis was first undertaken for the combined subject areas and subsequently for the following subject areas separately: environmental science, engineering, social sciences, computer science, agricultural and environmental science, and multidisciplinary. The analysis focused principally on the impact of specialized expert domains in sustainable development and urban planning in conjunction with the growing importance of Big Data and data-driven methods in urban planning and design. The question was whether the obtained results would indicate an increased silofication of approaches to sustainable planning and design or, alternatively, a trend towards multi-domain and data-integrated approaches? The analysis revealed that while datarelated themes occur throughout the examined subject areas, there exists a clear emphasis on discipline- and domain-specific approaches, however without any clear trends that would suggest increasing multi-domain linkages and data-integration. Moreover, the keyword search for “big data” did not turn up results for the subject area “multidisciplinary”, which highlights the importance of transdisciplinary research in the context of urban environments supported by data and information dialogue as the need of the hour.

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References Adams D, Novak A, Kliestik T, Potcovaro AM (2021) Sensor-based bid data applications and environmentally sustainable urban development in internet of things-enabled smart cities. Geopolit Hist Int Relats 13(1):108–118 Angelidou M, Psaltoglou M, Komninos N, Kakderi C, Tsarchopoulos P, Panori A (2018) Enhancing sustainable urban development through smart city applications. J Sci Technol Policy Manag 9(2):146–169. https://doi.org/10.1108/JSTPM-05-2017-0016 Batty M (2013a) Big data, smart cities, and city planning. Dialogues Hum Geogr 3(3):274–279. https://doi.org/10.1177/2043820613513390 Batty M (2013b) Urban informatics and big data—a report to the esrc cities expert group. file:///Users/iktaho/Downloads/Basic_13595–3.pdf. Accessed 24 Sept 2021 Bettencourt LMA (2014) The Uses of big data in cities. Big Data 2(1):12–22. https://doi.org/10. 1089/big.2013.0042 Bier H, Knight T (eds) (2014) Dynamics of data-driven design—footprint 15. https://doi.org/10. 7480/footprint.8.2 Borgogno-Mondino E, Lessio A (2020) Geospatial tools in support of urban planning: a possible role of historical maps in programming a sustainable future for cities. In: Gervasi O, Murgante B, Misra S, Garau C, Blecic I, Taniar D, Apduhan BO, Roche AMAC, Tarantino E, Torre CM, Karaca Y (eds) Computational science and its applications—ICCSA 2020. Lecture notes in computer science, vol 12252. Springer, Cham Broadus RN (1987) Towards a definition of “bibliometrics.” Scientometrics 12(5–6):373–379. https://doi.org/10.1007/BF02016680 Burgess R (2000) The compact city debate: a global perspective. In: Burgess R, Jenks M (eds) Compact cities—sustainable urban forms for developing countries. Routledge, London Chokhachian A, Lau KKL, Perini K, Auer T (2018) Sensing transient outdoor comfort: a georeferenced method to monitor and map microclimate. J Build Eng 20:94–104. https://doi.org/10. 1016/j.jobe.2018.07.003 Chokhachian A, Perini K, Giulini S, Auer T (2020) Urban performance and density: generative study on interdependencies of urban form and environmental measures. Sustain Cities Soc 53:101952. https://doi.org/10.1016/j.scs.2019.101952 Cox DTC, Shanahan DF, Hudson HL, Fuller RA, Gaston KJ (2018) The impact of urbanisation on nature dose and the implications for human health. Landsc Urban Plan 179:72–80. https://doi. org/10.1016/j.landurbplan.2018.07.013 Dale VH (1997) The relationship between land-Use change and climate change. Ecol Appl 7(3):753– 769. https://doi.org/10.1890/1051-0761(1997)007[0753:TRBLUC]2.0.CO;2 Darrel Jenerett G, Potere D (2010) Global analysis and simulation of land-use change associated with urbanization. Landsc Ecol 25:657–670. https://doi.org/10.1007/s10980-010-9457-2 Deutsch R (2012) Data-driven design and construction: 25 strategies for capturing, analysing, and applying building data. Wiley, London Dirmeyer PA, Niyogi D, de Noblet-Ducoudré N, Dickinson RE, Synder PK (2010) Impacts of land use change on climate. Int J Climatol 30(13):1905–1907. https://doi.org/10.1002/joc.2157 Duarte F, deSouza P (2020) Data science and cities: a critical approach. Harv Data Sci Rev. https:// doi.org/10.1162/99608f92.b3fc5cc8 Egidi G, Salvati L, Falcone A, Quaranta G, Salvia R, Vcelakova R, Giménez-Morea A (2021) Re-framing the latent nexus between land-use change, urbanization and demographic transitions in advanced economies. Sustainability 13(2):533. https://doi.org/10.3390/su13020533 Hardy JE, Mitlin D, Satterthwaite D (2001) Environmental problems in an urbanizing world: finding solutions in cities in Africa, Asia and Latin America. Routledge, London Harrison P, Klein G, Todes A (2021) Scholarship and policy on urban densification: perspectives from city experiences. Int Dev Plan Rev 43(2):151–173. https://doi.org/10.3828/idpr.2020.5

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Hensel M, Sørensen S (2014) Intersecting knowledge fields and integrating data-driven computational design en route to performance-oriented and intensely local architectures. Footprint 15:59–74. https://doi.org/10.7480/footprint.8.2.812 Huxhold WE (1991) An introduction to urban geographic information systems. Oxford University Press, Oxford Kitchin R (2018) Data-driven urbanism. In: Kitchin R, Lauriault TP, McArdle G (eds) Data and the city. Routledge, London, pp 44–56 Kharrazi A, Qin H, Zhang Y (2016) Urban big data and sustainable development goals: challenges and opportunities. Sustainability 8(12):1293. https://doi.org/10.3390/su8121293 Lambin EF, Geist HJ (2006) Land-use and land-cover change—local processes and global impacts. Springer, Berlin Mengmeng L, Verburg PH, van Vliet J (2022) Global trends and local variations in land take per person. Landsc Urban Plan 218:104308. https://doi.org/10.1016/j.landurbplan.2021.104308 Metzger MJ, Rounsevell MDA, Acosta-Michlik L, Leemans R, Schröter D (2006) The vulnerability of ecosystem services to land use change. Agric Ecosyst Environ 114(1):69–85. https://doi.org/ 10.1016/j.agee.2005.11.025 Moore M, Gould P, Keary BS (2003) Global urbanization and impact on health. Int J Hyg Environ Health 206(4–5):269–278. https://doi.org/10.1078/1438-4639-00223 Ojima DS, Galvin KA, Turner BL (1994) The global impact of land-use change: to understand global land use change, natural scientists must consider the social context influencing human impact on environment. Bioscience 44(5):300–304. https://doi.org/10.2307/1312379 Perini K, Chokhachian A, Dong S, Auer T (2017) Modelling and simulating urban outdoor comfort: coupling ENVI-Met and TRNSYS by grasshopper. Energy Build 152(1):373–384. https://doi.org/ 10.1016/j.enbuild.2017.07.061 Pritchard A (1969) Statistical bibliography or bibliometrics. J Doc 25:348–349 Sunguro˘glu Hensel D, Tyc J, Hensel M (2022) Data-driven design for architecture and environment integration. Spool Cyber-Phys Arch 5 Tucci G, Bonora V, Conti A, Fiorini L (2017) High-quality 3D-models and their use in a cultural heritage conservation project. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-2/W5:687– 693. https://doi.org/10.5194/isprs-archives-XLII-2-W5-687-2017 UN Big Data for Sustainable Development (2021) https://www.un.org/en/sections/issues-depth/ big-data-sustainable-development.html. Accessed 24 Nov 2021 Van der Aalst W (2016) Process mining—data science in action. Springer, Berlin Wang R, Peethambaran J, Chen D (2918) LiDAR point clouds to 3D urban models: a review. IEEE J Sel Top Appl Earth Obs Remote Sens 11(2):606–627. https://jijupeethambaran.weebly.com/ uploads/1/2/7/0/12702525/lidar_point_clouds_to_3d_urban_models_a_review.pdf. Accessed 10 Jan 2021

Ata Chokhachian (Dr. -Ing.) is a research scientist, educator, and advisor in the domains of building technology and urban climate, developing and employing computational decision-making processes, tools, and workflows for architects and urban planners. Since 2015, he has been appointed as a research associate at the chair of Building Technology and Climate Responsive Design, as well as chair for Architecture Informatics at the Technical University of Munich. In 2022, he defended his Ph.D. dissertation in developing experimental and simulation-based tools to quantify outdoor thermal comfort conditions in urban environments. In the summer of 2019, he was appointed as a visiting research fellow at the Sustainable Design Lab at the Massachusetts Institute of Technology. In January 2020, he co-founded Climateflux, a company offering platforms for data-driven and computational workflows for acquiring climatic knowledge. Michael U. Hensel is an architect and partner in the architectural practices OCEAN net (www. ocean-net.org) and OCEAN Architecture | Environment (www.ocean-a-e.com). He taught at the Architectural Association School of Architecture in London from 1993 to 2009. From 2011 to

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2018 he was director of the Research Centre for Architecture and Tectonics at the Oslo School of Architecture and Design. Since 2018 he is professor at the Faculty of Architecture and Planning at Vienna University of Technology and heads the research department for Digital Architecture and Planning (www.dap.tuwien.ac.at). Katia Perini is assistant professor at the Architecture and Design Department, Polytechnic School of the University of Genoa (Italy). Main research interests: effects and performances of nature based solutions in the field of environmental and economic sustainability in (of) urban areas and building/urban design. Katia Perini obtained the EU PhD label in 2012 at the University of Genoa. She was visiting student at the Delft University of Technology and visiting scholar at Columbia University (NY, USA, Fulbright grant) and at the Technische Universität München (TUM, DAAD award). Over 80 publications (peer reviewed journals, books, etc.).

Chapter 2

The Bigger Picture en Route to Informed Urban Environments Initiating Linkages Between Sustainability Science, Urban Science, and Architectural Science Through an Inter- and Transdisciplinary Data-Integrated Approach Michael U. Hensel Abstract The chapter sketches out a route for developing informed urban environments as an effective approach towards addressing complex sustainability problems especially for urban areas. The main thesis is that this can be initiated by linking the fields of sustainability science, urban science, and architectural science, based on shared inter- and transdisciplinary, as well as data-integrated approach with the aim to arrive at a framework that is both instrumental and problem-oriented. To commence this effort focus is placed on reviewing the main features, discourses and data-related approaches in sustainability science, urban science, and architectural science with the aim to identify potential connecting points and overlaps between these fields. Keywords Sustainability science · Urban sciences · Architectural sciences · Big data · Data-related methods

2.1 Introduction The chapter proposes a specific route for developing informed urban environments as an effective approach towards addressing complex sustainability problems especially for urban areas. The main thesis is that this can be initiated by linking sustainability science, urban science, and architectural science. This needs to be based on shared inter- and transdisciplinary, as well as data-integrated approach to arrive at a framework that is both instrumental and problem-oriented. Collecting reference literature at the onset of working on this chapter inevitably led to the Cambridge Urban and Architectural Studies book series. The foreword by Leslie Martin and Lionel March for the first volume published in 1972 made clear that there were no better words to lead into this chapter, even though these M. U. Hensel (B) Department of Digital Architecture and Planning, Faculty of Architecture and Planning, Vienna University of Technology, Vienna, Austria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_2

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words were written half a century ago. In this context Martin and March pointed out that “[m]ore and more the environment of our world is manmade; in its own right it deserves serious study … [This] … extends outside the usual boundaries of the world of architecture—to the measurement of that environment and its history and the methods that a greater self-consciousness can bring to the process by which the environment is created … [T]hese studies tend to remove the distinction between architecture and planning, between design of individual buildings and the collective choice of shaping the environment” (Martin and March 1972). Thus, Martin and March set out in a few sentences the contours of three key points around which ongoing discourse keep revolving and that also form key points of this chapter. These points are: (1) expanding inquiry beyond discrete objects (buildings) and their multiplication (urban fabric) to a more inclusive notion of environment; (2) linking knowledge fields and related methods for the purpose of designing environments; and (3) operating across scales instead of within discrete scales. In 1967, five years before launching the Cambridge Urban and Architectural Studies book series, Leslie Martin wrote that “the ultimate problem for the profession is that of setting out the possibilities and choices in building an environment” (Martin 1967). Likewise, Larry Busbea wrote in a foreword for a recent edition of Tomás Maldonado’s book ‘Design, Nature & Revolution: Toward a Critical Ecology’ that “to design in an environment is to design an environment” (Busbea 2019). To provide a definition of environment Busbea summarised Maldonado’s take as an integral understanding that includes “technology, culture, and global ecosystems … visualized and modelled as mutually generative processes” (Busbea 2019). This positions the task of designing environments beyond the limits of individual disciplines or discrete scales and implies that for any work there exist scales and systems that are smaller and larger than that of a specific construction or an urban or landscape scheme. The involved spatial, temporal, and functional scales correlate and interact and are linked to different stakeholders that play vital roles concerning questions of interaction, performance, ecology, and sustainability (Hensel and Sunguro˘glu Hensel 2020a, b). The question as to how to tackle this remains at the centre of any inquiry that focuses on sustainable urban environments. They revolve around how to define and enact the possibilities and choices Leslie Martin referred to. We may ask then how this can be done in an increasingly informed way towards what we might term informed urban environments. Today extensive unsustainable development results from cities, rapid urbanization, and construction, which impact massively on the environment, thereby constituting a major challenge for humanity in the 21st Century. For several decades by now rapid action has been called for with the aim to reduce negative impact on environment, climate, humans, and other species. One of the prominent responses are the United Nations’ 2030 Agenda Sustainable Development Goals (SDGs) (UN Sustainable Development Goals). In acknowledging the role of cities and rapid urbanization SDG 11 focuses on making “cities and human settlements inclusive, safe, resilient and sustainable”, thereby addressing a complex set of linked and compound sustainability problems. As pointed out on the UN’s website SDG 11 also connects to a range of other SDGs, including SGD 1 “no poverty”, SDG 6 “clean water and sanitation”,

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SDG 7 “affordable and clean energy”, SDG 8 “decent work and economic growth”, SDG 9 “industry, innovation and infrastructure”, SDG 12 “responsible consumption and production”, SDG 15 “life on land”, and SDG 17 “partnership for the goals” (UN Sustainable Development Goals Knowledge Platform). From an even broader perspective all SDGs are connected in significant ways. SDG 13 on “climate action”, for instance, is clearly linked with sustainable cities and human settlement, even though this relation was not specifically listed on the website of the UN. Given this entanglement and complexity the question arises how to meet these goals and what the dominant trends in doing so are or could be and how these might be linked. Recent research showed that current approaches to sustainability focus either on distinct domain problems or, alternatively, on the interrelatedness of sustainability problems (Halla and Binder 2020). Both approaches have their evident advantages and disadvantages. Separating sustainability problems into delineated sub-problems might serve to reduce complexity. Yet, this approach falls way short in incorporating the many ways in which such problems are linked and impact upon another, thereby often leading to inadequate results. On the other hand, operating on the entanglement of sustainability problems will at some point entail overwhelming complexity. Various experts suggested that one step forward in the attempt to overcome this dilemma is to employ a paradigm rooted in systems thinking (Reynolds et al. 2017; Williams et al. 2017) and complexity science (van Kerkhoff 2014), hence embracing multi-dimensionality, dynamics, non-linearity, and unpredictability. In general, establishing a suitable point of departure entails examining relevant developments that together can deliver vital strands of an integrative way forward towards advancing sustainable development of urban environments. First includes the evolving fields of sustainability science. In terms of urban planning and design and architecture it is useful to examine urban science, and architectural science in search for possible connection points or overlaps with sustainability science in search of an integrative and synergistic approach. Furthermore, this includes Big Data and data-driven methods in the context of planning, designing, implementing, and maintaining informed urban environments. A clear framing of sustainability science, urban science and architectural science is at least in part a difficult undertaking, even though there exists extensive literature on these fields. This difficulty results from several factors that apply to different degrees to each one of these heterogenous and fast evolving fields. There exist different views regarding their characteristics, and different experts place different emphasis on thematic approaches, focal areas, and related methods. To an extent this presents a problem for the purpose at hand since it impacts upon the level of specificity to which these fields can be defined and correlated. However, it is possible to identify a range of traits for each of those fields that can help to chart a way forward.

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2.2 Sustainability Science(s) Since the onset of the new millennium numerous experts and scholars have contributed to the development of sustainability science (Kates 2011). Clark and Dickson explained that at the start of the development of sustainability science there existed no single mature discipline, but instead different sciences that are “problemdriven, with the goal of creating and applying knowledge in support of decision making for sustainable development “ (Clark and Dickson 2003). This fragmented condition is currently still the case as Clark and Harley have pointed out (Clark and Harley 2020). Nevertheless, several useful general aspects can be stated about this field that places focus on the interaction between society and nature, and more specifically the shaping impact of social transformations upon the environment and vice versa (Clark and Dickson 2003). Clark and Dickson pointed out that the underlying complexity of such interactions implied that their segregation into sub-problems invariably leads to an inadequate understanding of nature-society systems (Clark and Dickson 2003) and that in seeking to meet this task scientists that pursue sustainability transition need to incorporate a wide scope of inquiry ranging from complex systems theory to cultural and political ecology (Clark 2007). Clark and Haley maintained that society-nature interactions constitute complex adaptive systems that are characterised by heterogeneity, nonlinearity, and innovation, the long-term development of which may not be predictable, but can nevertheless be comprehended and partly influenced (Clark and Harley 2020). Furthermore, the advancement of sustainability science entails expanding the knowledge of linked human-environment systems and delivering solutions to problems related to human needs, thereby pursuing a practical problem-solving and solution-focused approach (Clark 2007; Miller et al. 2014). Moreover, Clark and Dickson pointed out the need for addressing sustainability problems across a breadth of scales ranging from the global to the local, and the importance of the latter in providing context for dialogue and implementation of sustainable action and priorities (Clark and Dickson 2003).

2.3 Urban Science(s) The development of urban science reflects in general terms various aspects of the development of sustainability science. Still, several developments are much more recent and even key terminology is not always clearly defined. There exists, for instance, different definitions for the term “urban” (Wu 2014). Furthermore, Batty pointed out that there exist many sciences of the city with different focal areas, such as, urban climate and climate impact on urban form and function, urban ecology, physics of the built environment, social aspects such as governance and demography, processes of urban change and its drivers, etc. (Batty 2021). This reflects Clarks’ and Dickson’s statement about the heterogeneous mix of different knowledge fields at the onset of sustainability science (Clark and Dickson 2003), yet still

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in a less unified manner concerning shared goals or in terms of specific convergences between different expert areas that claim urban science status. What complicates things further in relation to setting out a joint urban science is the fact that the various knowledge fields that are currently involved are often already inter- and transdisciplinary fields, as is the case with urban ecology for instance. Still, there exist several discussions that frame urban science in more general terms and set out challenges that resonate and overlap with sustainability science. For instance, Acuto, Parnell and Seto stated the need for development and diffusion of new knowledge based on holistic inquiry to address complex and multi-dimensional urban challenges (Acuto et al. 2018). For Acuto et al. this entails commitment to de-silofication and reorganization of existing knowledge fields, as well as rethinking current science-policy interfaces (Acuto et al. 2018). Likewise, Alberti stated that urban science requires a theory of human settlements and their functions that is grounded in empirical evidence and recognition of the involved complexity (Alberti 2016). In this context Alberti called for broad interdisciplinary collaboration on a new research approach and agenda with focus on developing a shared framework and synthesis of knowledge for an integrative urban science (Alberti 2017). A new opportunity emerged for urban planning in its quest for a scientific grounding through the increasing availability of urban data and growing capacity of computers that enables data-intensive analysis and simulation of cities (Townsend 2015). While this development displays similarities with computer-based work on modelling and analysing cities in the 1960s and 1970s, Townsend pointed out that “the rise of the Internet presents new opportunities to involve citizens more actively, on a larger scale, and in more empowered roles than in the past”, thereby presenting “an opportunity to develop more transparent, ethical, and effective models for collaborative urban research involving universities, local government, and citizen science networks” (Townsend 2015). In this context Alberti pointed toward the importance of Big Data and data science in facilitating “understanding [of] the mechanisms driving urban systems [that] will allow urban scientists to inform development of innovative technologies and planning strategies for transitioning toward sustainable and resilient cities” (Alberti 2017). Additionally, Big Data has gained prominence in the context of cities (Bettencourt 2014; Batty 2016) and urban planning (Batty 2013a). However, beyond these general aspects it is necessary to examine some examples of the inter- and transdisciplinary fields that claim urban science status. Two of the fields that are frequently discussed as urban science are urban ecology and urban informatics. These were selected for closer examination. Urban ecology gradually evolved from human ecology in the context of sociology. However, it was not until recent decades that ecologists began to take interest into cities based on growing “concerns with environmental impacts of urbanization, the rise of ecological views emphasizing non-equilibrium and patch dynamics, and the pervasive influences of the ongoing sustainability movement “(McDonnell 2011; Wu 2008, 2014). Urban ecology is an interdisciplinary field with “deep roots in

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many disciplines including sociology, geography, urban planning, landscape architecture, engineering, economics, anthropology, climatology, public health, and ecology” (Marzluff et al. 2008). Focus is placed on humans and nature and their interaction in cities. More recently urban ecology has been proposed as a science of cities (Forman 2014), attesting to “the potential of the field to mature as a holistic, integrated science of urban systems” and that advancing it necessitates “conceptual synthesis, knowledge and data sharing, cross-city comparative research, new intellectual networks, and engagement with additional disciplines” (McPhearson et al. 2016). Given the interdisciplinary character of urban ecology and the focus on humannature interactions, it is relatively easy to identify its shared disciplines, thematic and methodological connection points and overlaps of goals and approaches. Additionally, recent discussions are beginning to focus on the role Big Data and data science can play in the development of urban ecology (Yang 2020). Urban informatics is a transdisciplinary field that aims at “understanding, managing, and designing the city using systematic theories and methods based on new information technologies” (Shi et al. 2021). Batty elaborated that “explaining and measuring the spatial structure of the city in terms of its form and function is one of the main goals … It provides links between the way various theories about how the city is formed, in terms of its economy and social structure, and how these theories might be transformed into models that constitute the operational tools of urban informatics” (Batty 2021). Furthermore, Liu explained that urban informatics bring together urban science, geomatics and informatics, whereby urban science provides the questions and uncovers principles, geomatics delivers data acquisition and management methods, and informatics delivers the technologies for utilizing principles and data for developing solutions to urban problems (Liu et al. 2021). Urban informatics utilizes different types of data acquisition that show how cities change constantly, different methods of visualizing urban processes and modelling and simulating them to enable predictions of future developments (Batty 2021). According to Batty, this enables the identification of vital aspects and functions of cities with the aim to extend knowledge from city as system to systems of cities (Batty 2021) and Big data plays a critical role in urban informatics (Batty 2013b). In this context, it is important to also recognize and examine the notion of the smart city. As numerous researchers have pointed out there exists different definitions for smart cities (Hollands 2008; Albino et al. 2015; Dameri 2017; Camero and Alba 2019). Recent literature and bibliometric analysis show commonly cited references and the breadth of definitions (Camero and Alba 2019). More generally, Angelidou stated that the notion implies a conceptual urban development model (Angelidou 2014). Dameri described a smart city as “a well-defined area, in which high technologies such as ICT, logistics, energy production, and so on, cooperate to create benefits for citizens in terms of well-being, inclusion and participation, environmental quality, intelligent development” (Dameri 2013). Furthermore, Angelidou described two key developments that focus on urban futures and knowledge and innovation economy respectively: “The urban futures strand shows that technology has always played an important role in forward-looking visions about the city of the future. The knowledge and innovation economy strand shows that recent technological advancements have

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introduced a whole new level of knowledge management and innovation capabilities in the urban context” (Angelidou 2015). Ramaprasad et al. (2017) presented a definition of smart city in the form of an ontology that “encapsulates the combinatorial complexity of the concept”, based on thirty different definitions of smart concepts. The notion of smart city has thus been discussed and deconstructed in some detail and clearly plays an important role in the context of utilizing urban data. Kitchin (2018) posited that “cities … are becoming more data-driven and are enacting new forms of algorithmic governance. However, the data and algorithms underpinning them are far from objective and neutral, but rather political, imperfect, and partial. The smart cities that data-driven, networked urbanism purports to create are then smart in a qualified way” (Kitchin 2018). To offset the involved risks Kitchen points towards the necessity to tap into different forms of knowledge, linking scientific and practical instrumental knowledge with practice- and experience-based knowledge, thereby essentially promoting a transdisciplinary approach (Kitchin 2018).

2.4 Architectural Science(s) It is important to recognize that cities are in large part made up of constructions and that there exist critical interactions across scales that make it necessary to involve a version of architectural science. Currently a possible agenda and related development of architectural science for the purpose of linking up with sustainability science and urban science is at best at its very beginning. Most of what is today referred to as architectural science is rooted either in the humanities or the engineering science. The latter is often understood as a service to architectural design, with the mandate to refine designs to meet specified requirements. This approach has been largely subsumed under the title building science but has also increasingly been labelled as architectural science (Szokolay 2004). While this approach comprises important contributions, it also present several impediments: (1) by placing near exclusive focus on the built object, its interior, on its material specification and energy consumption; (2) by positioning itself as a service that focuses on optimization of existing designs; and (3) in consequence pursuing a limited scope that lacks a broader outlook towards expanding the remit of architectural science and bringing it right into the centre of the design process. However, in the context of performance-oriented design, one version of which started up more closely linked to engineering science-based approaches and thus related to the remit of building science, new possibilities evolved that seek to bridge the form-function divide thereby focusing on broader aspects of architecture and environment interaction (Hensel 2010, 2011, 2012, 2013; Hensel and Sunguro˘glu Hensel 2020a) and extending this further into inquiries as to how architecture can be in the service of the bio-physical environment (Hensel and Sunguro˘glu Hensel 2020b). Furthermore, different approaches to data-driven architectural design and construction have developed over recent decades (Deutsch 2012; Bier and Knight 2014; Hensel and Sørensen 2014; Brown et al. 2020). The different approaches generally focus on architectural

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design and often more specifically on simulations and processes of design optimization especially in the context of performance criteria related to different sustainability requirements. However, there are also emerging efforts to expand the scope of datadriven design to multi-domain and trans-scalar approaches that span across traditional planning and design scales and address more complex architecture and environment interactions (Sunguro˘glu Hensel et al. 2022). As stated above, the future development of architectural science needs to expand its remit considerably to link up in a meaningful way with sustainability science and urban science. Since constructions make up the bulk of cities it is necessary to understand and design them as part of a trans-scalar approach. This development could be informed by the way architectural science can be linked with various characteristics and aims of sustainability science and urban science, thereby identifying overlaps of inter- and transdisciplinary scope, and data related aspects ranging from coordinated data acquisition to trans-scalar data-integrated computational modelling and simulation approaches.

2.5 Mapping Potential Affiliations Across Knowledge Fields Further research is required to gain a detailed overview over the three fields and their actual or potential overlaps and complementarities. For this purpose, it will be useful to examine sustainability science, urban science, and architectural science extensively in a much more detailed way. This needs to be done with the aim to identify potential thematic connection points and potential joint actions. In this context systems-thinking can offer useful methods and tools. A key concept of critical systems thinking is boundary critique (Ulrich 1996). Ulrich stated that “the practical implications of a proposition … its meaning as well as its validity depend on how we bound the system of concern, i.e., that section of the real world which we take to represent the relevant context. Our judgment of the merits of a proposition … will depend heavily on this context, for the context determines what ‘facts’ (e.g., consequences) and ‘values’ (e.g., purposes) we will identify and how we assess them” (Ulrich 2003). This makes clear how important boundary judgements are, as well as highlighting the need for transparency in this process. Moreover, since the three fields to be mapped are heterogeneous and in continual development it will be necessary to perform their mapping not only one but frequently. For such mapping, it can be useful to employ an approach termed giga-mapping (Sevaldson 2011). Sevaldson elaborated as follows: “GIGA-mapping is … extensive mapping … investigating relations between seemingly separated categories and so implementing boundary critique to the conception and framing of systems. GIGA-maps try to grasp, embrace and mirror the complexity and wickedness of real-life problems” (Sevaldson 2011). In general, further research efforts will include clarification of terminology across the fields, systematic linkage of different approaches in urban science, and a muchneeded clarification and expansion of the remit of architectural science as discussed above.

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Additional research efforts need to focus on the SDGs and the related subgoals to further clarify the specific relations between the different SGDs. It has been suggested that further advancement of integrated research and action depends on the linkages and interacts between the SDGs, highlighting positive and negative relations i.e., conflicts that result in trade-offs (Nilsson et al. 2018) and co-benefits. Towards this end Nilsson et al. elucidated that there exists a considerable yet fragmented knowledge of SDG interactions, but no actual framework for the purpose of aggregation and systematization of this knowledge into a knowledge base. To address this Nilsson et al. pursued the development of what they term the SDG interactions framework (Nilsson et al. 2016), a “conceptual framework for mapping and assessing SDG interactions using a defined typology and characterization approach”. (Nilsson et al. 2018) They state the twofold use of the framework as a potential policy tool and a tool for scientific research, i.e., as “an experientially-based compendium of case studies from which to drive implementation and prioritization”, as well as support for synthesis, analysis and modelling of context-specific cases. (Nilsson et al. 2018) They envisage a web-based knowledge repository as a learning, collaboration and practice platform with focus on SDG interactions, but state that such a platform “should not be about prescribing courses of action in given contexts. These are political decisions that should emerge through national processes. … [T]he knowledge platform should proactively support evidence-informed dialogue and learning among different stakeholders” (Nilsson et al. 2018).

2.6 Complementarity Between the Fields and Overlaps in Capacities In the context of sustainability science Clark and Harley proposed several required capacities that include “the ability to measure sustainable development, to promote equity, to adapt to unexpected situations, to transform the system into sustainable development pathways, to link knowledge with action, and to devise adequate governance to support the enactment of these capacities” (Clark and Harley 2020). Furthermore, Pennington et al. suggested that sustainability science competencies should be expanded to include the ability to collaborate across disciplines, as well as data science competencies (Pennigton et al. 2020). Nelson and Stolterman’s positioned design, including architectural design, as inquiry for action and elaborated four required capacities: routine, adaptive, design and value expertise (Nelson and Stolterman 2012). Routine expertise is based on the premise that design situations are the same, yet often unsuitable for finding adequate responses to complex and dynamic situations. In this situation adaptive or reactive expertise is useful but might not always constitute an adequate long-term approach. Nelson and Stolterman posited that it is frequently necessary to create change and that for doing so design and value expertise are required (Nelson and Stolterman 2012). To shift the context for routine and adaptive expertise it can be useful to

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foreground an interdisciplinary approach to establishing adequate starting positions for tackling compound sustainability problems through design. Design expertise can be developed and exercised formulation of design criteria and project briefs and through research-by-design as a mode of projective inquiry. Value expertise can be fostered by a multi-scalar and multi-domain approach to developing and evaluating future scenarios over a range of spatial and time scales. Moreover, data science competencies are gaining importance not only in urban science, but also in architectural science and design in general. A first set of overlaps that emerges across the fields is that of multi-stakeholder involvement, action-oriented approach, and data science competencies. There are likely many other overlaps that will become apparent through the above suggested mapping of the fields and their interdisciplinary outlook and composition. An interesting possible complementarity is that of the science related competencies and design related competencies. Jerneck et al. pointed out the need of knowledge structuring in sustainability science and commenced work towards this end (Jerneck et al. 2011). This was done in the format of “a generic research platform … comprising three components: core themes (scientific understanding, sustainability goals, sustainability pathways); cross-cutting critical and problem-solving approaches; and any combination of the sustainability challenges above”. Based on this effort, they propose a series of questions with the aim of advancing theory and methodology in sustainability science: “how new synergies across natural and social sciences can be created; how integrated theories for understanding and responding to complex sustainability issues can be developed” etc. (Jerneck et al. 2011). These synergies can be extended to seek for useful connections with design as inquiry for action (Nelson and Stolterman 2012) that can play a key role and add useful approaches and methods for understanding and response to complex sustainability issues.

2.7 Research Paradigm and Data-Integrated Approach In the context of sustainability science Pim Martens called for a new research paradigm that operates on the complex and multi-dimensional characteristics of sustainable development and that should incorporate “different magnitudes of scale (of time, space, and function), multiple balances (dynamics), multiple actors (interests) and multiple failures (systemic faults)” (Martins 2006). This corresponds with similar calls in urban science and increasingly also in architectural science. As has been shown, Big Data already plays a key role in advancing sustainable development (UN Big Data for Sustainable Development). Still, more detailed inquiry needs to be undertaken to establish in how far the current use of Big Data and data-driven methods in sustainability science, urban science and architectural science are implemented in sub-fields and in relation to addressing sub-problems and whether approaches exist that work across the field and on the complexity of entangled sustainability problems.

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This needs to be accompanied by a development of an integrative data- and information-based approach that links the three fields. Multi-modal data acquisition is common in all three fields and expert databases either exist or are under development. Vast amounts of data have been acquired, but not necessarily analysed and correlated in each field, let alone across them or across their sub-fields. Here questions arise as to how data that may be jointly collected, but also how data can be upgraded into actionable information, knowledge and ultimately into utility through joint efforts.

2.8 Conclusion As discussed above, the development of informed urban environments as an effective approach towards addressing complex sustainability problems especially for urban areas presents numerous considerable challenges. The proposed linking of sustainability science, urban science and architectural science is not an easy task given the fact that these fields are heterogeneous and in perpetual development. First, there exists a need for clarification of key terminology and the scope of each field. Second, extensive mapping exercises are required to identify overlaps and complementarities in composition, aims, competencies, methods, etc. as well as in terms of the role data currently plays and the role it can play in the future in providing useful links and resources for collaboration across the fields. This needs to be based on shared inter- and transdisciplinary affiliations, a shared data-integrated approach and importantly through joint projects to arrive at an approach that is both instrumental and problem-oriented. Given the fact that these fields are heterogeneous and evolving it seems clear that seeking to coordinate and enforce such affiliations in a strict top-down manner would be ineffective. Clark and Haley pointed out that “the Anthropocene is at its core a complex adaptive system centred in the intertwined, coevolving interactions of nature and society … Because it is heterogeneous, experience in one location will be an important but perilous guide to action in another” (Clark and Harley 2020). This indicates that context-specificity is a critical factor that plays an important role in seeking to instrumentalise experience and knowledge. For this reason, affiliations between the fields might best be fostered based on projects through which to build shared knowledge. Data-integrated methods could serve as a key instrument in this effort. To pave the way for this will be the challenge of follow-up research along the trajectories outlined above.

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Michael U. Hensel is an architect and partner in the architectural practices OCEAN net (www. ocean-net.org) and OCEAN Architecture Environment (www.ocean-a-e.com). He taught at the Architectural Association School of Architecture in London from 1993 to 2009. From 2011 to 2018 he was director of the Research Centre for Architecture and Tectonics at the Oslo School of

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Architecture and Design. Since 2018 he is professor at the Faculty of Architecture and Planning at Vienna University of Technology where he heads the research department for Digital Architecture and Planning (www.dap.tuwien.ac.at).

Chapter 3

How We See Now: Traversing a Data-Mosaic Billie Faircloth, Christopher Connock, Ryan Welch, Kit Elsworth, and Elizabeth Escott

Abstract Data has an immense role in the design and planning of urban environments. Data, however, is not neutral, nor is it without a domain. With increasing frequency, designers are awakening to the datasets and databases of other fields such as urban ecology, environmental management, and public health. Designers are presented with a vast opportunity that momentarily obscures the role of practicespecific databases for a much larger prize—the agency of making data-mosaics. A data-mosaic is a multi-domain, unstructured data complex. Its contributors might originate from multiple professions with different aims and ambitions. Designers who work to make data-mosaics see the gaps in their collective understanding of urban environments as quickly as they see the opportunities to participate in transdisciplinary collaborations. This chapter of Informed Urban Environments incorporates a practice-based perspective on the opportunities, challenges, limitations, and applications of data and data-driven design. KieranTimberlake, a Philadelphia-based practice, discusses modeling practices associated with vegetation, life cycle assessment, and occupant comfort to demonstrate the profession’s increasing awareness of mosaic-making. Keywords Architectural design and computation · Data-driven design · Mosaicking · Transdisciplinary · Urban ecology · LCA · Occupant comfort B. Faircloth (B) KieranTimberlake, University of Pennsylvania, Philadelphia, PA 19104, USA e-mail: [email protected] C. Connock · R. Welch · K. Elsworth · E. Escott KieranTimberlake, Philadelphia, PA 19123, USA e-mail: [email protected] R. Welch e-mail: [email protected] K. Elsworth e-mail: [email protected] E. Escott e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_3

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3.1 Introducing a Data-Mosaic Our discussion on data-mosaics begins with a candid admission: We find ourselves in a messy but vital predicament. The scale of our problem is planet-sized. It transcends a single disciplinary domain. To act, a designer can and should knit together multiple scales and types of data. A mosaic of data could be a basic informational construct that imperfectly describes the interface between individuals, buildings, cities, and ecology. In the essay, we explore a question as practicable as it is epistemological: What are data-mosaics, and to what degree can architects and urban planners make, use, and extend them? Provisionally, we understand a data-mosaic to be a multi-domain data complex with the potential to connect design to other environmental science disciplines. It is an expression of fragmented but sustained efforts to characterize social, ecological, and technical interactions. We can establish three general observations of our profession’s use of data: With increasing competency, designers collect, parse, and structure original data sets through the lens of architecture, often for deciding or exploring design options (Deutsch 2015). With increasing clarity, designers who collaborate on inter- or transdisciplinary teams draw upon the datasets and databases from domains such as urban ecology, environmental management, and public health to rightly expand the system boundaries of buildings and connect actions to sustainable outcomes (Burry 2020; Dunnin-Woyseth and Nilsson 2011, 2013). And, with increasing frequency, designers are presented with an opportunity to assemble data across domains, encountering the potential for creating mosaics of data (Hensel et al 2020; Kahn 2020; Seifert et al 2020; Tuncer and Benita 2021). In this essay, we use projects by our design firm KieranTimberlake to demonstrate encounters with data-mosaics based on data-driven and research-driven design paradigms (Deutsch 2015, 2019; Hensel and Nilsson 2016).

3.2 The General State of Data Now As our planetary data approach hundreds of ‘zettabytes’ and the phrase’Big Data’ turns over 20 years old, size is no longer novel but still unevenly distributed. A “Digital Feudalism” exists in which data ranging from our online interactions to the sensor outputs of our phones, cars, and toasters is collected and stored by a few entities (Meinrath et al. 2011). Over 80% of these zettabytes are considered unstructured data with no preset model or schema, making them typically challenging to analyze (Sagiroglu and Sinanc 2013). Data-driven machine learning algorithms have allowed data-rich organizations to gain insights into these large stores of unstructured data and actively drive decisions that affect our daily lives. Independent analysis of these increasingly common algorithms has uncovered racial bias in healthcare allocation, court-predicted recidivism, and gender bias in hiring practices and job advertisement targeting (Claburn 2021; Dastin 2018; Dressel and Farid 2018; Obermeyer

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et al. 2019). These findings, combined with the growing asymmetries in data ownership, have triggered a series of global reckonings regarding data collection methods, structure, bias, access, and ownership. Given the size and diversity of data now, it should come as no surprise that “mosaicmaking” or “mosaicking” data is becoming an established practice in data science, one that foregrounds the socio-technical negotiations underpinning all data, its transfer, construction, and use (Capotosto 2021; McInerney 2020; OCHA 2021; The Centre for Humanitarian Data 2020). Mosaicking is a process most associated with mosaic theory, which was first established in the realm of national intelligence, where the intentional assembly of data was cast as both an essential and nefarious endeavor (Kerr 2012; Pozen 2005). The mosaic theory contends that “apparently harmless pieces of information when assembled together could reveal a damaging picture.” (US NARA 2005, p. 71) The mosaic effect, an extension of mosaic theory, is explained in humanitarian efforts. It teaches that a mosaic of data depends upon approaches to blending data and can compromise the privacy of individuals and communities as quickly as it can support beneficial changes for them (Capotosto 2021; McInerney 2020; The Centre for Humanitarian Data 2020). The mosaic theory situates data types and their quantity as the primary materials for mosaic-building and data-linking as a means of mosaic traversing. These materials and methods are fundamental when considering mosaic-making in the domains of architecture, cities, and other environmental disciplines (Bibri and Krogstie 2020; Faircloth et al. 2018; Francisco and Tuncer 2019; Burry et al. 2015).

3.3 Data in the Domain of Architecture and Cities Thus, as we move from planetary zettabytes to architectural practice-based gigabytes, from the unstructured web to the domain of structured Building Information Modeling (BIM) databases, any effort to make a data-mosaic may seem far removed from architectural practice. Yet, the need becomes acute when designers step beyond their virtual or physical walls into urban environments. Attempting to describe a project’s broader context is an exercise in data empathy: buildings with patchy height information, tree locations with no species, or seemingly critical yet abbreviated demographic metadata tags with no description. Data rarely exists at a single granularity or completeness. When architects predict their interactions in urban environments, they must connect multiple spatial and temporal scales—from building to the city and from hour to year. The data we acquire for our predictions may be real-time, raw, and unstructured but are more often semi-structured: data that exists outside of a database but have a distinct schema and opinion on acceptable use. Moreover, the data we leverage are not always collected but the result of a series of model input–output relationships—each with its priorities and biases (Winsberg 2010). We need to find data and understand how and for whom they are constructed to leverage them properly (Loukissas 2019).

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To explore how data pervade from individual buildings to neighborhoods to cities to the environment, we explore the example of building-level energy model inputs such as the Typical Meteorological Year (TMY). First produced by Sandia National Laboratories in 1978, TMY provides designers with a ‘reasonably sized’ data set of hourly weather conditions for 12 typical months culled from a larger number of years (Wilcox and Marion 2008). The very idea of a representative year of weather data was a product of its time—the size of the data set tailored to the computational power of the late seventies (Vignola et al. 2013). The typical months of a TMY are chosen by comparing each month of a particular year across nine daily indices weighted based on their importance to solar energy conversion systems and buildings (Wilcox and Marion 2008). This bias towards solar aspects determines the questions one can ask. Moreover, a TMY inherently ignores extreme weather events and, subsequently, the ability to design for worst-case scenarios (Crawley and Lawrie 2015). KieranTimberlake developed their own TMY + to account for these differences, while Crawley and Lawrie have proposed similar formats such as the eXtreme Meteorological Year (XMY) (Crawley and Lawrie 2015; KieranTimberlake 2014). The University of Southampton’s Sustainable Energy Research Group saw that accelerating climate change may make the extreme look typical and have created tools like CCWorldWeatherGen to morph existing weather data per climate change scenarios (Jentsch et al. 2013a, 2013b). Understanding the structure of critical inputs and their history allows designers to question and extend them to create new practices with data. Our TMY-derived building energy predictions and metered resource use are aggregated to near ‘big data’ levels to benchmark building performance at the city scale and beyond. Large stores of both empirical and simulated building energy data have emerged in the United States Department of Energy’s Building Performance Database (BPD) and the AIA 2030 Design Data Exchange (DDx), respectively. Existing smaller, highly curated sources of energy use like the Commercial Building Energy Consumption Surveys (CBECS) aim for representative samples through an in-person survey (Mathew et al. 2014). This methodology limits the size of the data set and subsequently the ability to analyze the truly local – comparing against peers per market, region, or building type (Mathew et al. 2014). Theoretically, the BPD allows for more granular questions with almost a million buildings empirically recorded. Still, it is limited by the interests and expertise of the organizations that volunteer their data (Sohn and Dunn 2019). The socio-technical interaction underpinning the construction of data is no more apparent than when a city uses building-level data to predict or manage its energy use and carbon emissions. Data-driven policy in the form of building performance standards requires building owners to report actual energy use; mandate the creation and use of data stores; and it may require the public display of data in the form of certificates, labels, and online building performance data maps (Cohen 2019; Garodnick 2018; The Council of the City of Philadelphia 2012; UK Parliament 2007). For instance, in New York City, a series of local laws under the Climate Mobilization Act introduce funding for improvements and require energy use reductions, consistent reporting, and the

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visible public display of annual energy consumption (New York City Mayor’s Office of Climate and Sustainability 2019). However, the juxtaposition of failing grades (based on actual energy consumption) with green building rating system plaques (based on predicted energy consumption) has exposed the performance gap and the public’s lack of faith in the processes we use to meet larger climate goals (Millard 2021). As more institutions, cities, and governments leverage data in policy, the need for a genuinely transdisciplinary understanding of all data collections’ sources, extents, and purpose increases.

3.4 Data in the Domain of Other Disciplines The domains of ecology, environmental management, applied geography, and public health have an equally temporally-and-spatially-rich data structure. Urban ecologists, environmental managers, applied geographers, and public health specialists use unstructured, semi-structured, and structured data. These data support domainspecific models for ecosystem services, dynamic systems, geographic information systems (GIS), habitats, and diseases. They characterize the interaction between individuals, cities, and their environment. Subject-specific databases are essential to this work. For instance, Schmidt and Seppelt provide a review of 29 databases for ecosystem service modeling to determine their status, identify challenges, and rank areas of improvement (Schmidt and Seppelt 2018). A review of datasets associated with trees and vegetation alone yields numerous examples across scales on tree rings, tree species, experimental forests, urban forests, environmental impacts related to forestry practices, air quality and human health, and whole-earth vegetation dynamics such as atmospheric carbon cycling (Ecoinvent 3.8 2021; Hirabayashi and Nowak 2016; i-Tree Database 2.16 2022; Kindermann et al 2018; Urban Forest Data 2013; Tree Ring 2020; USDA 2018; USGS 2022). Contributions to these datasets depend upon an array of accepted collection methods associated with qualitative and quantitative research, including field measurement, surveying, drone sensing, and remote sensing. Data and datasets are made by individual scientists, research teams, organizations, city to national governments, and citizen scientist programs. The purpose of data collection initiatives varies, and much of these data are open and accessible for both research teams and policymakers.

3.5 Contextualizing a Data-Mosaic Data and datasets that could inform new relationships between individuals, buildings, cities, and ecology are plentiful and could present an impossible task to inventory them. Instead, we can see the potential scope of these data and our vital interest in mosaicking them through the planetary boundaries (PB) framework and the emerging field of planetary health.

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The PB framework aims “to define a safe operating space for human societies to develop and thrive based on our understanding of the functioning and resilience of the Earth system (Steffen et al. 2015).” Developed by Will Steffen and colleagues through the Stockholm Resilience Center in 2009 and evolving ever since, this socioecological work is inherently data-rich, inter- and transdisciplinary. It attempts to understand “numerical thresholds” for terrestrial “control variables” such as climate change, land-system change, biodiversity loss, and biochemical flows (Frumkin 2020; Stockholm Resilience Centre 2019). The PB framework is extended further by researchers within the emerging field of planetary health who mine clinical health data and health studies to expose the intersection between the degradation of earth’s systems and human health, thereby connecting individuals, buildings, cities, and the environment (Frumkin 2020). The PB framework and the emerging field of planetary health alone encompasses a vast amount of data and essential questions for us to consider when making datamosaics: How do data associated with the design and management of buildings, landscapes, and cities intersect with data that represents the tight coupling of planetary health and human health data? Rather than taking data from several entangled, disciplinary domains and applying them through a generative design paradigm, in what ways could we contribute data to a much larger enterprise through an action-based, outcome-oriented, feed-back-driven design paradigm?

3.6 Assembling a Data-Mosaic We understand a data-mosaic as a multi-domain data complex of vital interest to teams shaping urban environments and connecting knowledge across design, planning, environmental science, and public health disciplines. We should not misconstrue a data-mosaic as presenting a whole or even nearly complete picture of dynamic systems and their interactions. Instead, we believe a data-mosaic to be disordered and disorganized. It is patchy, gap-ridden, and simultaneously composed of structured, semi-structured, and unstructured data. The materials for making a data-mosaic are ever-expanding by the human and institutional will to characterize—this is a sociotechnical process used by disciplines to describe what something is and does through discipline-specific research and modeling methods. Teams can deliberately make mosaics of data by linking disparate types and scales of data. And they can see now where data and disciplinary domains are linked, unlinked and unlinkable. We approach assembling a data-mosaic through five fundamental principles: 1.

Embrace an inter-and trans-disciplinary mindset: An individual can glimpse a data-mosaic by browsing, borrowing, dissecting, and ultimately mosaicking data that originates with other disciplines. However, we presume that a datamosaic is more aligned with the fields of complexity and sustainability science, and we are suspicious of seeing a data-mosaic through a single disciplinary lens. Furthermore, making a data-mosaic is associated with the attributes of

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inter-and transdisciplinary teams. Conversing, sharing knowledge, and gaining perspective are essential to linking the data at hand (Augsburg 2014; Fritz and Meinherz 2020; Guimarães et al. 2019; Stock and Burton 2011; von Wehrden 2019). Learn from mosaic theory and the mosaic effect: Teams who make mosaics proceed deliberately. They intentionally link data using multiple techniques to see a bigger, more complete picture. Although designers, planners, and environmental scientists who work together may lack the immediacy to link data, they can proceed with the same degree of intention and care when exploring and creating new data-linking conventions (Capotosto, 2021). Challenge disciplinary-bound data and data modeling habits: Assembling a data-mosaic is a foray into multitudes of inputs and outputs, coarse and fine, attributable to domain-specific needs, shorthand, vocabulary, and computational practices. Teams who make mosaics of data are positioned to hybridize research and computational methods, examine new correlative techniques, and demonstrate approaches to multi-scale modeling. Advocate for new system boundaries: Teams who pursue mosaic-making are working to understand complex systems and the logic used to define a system and its boundary. They can use data and mosaics of data to challenge both the technical and ethical concerns of systems and disciplines. (Frumkin 2020; Tinder 2018). Share mosaic-making stories: Accessible explanations for the connections behind our data-mosaics are just as important as the data themselves. When teams work with data from different disciplines, notions of what is accessible must expand beyond that data being simply public to something understandable by a layperson, experts, and machines alike (Thorp 2021). Sharing the stories behind our connections allows wider audiences to question and build upon new knowledge created.

We will give an idea of data-mosaics using three case studies from the architectural and research projects of KieranTimberlake that incorporate modeling practices for trees, environmental impacts, and human thermal comfort. These are data-linking, mosaic-making efforts that show an informed urban environment. They are possible because of our firm’s commitment to transdisciplinarity and an integrated research group composed of individuals who represent fields ranging from architecture and art to urban ecology, industrial ecology, environmental management, physics, and computer science. We have shaped these efforts through “wait, what moments,” which we understand as unplanned and spontaneous instances where one individual’s knowledge becomes amplified through another’s—affording a glimpse of a potential data-mosaic (Ryan 2017). Together and for more than a decade, we have had numerous and diverse opportunities to explore the relationship between architecture and its environment, socialize knowledge customarily bound to a single discipline, and use our agency to create new workflows and new knowledge for architecture. We describe our work through a range of disciplinary terminology, methods, data, and

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datasets, which are essential to a body of work that seeks to expand and challenge the system boundary of architecture massively.

3.7 Seeing a Data-Mosaic by Modeling Trees The growth and preservation of urban tree canopy (UTC) is an established goal for decision-makers implementing city-level sustainability and climate action plans (Baltimore County 2017; Grove and Locke 2011; Hermansen-Baez 2019; Trees Atlanta 2015). KieranTimberlake’s intent to demonstrate the value of UTC began with an effort to represent vegetation in architectural models, which continues to instigate essential “wait, what” moments within our research group (KieranTimberlake 2019). This work represents a convergence of thought within our practice that seeks to understand the overall value and function of vegetated landscapes to mitigate heat gain, regulate stormwater runoff, increase outdoor thermal comfort, and improve the value of public open space (Piana and Carlisle 2014). Our work began with a dataset from 57 sensors embedded in varying depths within an existing masonry wall capturing temperature data at 5-min intervals (KieranTimberlake 2013). We meant to use these data to determine the wall’s rate of heat loss, which we did. These data, collected through winter and spring, also captured the thermal buffering effects of vegetation, as trees several feet from the wall bloomed, leafed, and shaded the building. Tree shade will offset a building’s thermal load. For instance, one study suggests that shade can reduce seasonal cooling energy needs by an average of 30% (Akbari et al. 1997). However, trees are dynamic, varying significantly in form, growth pattern, and seasonality (Chokhachian and Hiller 2020). In our experience, trees are rarely integrated into building energy models or solar studies despite their benefit, thereby excluding the opportunity to study and measure a viable design strategy for reducing operational carbon emissions. The insight provided by these data suggested an immediate bridge between energy modeling and ecosystem services modeling. It also meant expanding the system boundary of both modeling practices. However, first, we needed to answer two fundamental questions: How do you see a tree? And how do you represent a tree in a model whether ten or 10,000? These are data resolution and representation questions. The primary technical challenge was determining tree-species-and season-specific shading coefficients , and the amount of light passing through a tree. We explored various approaches to tackle the resolution question with the basic agreement that we would attempt to include trees in a range of modeling efforts. A descriptive approach suggested abstracting a tree’s height and crown volume into its basic shape—cone-on-stick or ball-on-stick—and connecting each basic form to solar data and growth curves. Ultimately, we developed an approach to connect these basic shapes to higher-resolution data. Next, we worked with eleven significant trees that grew in a campus quad adjacent to several university buildings. We measured the crown width, crown base height,

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and overall tree height for each tree. We took four readings of each tree canopy, one in each cardinal direction, to derive an average gap fraction. This quantity represents the proportion of sky visible through the canopy, which depends on leaf density and shape. Additionally, we compared the quality of our measurements to data from highresolution images using software to calculate the leaf area index, gap fraction, average leaf inclination angle, fraction of absorbed photosynthetically active radiation, and vegetation cover fraction (KieranTimberlake 2019, Fig. 3.1). Ultimately, we could

Fig. 3.1 Cross-referencing digital and analog methods for canopy analysis using spherical densiometer (not pictured), hemispherical photography (top), and proprietary tools like LI-COR’s LAI-2200C (bottom)

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test the impact of trees on building envelope performance, integrating this highresolution spatial data into a building energy modeling workflow for design options. Mature and dense vegetation contribute seasonally to shading and solar access for buildings and courtyards. A site’s vegetation is often represented in tree databases maintained by forest managers who collect basic tree geometry and phenotype data based on standard forestry methods through ground measurements and categorization. For a site with more than 10,000 trees we used tree survey information and species-specific data to approximate tree geometry and type-specific parameters to assign shading properties which necessitated four factors per tree: location, height, crown volume, and shading coefficient. Tree locations were brought into modeling software and parameterized using a custom script that combined tree survey data from a spreadsheet, tree locations exported from GIS, topography from a 3d model, and basic tree shapes. Once we represented tree massing, we imported this model into a digital site model that combined proposed massing scenarios, surrounding buildings, topography, and existing trees. Within an integrated architecture and landscape model, we conducted solar studies using shading coefficients for conifers, broadleaf deciduous, and evergreen trees that took seasonal differences of leaf-on and leaf-off into account. Assembling a data-mosaic allows us to debate representations of entities like trees in the context of a model’s goal and intended use. We may need to test one or several modes of abstraction and representation for a given model and be open to new hybrid models and new uses for models. In some instances, it is possible to access high-resolution data sets to make calculations. In other cases, on-site measurement and approximation are necessary. While these types of data evidence issues of model quality and appropriateness through data granularity, they also suggest that mosaic-making offers the profound potential for expanding the system boundary of architecture.

3.8 Seeing a Data-Mosaic by Modeling Embodied Environmental Impacts Through their extraction, manufacture, use, and disposal building materials acidify oceans, deplete the ozone, and warm the earth. Constructing and operating buildings accounts for over one-third of global carbon emissions (IEA and UNEP 2018). Cities currently account for almost three-quarters of global carbon emissions. Our planet’s rapid projected urbanization, the equivalent of building an entire New York City every month for 40 years, means that cities are and will continue to be significant drivers of embodied carbon (IEA and UNEP 2017). Unlike operational carbon, which cities can potentially mitigate through future renewable energy sourcing and renovation, embodied carbon is irreversibly released before building occupancy. Managing our planetary carbon budget is an immediate and gnarly problem that requires more comprehensive life cycle thinking about materials over time. Our effort to model

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and measure the environmental impacts of building materials navigates multiple aspects of a data-mosaic: gap-ridden multi-domain data, fuzzy contested boundaries, numerous scales and granularities, and constant epistemological questions. A building is not a single product but a complex and dynamic system. Thousands of materials and processes come together at the point of construction and in fits and starts over the building’s entire life cycle. Life Cycle Assessment (LCA) models have traditionally been the domain of certified practitioners who perform analysis after an owner completes a building project. We began digging more deeply into the modeling practice of LCA to drive our design decisions using relative life cycle impacts as early as possible—developing a feedback loop at scales smaller than the whole building. Moreover, advancing a single firm’s practices would not be enough to create meaningful impacts—we needed to scale beyond our projects. The desire to democratize such a complex multi-domain problem presented our team’s first “wait, what” moment. How can we adopt one discipline’s modeling practice to give agency to a non-expert audience? The development of Tally®, a Life Cycle Assessment application, challenged us to examine assumptions regarding designers, their practice, and their agency advocating for climate action through design. To do this, over the last decade, we have engaged the knowledge and modeling methods associated with Ecology, Atmospheric Chemistry, Architecture, Materials Engineering, and Climate Science. We quickly ran into a few challenges in performing LCA for the built environment. For impacts, LCA relies on having a complete picture of the buildings we construct. How do we catalog the entirety of a building before it’s built? How do we source impact data for architectural assemblies and their constituent raw materials? To do this, we needed to expand beyond our disciplinary bubble and partner with the data provider Sphera to leverage the GaBi LCI database. As we began our role as data co-arbiters, we leveraged the first principle of mosaic-making—asking whose data is here, why, and what is missing? Early in the life of Tally, we found ourselves in the middle of an industry developing new data values and conventions. Accounting for biogenic carbon, the carbon transferred between nature and the technosphere as bio-based materials grow, combust, or degrade, was in flux. At our first software release, there was no standard for inclusion and an ongoing nuanced debate on uncertainty (Breton et al. 2018). Forests are significant carbon sinks, but drastic land-use changes or natural disturbances can turn them into equally significant carbon sources (Breton et al. 2018). Forestry is potentially the solution for climate change and simultaneously one of its primary drivers. What could we ethically convey? This time, the “wait, what” moment came from opposing industrial councils. Each material industry representative saw the inclusion of a new sequestration source as either premature and unfairly skewed, or not a moment too soon for a warming planet. We ultimately relied on the precautionary principle to guide us for that first release—waiting for ISO 21930:2017 to recommend the inclusion of biogenic carbon four years later (ISO 2017). This constant questioning of what degree we can know something would continue throughout our development.

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Our industry may think we have a complete database of architectural elements in our Revit Building Information Models. However, a Revit wall does not contain the necessary resolution for performing an LCA analysis. The objects typically invisible to BIM matter, such as stud spacing, fasteners, and adhesives. What began as a dense tracking spreadsheet trying to identify all materials in a building evolved into a Revit plugin that allows designers to scaffold more detailed vetted assemblies onto existing models using Revit materials (Fig. 3.2). This additional layer of assemblies and application rates references low-level material data from Sphera without requiring detailed modeling. We designed the interface to not only allow for the speed needed in design stage modeling but to educate the user in real-time through the user interface (UI) and interaction (UX) cues—leveraging Revit geometry and design options to define the scope for comparing potential iterations (Fig. 3.3). In a data-mosaic, data, model, and interface are tightly coupled. The development of the intermediate data between material and architectural assemblies and the interface to apply it was a constant negotiation between the needs of designers, building materials manufacturers, and LCA best practices. For example, we decided to set default but editable values for service life, a prediction of material and system maintenance, repair, and replacement over time, by Tally material in each assembly (Grant and Ries 2013). This was an attempt to bridge the gap between novice and expert modelers—sensible defaults with the ability to adjust for edge cases. The “wait, what” moment of the industrial council emerged again: “How could you put 30 years for Gypsum?” The service life heavily depends on the application context—a material in one architectural context could require much less maintenance than another. Our editable defaults needed to be legible to designers, but there was again a more profound question of just what we could faithfully characterize. What data was available and appropriate? Where were neutral

Fig. 3.2 Tally Revit plugin architectural component and quantity takeoff interface

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Fig. 3.3 Tally Revit plugin results visualization comparing potential cladding options

citable sources of service life beyond those of industry? How do we communicate these nuances without overwhelming modelers? When assembling a data-mosaic, these epistemological and transdisciplinary questions are a necessary practice. Tally has been an act of advocacy involving as much teaching as coding. Over the years, Tally hasn’t changed that much, but the community has grown. There was little literacy on embodied carbon and environmental impacts and minimal incentive for manufacturers to track or publish this data, but that has changed (Adams et al. 2019; C40 Cities 2022; Huang 2019; LETI 2020; Lewis et al. 2021; Simonen et al. 2017). Through additional partnerships, we expanded the scope of analysis from design to procurement and construction. For example, we made an exporter to Building Transparency’s EC3 tool that leverages the bill of materials Tally creates towards actual products, not performance-based specifications. To make our tool genuinely accessible, we eventually gifted Tally to Building Transparency, an organization that provides “open access data and tools necessary to enable broad and swift action across the building industry in addressing embodied carbon’s role in climate change (Building Transparency 2020).” Most importantly, because of increased embodied carbon literacy in the design sector, cities have been pressured to take action with the Carbon Neutral Cities Alliance (CNCA), committing funds to policies that will “aggressively shift the fundamental attributes of the systems that have caused the climate crisis (CNCA 2022).”

3.9 Seeing a Data-Mosaic by Representing Human Comfort Whereas Tally attempts to tackle the embodied balances of our planetary carbon budget, our occupant comfort survey application Roast™ aims to respect the occupants’ experiences within our buildings while we lower our buildings’ operational

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carbon. We spend most of our time indoors (Khajehzadeh and Vale 2017; Leech et al. 2002; Schweizer et al. 2007). Office environments have typically been spaces with consistent temperatures driven by mechanical conditioning. A more detailed understanding of the drivers behind occupant comfort can allow for more energy-efficient building operation while respecting occupants’ needs and improving the wellness of people and communities. KieranTimberlake’s move to the former home of Ortlieb’s bottling plant provided a unique opportunity to model an existing site at a new resolution and evolve our understanding of the many facets of occupant comfort. As we attempted to meet our ambitious goal of a naturally ventilated building in Philadelphia and eventually scale these lessons learned beyond our practice, we engaged mosaic-making via epistemological challenges of data acquisition and representation, new correlative practices, bridging disciplines, and expanding our social and economic terrain. We installed over 270 sensors to measure temperature and relative humidity to supplement our early energy models across roughly 6,500 m2 . Anecdotal conversations quickly indicated that our team had varying levels of success acclimating to an entirely naturally ventilated building. Even when we hit traditionally acceptable temperatures, they weren’t experienced uniformly. As a result, we needed to test our past models empirically, and thermal comfort studies were an essential method to capture the nuances of individual adaptation and perception. We leveraged our background in comfort science to frame two corollary endeavors: the dissemination of a longitudinal study across two cooling seasons and a web-based survey application that would eventually become our commercial product Roast. We needed to build out the actual questions we would ask our occupants across time and space while navigating a delicate balance between standards and speed. The initial web-based survey included only five inputs: employee ID, attire, thermal sensation, recent activity, and location. To increase the speed of survey taking, we built out typical outfits using clo values, a measure of clothing’s thermal insulation. We leveraged the Bedford 7-point scale (much too cold, too cold, etc.) despite its age because it effectively combined two ASHRAE thermal comfort scales, thermal sensation (hot to cold) and thermal acceptability (satisfied to dissatisfied), giving us layperson legibility and speed without sacrificing fidelity (ASHRAE 1992; Bedford 1936; McIntyre 1976; Vesely et al. 2015). This hybridized and lean survey was emailed to all staff every weekday at 10 am and 4 pm, buffered from typical arrival and departure times to have the greatest number of responses and avoid increased metabolic rates from commutes. Interior and exterior temperature and relative humidity were captured in parallel, but in a surprising “wait, what” moment, we found little difference in our granular interior sensors despite our passive system. Our adventure in mosaic-making already had us surgically combining standards for the problem at hand and grappling with the characterization of temperature, activity, space, and even the clothes we wear. At the end of our seasonally bracketed study, we had amassed 9,889 survey responses and temperature measurements from a mix of 130 people. After a few incentivizing raffles to combat survey fatigue, our response rate of 39% was just enough to meet the ASHRAE 55–2013 criteria of 35% (Elsworth et al. 2018). In

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Fig. 3.4 Roast thermal comfort survey results indicate that less than 80% of the population was comfortable when temperatures exceeded 28.5 °C

combination with our sensors, we were able to determine several trends. Over 80% of the office responded comfortably at 28.5 °C or below—well above the typical setpoint for office space (Fig. 3.4) (Elsworth et al. 2018). Even with our nightly ventilation, the building interior started the day 2.8 °C warmer than the exterior temperature. Surprisingly, humidity wasn’t a factor in occupant comfort, most likely due to the high amount of air movement from our array of fans. Finally, we learned that clothing helped maintain thermal comfort until 26 °C, at which point overall comfort started to decline (Elsworth et al. 2018). These operational and analytical insights prompted a fresh look at our survey application built for a broader audience, with a more comprehensive set of inputs developed by a broader range of collaborators. If we truly wanted to help tackle climate change and make our planet’s structures more responsive to the people who inhabit them, we needed to increase the scope of human comfort within our data-mosaic. We added air movement and quality questions to be in greater dialogue with larger stores of surveys like the ASHRAE Global Thermal Comfort Database II (Földváry Liˇcina et al. 2018). To expand our understanding of occupant comfort’s multivariate nature, we included questions on visual comfort, productivity, and mitigating factors. Critical to these additions was a continuous referencing and questioning of ASHRAE and ISO standards while balancing legibility, interoperability, and brevity (Fig. 3.5). In parallel with our ongoing methods investigation, we collaborated with copywriters, web designers, and database engineers to build a scalable, responsive web application that could run on any device or browser. This broader group helped hone a survey that met our performance standards while remaining intuitive to navigate and avoiding any jargon that could confuse the survey taker. We created standardized visualizations so that our users could consistently communicate changes temporally and spatially (Fig. 3.6). Our willingness to share these comfort visualizations with our users was a vast “wait, what” moment for our thermal comfort scientist—a strong advocate for blind studies. In the end, we acknowledged the value of blind studies for academic

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Fig. 3.5 Roast occupant comfort survey question cards and answers

Fig. 3.6 Roast thermal comfort survey visualization across time (days) and space (floorplate)

integrity and social exchange to combat survey fatigue—enabling multiple settings for no results, just a user’s results or all results to be shared. We also understood the need for transparency, so we built in exports for any set of data and an API that allows for reintegration into whatever interface a building owner, architect, or researcher desired—filling the previously patchy stores of human comfort within our data-mosaic.

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Solving the problems of our cities requires a scale of intervention and collaboration beyond projects, beyond narrow definitions of design. Our mosaic-making has allowed us to engage in broader dialogues and advocate for causes we believe are crucial to the profession’s advancement.

3.10 Traversing a Data-Mosaic: How We See Now We have walked through practice-based data narratives to see, know, and interact with data-mosaics. While our primary disciplinary domain limits us, we claim the agency of mosaicking for a broad range of disciplines shaping actions in the built environment. At the outset of this endeavor, we foreswore any presumption of holism. Our approach to assembling a data-mosaic attest to this. In other words, our aim was not to present data-mosaics as all the bits of data and the greater sum thereof. We contend that a data-mosaic, fragmented and ever-expanding, is less of and less helpful as a formal, instrumentalized framework. Instead, we believe a data-mosaic to be an assemblage, when directly engaged by inter-and transdisciplinary teams, helps to expand our agency to see now. We embrace the need to see now in two ways: In the first, seeing now is a point-intime when a team must see as far as possible into the present, challenging presumptions about types of knowledge, useful and less useful data, and the delineation of a system and its boundary. In the second, seeing now evokes a sense of urgency for our future. What is needed now from our disciplines? How are present decisions and conventions connected to future outcomes for people and cities? Mosaic-making is, in its simplest form, an active conversation that requires participation. Acknowledgements In the spirit of our transdisciplinary approach, the authors would like to acknowledge the contributions of several members of KieranTimberlake to these projects, including Collin Barlage, Roderick Bates, Frannie Bower, Stephanie Carlisle, Merlin Cherian, Chad DotsonJones, Alex Knipe, Christian Kraft, Lily Lauben, Max Piana, Carly Regn, Kevin Tayah, Alison Worthington, Matt Wagar, and Carin Whitney. In addition, Billie Faircloth would like to acknowledge her 2019 Keynote at the Symposium on Simulation for Architecture+Urban Design that explored transdisciplinary modeling practices and inspired further inquiry into data-mosaics.

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Billie Faircloth, FAIA is a practicing architect, educator, and partner at KieranTimberlake, where she leads a transdisciplinary research team in order to define a relevant problem-solving boundary for the built environment. Billie has published and lectured internationally on research methods for a transdisciplinary design practice; the production of new knowledge on materials, climate, and thermodynamic phenomena through the design of novel methods, tools, and workflows; and the history of plastics in architecture to demonstrate how architecture’s approach towards transdisciplinary practices and new knowledge has changed over time. Her articles have been published by the Journal of Architectural Education, Princeton Architectural Press, and Royal Danish Academy of Fine Arts. Prior to joining KieranTimberlake, she was an assistant professor at the University of Texas at Austin School of Architecture. She is the recipient of Architectural Record’s Women in Architecture Innovator Award in 2017. She is presently an adjunct professor in the Environmental Building Design program at the University of Pennsylvania’s Weitzman School of Design. Christopher Connock leverages building design and computation toward an effective, agile, and imaginative practice. As KieranTimberlake’s Design Computation Director, Christopher explores topics related to construction systems, digital fabrication processes, hardware-software interfaces, and informatics. He has applied this knowledge across a variety of projects including a sustainable multi-use building for New York University and an affordable, quick-to-build housing solution for India’s emerging middle class. Christopher has also created immersive virtual environments and an array of bespoke software tools for architectural design projects. His portfolio includes Pointelist™, a wireless sensor network, as well as Tally®, a Life Cycle Assessment plugin for Revit, and Roast™, a customizable comfort survey app. Christopher lectures on generative and parametric design, application development, immersive environments, data visualization, and digital fabrication at a number of conferences and universities. He co-taught a research studio at the University of Pennsylvania’s School of Design, and has sat on juries at Yale University, the University of Pennsylvania, the University of Virginia, the Pratt Institute, Drexel University, the New Jersey Institute of Technology, and Temple University. Ryan Welch develops tools and workflows that guide data-driven design thinking. Specializing in building performance, Ryan creates workflows and ways of envisioning data to solve architectural problems, primarily through the development of new software tools. Ryan is the lead software, database, and user experience developer for Tally®, a life cycle assessment application that allows architects to calculate the environmental impact of building materials. Ryan’s previous projects include Pointelist™, a high-density wireless sensor network, and Roast™, a customizable

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thermal comfort survey application. Ryan worked with Rice University to develop a custom interactive dashboard that enabled the university to study master planning scenarios. He also led the development of custom digital workflows for the US Embassy in London. Ryan has taught architectural design at Cooper Union, Yale University, and Princeton University. He has also taught workshops with KieranTimberlake Partner Billie Faircloth at Harvard University, the University of Minnesota, the Royal Danish Academy of Fine Arts, and the University of Pennsylvania. Ryan has lectured across the country and overseas at SimBuild, the Design Modeling Symposium, and the Passive and Low Energy Architecture (PLEA) Conference. Kit Elsworth interprets research and analysis to generate actionable conclusions that guide design decisions for firm projects and influences the architecture industry. As KieranTimberlake’s Building Performance Specialist and member of the firm’s interdisciplinary Research Group, his work leads him to proactive and project-specific research in occupant comfort, passive building systems, energy analysis, consultant engagement and workflows, and post-occupancy building performance. Kit is a member of the AIA’s Committee on the Environment 2030 Working Group to help evolve the AIA 2030 Commitment and increase industry participation. He teaches Building Performance Simulation at the University of Pennsylvania, and has served on juries at the University of Pennsylvania, Temple University, and Jefferson University. Before working at KieranTimberlake, Kit received his Master of Building Science and Sustainability from the University of California, Berkeley. Kit’s thesis work focused on human thermal comfort at high metabolic rates in warm environments with the use of personally controlled fans to maintain comfort. Elizabeth “Efrie”Escott, AIA explores materials, digital technologies, and environmental systems as an Associate in the Research Group at KieranTimberlake. As a researcher and licensed architect, she works with design teams to translate data-driven research into design practice. Efrie leads KieranTimberlake’s efforts to reduce embodied carbon, in addition to being a member of the award-winning development team for Tally®, a BIM-integrated tool that measures environmental impacts. Efrie also co-leads KieranTimberlake’s taskforce promoting climate advocacy, sustainable design, and human health in the built environment. Efrie lectures internationally about environmental research and teaches at the University of Pennsylvania. She was a member of the USGBC Materials and Resources Technical Advisory Group, the founder of Philadelphia’s Dynamo User Group, and is a current AIA Philadelphia Women in Architecture Co-Chair. Before joining KieranTimberlake, Efrie studied architecture and theoretical physics at Yale College. She received a Master of Architecture from the University of Michigan and a Master of Environmental Management from Yale University, where she conducted research in industrial ecology and green chemistry.

Chapter 4

The Role of Information Modelling and Computational Ontologies to Support the Design, Planning and Management of Urban Environments: Current Status and Future Challenges Cédric Pruski

and Defne Sunguro˘glu Hensel

Abstract Over the past decades the understanding of urban environment has undergone profound changes. This has been accelerated by the advent of information science and big data, and the ever-increasing quantity of data produced by smart devices located in urban areas and remote-sensing. This development has been accompanied by advanced information and communication technologies designed to take advantage of this data deluge. Computational ontologies have been proposed to turn this data into knowledge that can be exploited for a variety of tasks ranging from information retrieval to decision support. In this chapter, we review how recent approaches for information modelling in urban environments implement computational ontologies. This survey highlights the problems these approaches intend to solve, as well as their limitations. Based on this analysis, a roadmap of future research is drawn that needs to meet the environmental and ecological challenges raised by urbanization. Keywords Urban environment · Smart city · Green city · Ontology · Information modelling · Knowledge representation and reasoning · Decision-support

C. Pruski (B) Luxembourg Institute of Science and Technology, 5, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg e-mail: [email protected] D. S. Hensel Southeast University, Si-Pai-Lou 2, Nanjing 210096, Jiangsu Province, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_4

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4.1 Introduction Urbanization is a major trend in world population and land use change, and the consequent land and resource degradation poses one of the greatest environmental challenges of the 21st Century (Seto et al. 2010). More than half of the world population now lives in cities and this ratio is on the rise. Urban areas are growing faster than, or as fast as their populations (Güneralp et al. 2020), and the impact of this expansion goes far beyond the city borders. Urban expansion and densification, is a major threat to agriculture and food security, as well as to other semi-natural and natural land systems, the disappearance and abandonment of which puts biodiversity, human health and well-being, and livelihoods at risk. On the other hand, urbanization presents an opportunity for conservation, restoration, climate change, and environmental response in general, since the largest impact can come from change in urban environments. To achieve this, decision makers rely on knowledge, science, empirical evidence, and theories on how to design, plan, and manage cities and urbanization (Solecki et al. 2013). Urban data is an enabler and is now available in vast quantities (Runting et al. 2020). However, data in and of itself is of limited added value if it is not turned into knowledge to support decision making in the design, engineering, planning, management, maintenance, and governance of urban environments and systems across scales. In addition, the results of data-driven urbanism, architecture, design and construction, and landscape (Kitchin et al. 2017; Alina et al. 2016; Walliss and Rahmann 2016), which is greatly advancing is yet to deliver the expectations from data exploitation to provide the kind of insights that can facilitate transition to sustainable and ecological urbanisation. Moreover, decision-support is particularly important in cases where decisions have to be data-driven, evidence-based, knowledge intensive, and context-specific, and where trial-and-error and intuition cannot be afforded. The need to address this challenge in urban environments has never been so urgent, in the process of constant adaptation to changing and increasingly more complex requirements, conditions, and sustainability challenges (Alberti et al. 2019). Converting digital data into useful knowledge is a computer science challenge where the models that represent a domain through concepts and relationships called ontologies are useful. These have emerged mainly through the Semantic Web initiative and aim at representing the knowledge of a given domain in a logic-based manner providing great capabilities to solve complex data and knowledge intensive problems such as semantic interoperability, information retrieval, knowledge discovery or decision support. However, contrary to disciplines like medicine where ontologies have been used for decades (Noy et al. 2009), their utilization for addressing problems in urban environment is relatively new and is gaining the interest of many communities (Falquet et al. 2013; Teller et al. 2007; Komninos et al. 2016). For this reason, it is important for potential users to understand what ontologies are, how they have been used to approach existing problems in urban environment and what is the potential of ontologies for tackling future challenges. In this chapter, we survey recent approaches implementing computational ontologies in the urban environment domain. Unlike other reviews where the assessment of ontology development have

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been focused on particular contexts, such as Industry 4.0 (Kumar et al. 2019), urban development (Teller et al. 2007), Remote Sensing (Arvor et al. 2019), Smart City (De Nicola and Villani 2021), building automation (Butzin et al. 2017), construction (Zhong et al. 2019), and construction geometry (Wagner et al. 2020), this chapter expands the research to explore the plurality of approaches found in the domains of urban environment and domain particular tendencies. The goal is to provide an overview of these approaches. We start with a definition and description of the various type of used ontologies, the various tasks these ontologies are useful for, as well as their advantages and drawbacks. We then put in perspective the current challenges in urban environments with the limitations in the approaches to the implementation of computational ontologies for future research on green urban environments. The chapter is structured as follows: Sect. 4.2 defines key concepts such as ontologies and urban environment. Section 4.3 describes advantages and limitations of existing works that implement computational ontologies in urban environment. Section 4.4 discusses the analysis of trends and open challenges. Section 4.5 wraps up with concluding remarks.

4.2 Background In this section we review the definitions of ontology and urban environment used in the reviewed existing works.

4.2.1 Ontology Since the advent of the Semantic Web, ontologies have been accepted in computer science as a way to formally represent knowledge. Ontology is usually defined as “a specification of a conceptualization” and describe a domain of discourse through the definition of the concepts of that domain, their properties and the relationships between them in a consensual manner (Guarino 1997). Since ontologies have solid logic grounding, they have many different properties that are useful for solving problems in an automatic way ranging from information and knowledge structure, information retrieval, to knowledge discovery and decision support as illustrated in Sect. 4.3. Three types of ontologies can be distinguished. When ontologies are focused on a particular domain they are referred to as domain ontologies. There also exist more general and domain-neutral ontologies like DOLCE or SUMO referred to as upper (or generic) ontologies. When they are tailored for problem-solving or carry out a complex task (expert system) then they are referred to as application ontologies.

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4.2.2 Urban Environment Urban environments are characterized as human-dominated areas where land conversion is happening at the highest rates (Güneralp et al. 2020), most resources are consumed (Forum 2018), where vulnerability to disasters is increased (Hamstead et al. 2021), and nature conservation and restoration is most needed (Dover 2015) and most difficult (Garrard et al. 2018) to ensure protection of ecosystems and ecosystem services, and human health and well-being. Decision making in the built environment relies on the combination of various types of data. A number of developments have been at the forefront in utilizing data to solve urban problems, which contributed to the generation and at the same time to the silofication of urban data. In the context of the built environment, semantic interoperability challenges (e.g., pertaining to connecting CAD and GIS, and other platforms) have triggered the proliferation of domain ontologies, tailored mainly to meet knowledge/project management, and information and work flow, and retrieval needs. In order to understand how ontologies have proliferated in urban environments, it is important to understand the contexts, and delineate the different concepts of urban environment with practical implications. One of the most significant developments is the transition to the current 4th industrial revolution, termed industry 4.0 (I4.0). This was an outcome of a significant technological progress in the areas of digital data, access, connectivity, and automation. One of the main ideas behind I4.0 is that multiple intelligent systems, including human and machine (software and hardware) agents, can interact and semantically communicate in a reliable and secure way, where semantic web and information technologies such as ontology have a key role. In this context, the urban environment is considered as a cyber-physical system focused on the interaction between people and technology, where ontologies have found their way into robotics, construction, and manufacturing (Kumar et al. 2019). The Smart City concept is equally a strong influence with impact on urban design, planning, and management in the pursue of a better response to urban problems through utilizing big urban data and semantic web, artificial intelligence, IoT, remote sensing, and GIS. The Smart City approaches mainly perceives the urban environment as a social-technological system focused on technologyenabled smart environment (e.g., equipped with sensors) and human interactions (Kitchin et al. 2017). At the building scale, the digital transformation in the AEC industry, in particular through computer-aided design (CAD), computer-aided engineering (CAE), and building information modelling (BIM) is rapidly changing the creation, management, and exchange of building data in the whole life-cycle process of a construction project. In the AEC context, the urban environment is typically considered as a technological system. In the contexts of urban ecology, landscape management, environmental planning, and urban agriculture for example, the urban environment is increasingly viewed as a social-ecological system. It is considered as complex and adaptive system of interacting bio-geophysical components and social actors, and which provide essential ecosystem services to humanity. Even though there exist works that apply ontologies to bring ecology into urban decision making (Cormenzana et al. 2014), they are relatively few in comparison. In all of

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the aforementioned approaches, the urban environment is either viewed as a cyberphysical, social-technological, technological, or social-ecological system, except in the perspective of urban environments as social-technological-ecological systems (SETs). The latter provides the most integrated approach that seems most suited to guide transition to green, sustainable, resilient, and smart cities (Hamstead et al. 2021).1 It is in this context, human-driven environmental transformation and digital technologies are considered as key to the understanding and shaping of ecological urbanization. However, the potentials of ontologies, is yet to be recognized, especially in the context of knowledge production, preservation, recovery, integration, discovery, and adaptation challenges (Kim et al. 2021; Muñoz-Erickson et al. 2017).

4.3 Ontologies for Urban Environments In this section, we review existing approaches dealing with ontologies in urban environments. We present standards used in ontology engineering, upper and domain ontologies, and highlight the various applications they are supporting. To do so, we have applied a systematic method to retrieve relevant literature from major portals of the fields of urban environment and the Semantic Web as well as catalogues describing available ontologies like LOV4IoT2 or AgroPortal.3

4.3.1 Standard Ontologies Standard ontologies are defined and managed by organisations like the W3C, ensuring their accessibility and the maintenance of their content over time. While standard ontologies are numerous, we review in this article only those that are specific to the urban environment field.4 IfcOWL5 is an ontology expressed in the OWL standard of the W3C that represents the Industry Foundation Classes (IFC) Schema, which is the open standard for representing building and construction data (Pauwels and Terkaj 2016). IfcOWL allows the representation of building data as directed labelled graphs that aim at facilitating the alignment of building data to other relevant models like Sensor models or geographic information models. A typical example of the added value of ifcOWL is provided in (Hu et al. 2021) where the ifcOWL ontology is used to improve the

1

https://smartergreenercities.com. https://lov4iot.appspot.com/?p=ontologies. 3 http://agroportal.lirmm.fr. 4 Other ontologies like W3C SSN, SOSA, W3C WoT Thing Description or EMMO have been excluded since they do not cover the whole urban environment field. 5 https://github.com/buildingSMART/ifcOWL. 2

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integration of data coming from various sources to take decision in the building management process. Recently (Vinasco-Alvarez et al. 2020) proposed to implement semantic web technologies to turn multi-dimensional urban data into linked urban data models. CityGML 2.0 Ontology6 converts CityGML application schema based on geographic markup language (GML) into an ontology based on Web Ontology Language (OWL). CityGML is an extendable spatial data standard issued by the Open Geospatial Consortium (OGC) for storage and exchange of semantically rich 3D city and landscape models (e.g. ArcGIS). It focuses on the 3D and semantic representation of topography including terrain, building, vegetation, and water bodies. A comparable standard in the context of BIM is the Industry Foundation Class (IFC), which already exists as an ontology standard (IfcOWL) for representing building and construction data. In order to provide means for tagging equipment, nodes, data, and locations within a building in a standardized format the Brick Schema7 has been developed. It is an ontology-based metadata schema that captures the entities and relationships necessary for effective representations of buildings and their subsystems. The schema describes buildings in a machine-readable format to enable programmatic exploration of different operational, structural and functional facets of a building. The Smart Applications REFerence Ontology8 (SAREF) is a standard model managed by ETSI that describes, at a high level of abstraction, the core concepts of the smart applications domain and relationships between them. SAREF has been designed taking into account reuse and alignment, modularity, extensibility and maintainability. This ontology has been further extended, through the SmartM2M initiative, to various subdomains with a family of dedicated ontologies. Among these, there is the SAREF4ENVI ontology for the environment domain aiming at solving the lack of interoperability between sensors that can measure and share information about light pollution.

4.3.2 Domain and Upper Ontologies for Urban Environments In this section we review existing domain ontologies specific to the urban environment domain and describe their underlying upper ontology (if any). As domain ontologies are numerous, we provide a selection of ontologies specific to the urban environment domain to introduce various aspects important for the ontology life cycle such as the underlying ontology language, construction methodology and tools. In (Kuster et al. 2020), Kuster et al. introduce the UDSA ontology for real time urban sustainability assessment. It is constructed following the NeOn methodology (Suárez-Figueroa et al. 2015) based on the reuse of existing (standard) ontologies 6

https://github.com/VCityTeam/UD-Graph. https://brickschema.org. 8 https://saref.etsi.org/core/. 7

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(SSN, DUL, QUDT and GeoSPARQL) some of which are grounded in upper ontologies. The main goal of the ontology is to give the user insights on the impact of their actions on the sustainability indicators, criteria and themes (see Sect. 4.3.3). UDSA has been experimentally evaluated using SPARQL representation of the competency questions the ontology has to answer. The SPARQL queries were evaluated on datasets collected from various sensors on the site of Ebbw Vale in the UK. In (El-Diraby and Osman 2011), the authors present a domain ontology to formalize construction concepts in urban civil infrastructure products that focuses on their functions, roles and semantic attributes to facilitate human representation of their construction knowledge. The ontology was created using Protégé, following Grüninger & Fox methodology (Grüninger and Fox 1995) and has been reviewed by domain experts. The work of Kominos et al. describes the Smart City Ontology (SCO) (Komninos et al. 2016). It is a high-level ontology about the Smart City domain build from scratch but directly inspired from existing smart city ontologies (and their applications) like SOFIA (from Coruna in Spain) or the SCRIBE ontology developed by IBM. The authors further illustrate how existing ontologies on smart cities can be aligned with the SCO in order to underline the completeness of SCO. Their ontology is build using the Protégé application and is grounded on existing upper ontological framework like SKOS, and FOAF. In (Berta et al. 2016) Berta et al. describe the Urban Morphology Ontology (UMO) for describing, in generative terms, the structure of urban fabrics. The goal is to support urban designers in the early stages of design process, like street pattern and massing definition, by generating in real time a number of design scenarios, starting from a large number of constraints and requests. UMO has been designed (and made available) via the WebProtégé applications and has been validated through the comparison of twelve case studies in recent urban development. Recently, the Building Topology Ontology (BOT) (Rasmussen et al. 2021) was published by the Linked Building Data Community Group of the W3C to describe, in OWL DL, relationships between the sub-components of a building. It was suggested as an extensible baseline for use along with more domain specific ontologies following general W3C principles of encouraging reuse and keeping the schema as simple as possible. The Digital Construction Ontology Suite11 (DICO) aims at making the semantics of the data produced at each lifecycle stage in digital construction understandable to human and software agents. DICO’s objective is to describe the relevant objects including physical and spatial entities, temporal regions, information contents, agents, activities, and groupings of objects as well as their properties (relationships and attributes). The aim is to improve the management and execution of construction or renovation projects through facilitating the integration of information coming from disparate sources, generated by BIM, data collected on construction sites through mobile devices, sensors and scanners etc. (Table 4.1).

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Table 4.1 Reference table. The referencing number given in this Table is used for the cited work in Table 4.2 Number

References

Number

References

1

An et al. (2019)

20

Martinez et al. (2019)

2

Barramou et al. (2020)

21

Med et al. (2017)

3

Beck et al. (2010)

22

Mignard and Nicolle (2014)

4

Belgiu et al. (2014)

23

Mirarchi et al. (2020)

5

Boussuge et al. (2019)

24

Movshovitz-Attias et al. (2015)

6

Calafiore et al. (2017)

25

Moradi et al. (2018)

7

Cormenzana et al. (2014)

26

Myers et al. (2017)

8

Chowdhury and Schnabel (2018)

27

Orciuoli and Parente (2017)

9

Daneshfar et al. (2020)

28

Pileggi and Hunter (2017)

10

El-Diraby and Osman (2011)

29

Psyllidis et al. (2015)

11

Howell et al. (2017)

30

Qi et al. (2020)

12

Hu et al. (2020)

31

Rasmussen et al. (2021)

13

Huang et al. (2020)

32

Santos et al. (2017)

14

Karimi et al. (2021)

33

Terkaj et al. (2017)

15

Komninos et al. (2016)

34

Vergara-Lozano et al. (2017)

16

Kuster et al. (2020)

35

Vinasco-Alvarez et al. (2020)

17

Lee et al. (2015)

36

Wagner et al. (2020)

18

Lemaignan et al. (2006)

37

Wei et al. (2020)

19

Massaro et al. (2020)

4.3.3 Applications of Ontologies in Urban Environments In order to obtain a general overview on whether and how ontologies are used to enhance research, design, planning, construction, operation, management, maintenance, restoration, governance, and the use of urban environments, we conducted a systematic review of 60 recent literature references, including journal articles, books, and reports from 2010 to 2021. Only 40 of them were found to be suitable for analysis. Those that were eliminated were either compartmented reviews of ontologies, which guided our search for papers in those areas, or were found to suffer in terms of content, clarity, and methodology. This analysis is not about a survey to quantify the volume of content for comparative analysis to extract long-term trends. It is rather about revealing context-specific tendencies in the approaches, which are currently shaping urban ontologies. This study also served to establish key concepts and terms that can be used to initiate a scientometric analysis of a complex, heterogeneous, and rapidly evolving subject like urban environments and ontologies. This is useful because selection based on the frequency and co-occurrence of keywords in articles or keywords sections of articles is not always the best indicator for capturing what is important from the perspective of this review. Keywords also cannot always

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be adequately established based on a list of literature determined by citation and ranking metrics, especially when the field is not yet mature and where the survey is based on most recent publications. Based on this filtered literature pool composed by a wide range of contributions, the main objective was to identify the key drivers behind current progress and contexts where ontology development is widespread and advancing most rapidly, showing the state-of-the-art and good practice in the field. This overview serves to assess the most dominant vectors of development, arising as a result of the different needs, motivations, aspirations, and emphasis, and the gaps, which can be bridged by ontologies. This study spans diverse topics ranging from building to landscape and urban scale, built to natural environment, indoor to outdoor environment, intelligent human to machine agents, smart city to green city, BIM to GIS, CAD to CAE, smart manufacturing to robotic construction, building automation to ambient intelligence. They could easily be located in one or at the intersection between several of these focal areas. In order to make such a diverse set comparable, we have devised a matrix (Table 4.2), by which to analyse the focus of each disciplinary domain and specialization, and how and for which purpose they have exploited knowledge management, engineering, and reasoning. While this review is neither exhaustive nor complete it reveals nevertheless patterns in the use of ontologies that indicate that the ability to transform data and information into actionable knowledge remains considerably low in comparison to the steep technological progress and data creation. Ontologies developed in the contexts that promote digitization and automation (i.e., BIM, Smart City) do not currently match thentologyes needed to promote greener and ecological urbanization, and are therefore not yet recognized as a key enabler in the contexts that promote environment, sustainability, and resilience (i.e., sustainable development, Green City). This gap will be further discussed and exemplified.

4.3.3.1

Structure of Analysis

The reviewed literature list is organized in Table 4.2, which correlates (1) the type of capability derived from the ontology and (2) the domain in which this capability is exploited. Ontology-enabled capabilities deployed in these works are found to operate at different levels, which include decision support, data integration, knowledge discovery, knowledge representation, and knowledge retrieval. These are then aligned with the main purpose that each ontology is specifically designed for (e.g., what kind of decision making does the ontology support?) and correlated with their disciplinary domains (e.g., in which domain the ontology is used to support decision making?): AEC(OO) and Urban Planning, which are then further divided into subfields. The literature data points are further clustered in terms of their context of development, which corresponds with the application domains. The latter include smart city/building, green city/building, ambient intelligence, GIS, BIM, CAD, CAE, CAM, BAS, smart manufacturing, and robotics. In cases where the work links several

Building classification

Knowledge discovery

Knowledge Building representation geometry

Urban KPI

Semantic interoperability

[31BIM] [36BC]

[2GIS] [4GIS]

[21GIS] [2GIS]

[8BC]

[33BB]

Algorithmic process

[27AMB]

[30GC]

[8BC]

Urban Heat Island mitigation

Garden management

Simulation

[24GIS]

[25SC]

[32SC]

[12GC]

Building Building Building Building City Civil Urban Urban construction renovation automation sustainability modelling construction sustainability green/blue infrastructure

Building modelling

Data integration

Decision support

Urban design, planning and management

AEC(OO)

(continued)

[11GC]

[37GC]

[12GC] [34GC] [26GC]

[3GC] [11GC] [37GC]

Table 4.2 Summary table. The cited work is further categorized into: Green city/building (GC), Ambient intelligence (AMB), GIS, BIM, BIM-CAD (BC), Smart manufacturing (SM), Smart city/building (SC), GIS-BIM (GB), BIM-CAM (BC), GIS-BIM-Robotics (GBR), CAD-CAE (CC), BIM-BAS building automation system (BB)

60 C. Pruski and D. S. Hensel

Knowledge retrieval

Simulation

Building surroundings

Semantic interoperability

Urban KPI

Manufacturing process

Simulation

[5CC]

[18CC]

[17BC]

[14GBR]

[9 GB]

[23BIM]

[16SC] [28SC] [10 GB] [18SM]

[13 GB]

[2SC] [6SC] [10 GB]

[2GIS] [35GIS]

[19GC]

[16SC] [28SC][7SC]

Building Building Building Building City Civil Urban Urban construction renovation automation sustainability modelling construction sustainability green/blue infrastructure

Building modelling

Robot navigation

Urban design, planning and management

AEC(OO)

Table 4.2 (continued)

(continued)

[11GC]

[37GC]

[11 GB] [37 GB] [22 GB] [29 GB] [15 GB]

4 The Role of Information Modelling and Computational Ontologies to Support … 61

[1BC] [20BIM]

Building Building Building Building City Civil Urban Urban construction renovation automation sustainability modelling construction sustainability green/blue infrastructure

Building modelling

Manufacturing process

Urban design, planning and management

AEC(OO)

Table 4.2 (continued)

62 C. Pruski and D. S. Hensel

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disciplinary domains and scales, they populate more than one cell in the matrix. Wherever they focus on the integration of tools, this connection or extension is specified (i.e., BIM-GIS).

4.3.3.2

Patterns of Ontology Use in Urban Environments

Our systematic review shows that ontologies are mostly recognized for their properties to represent knowledge, supporting the representation of building’s geometry and surrounding environment, on-site construction assembly and off-site prefabrication processes. Ontologies are also useful for capturing urban indicators (KPIs) and simulation intent, and facilitate semantic interoperability (Rasmussen et al. 2021; Boussuge et al. 2019; Lemaignan et al. 2006; Mirarchi et al. 2020; Kuster et al. 2020; Pileggi and Hunter 2017; El-Diraby and Osman 2011; Massaro et al. 2020; Komninos et al. 2016). Semantic models represented by domain ontologies, which rely on the standardization of terminology and shared conceptualization is common and growing in great numbers in most of the areas that have been mapped in the domains of architecture, engineering, construction, landscape, to planning, policy, and renovation. In this context, ontologies are also seen as a tool for expanding BIM’s capacity to integrate other disciplines involved in construction projects to improve cooperation and communication, exchange and management of, and describing building data and beyond in an integrated and standard data framework. From this perspective there are growing efforts to exploit ontologies for integrating BIM with GIS, CAD, CAE, CAM, BAS, and robotics to enhance AEC, facility management, urban planning, and sustainable development. There are comparatively few examples where information is modelled to support targeted retrieval of integrated building and urban data and semantics. This have been used, for example, in solar energy simulations at the scale of a neighbourhood (Huang et al. 2020), to empower smart blue infrastructure solutions (Howell et al. 2017), and to provide manufacturing and assembly instructions determined according to available resources, which requires extending BIM product specification to include information about CAM (An et al. 2019), as well as to provide specifications for quality control and inspection to support construction processes (Martinez et al. 2019). So far, ontology applications in the AEC disciplines seem limited to knowledge representation and information retrieval. In the contexts of Smart City, GIS, urban design, ambient intelligence, building automation, Green City/Building and in green infrastructure, ontologies have been exploited for data integration, knowledge discovery, and decision support. In Green City/Building context, decision support has been used, for instance, for urban management (Wei et al. 2020), garden and water management and agriculture (Hu et al. 2020; VergaraLozano et al. 2017; Myers et al. 2017; Beck et al. 2010; Howell et al. 2017), and urban design for context-specific implementation of urban heat island mitigation strategies (Qi et al. 2020). The current balance struck between ontologies for management and interoperability relies on semantics and informatics approaches where they are beginning to change the urban environments at a fundamental level through enhancing and accelerating decision making through facilitating the integration of heterogeneous and complex urban data.

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4.3.4 Towards Knowledge Graphs for Urban Environments Ontologies have the ability to make the semantics of a domain explicit through the formal definition of concepts and the relationships between them. Ontologies place emphasis on the conceptualization of the domain and neglect the instances of the represented concepts. To overcome this limitations, Knowledge Graphs have been introduced (Singhal 2012), which combine characteristics of databases, graph theory and Knowledge base. Entity description in knowledge graphs contribute to one another, forming a network, where each entity represents part of the description of the entities, related to it, and provides context for their interpretation.9 In urban environment, knowledge graphs have recently been introduced for various purposes. First, knowledge graphs h.ve been used mainly for their ability to integrate data and knowledge. This has been done in (Huang et al. 2020) where the authors use knowledge graphs to reconcile BIM and CityGML models. As these two models use their respective distinct ontologies, the authors had to transform the data into knowledge graphs based on their respective ontologies (CityGML and ifcOWL/BOT ontologies), and link the building instance through a relation in the SKOS vocabulary, i.e. skos:exactMatch. In this work, knowledge graphs prove to be particularly useful to retrieve information about buildings, i.e., their geometry for use in an energy consumption simulation scenario. The work of Bassier et al. consists of extracting building component information from online (distributed) building geometry sources of information and transform it into a knowledge graph to be published as linked data under the RDF standard (Bassier et al. 2020). This is done using Machine Learning techniques applied on any building mesh. The resulting data is then annotated with semantic information coming from Linked Building Data ontologies such as BOT, PRODUCT and OMG/FOG/GOM and transformed into RDF triples. Knowledge graphs have also been used for decision support. This is the case of Santos et al. who have addressed the problem of computing indicators in order to build and display dashboards for the purpose of comparing cities (Santos et al. 2017). This is done based on OWL ontologies that describe the considered infrastructure (and associated metadata) as well as the indicators themselves. This allows the use of a reasoner to automatically infer the indicators defined in the ontology which, in turn, enables the generation of knowledge to explain the found indicators before being displayed in dashboards and used for advanced data analysis. The proposed approach has been evaluated in the context of urban mobility.

9

www.ontotext.com/knowledgehub/fundamentals/what-is-a-knowledge-graph/.

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4.4 Challenges for Better Exploitation of Ontologies in Urban Environments Taking into account the analysis of the literature described in Sect. 4.3.3, several challenges must be met to unleash the full capability of ontologies for a better urban environment, and meet the expectations of Industry 4.0, and Smart and Green City/Building, and sustainable development. Ontologies have to be FAIR (Findable, Accessible, Interoperable, and Reusable). Recently, the scientific data management community has published a set of principles to make digital assets FAIR in order to maximize their impact. However, in most of the reviewed papers, the ontology they describe are not made publicly available, thereby limiting their benefit for software applications for urban environment. FAIR principles therefore need to be accepted by stakeholders and appropriate tools have to be designed to make these ontologies FAIR. Furthermore, reasoning capabilities of ontologies remain underexploited. As decision-support systems are becoming critical in urban environment, their logic foundation and their support for reasoning have to be considered to aid stakeholders in making good decisions. This requires gathering and exploitation of the appropriate type of data whose semantics (including datatypes and associated values) has to be made explicit in order to facilitate their classification according to the tasks or decisions to be considered. Ontologies have to be maintained over time. In addition to the FAIR principles, ontologies have to smoothly evolve according to the continuous modifications in the knowledge of their associated domain. Some domains like health are more advanced (Da Silveira et al. 2015). Urban environment can therefore benefit from examining work in other disciplines, but obviously need to adapt them to specified needs. This involves developing systematic frameworks for progressive integration of new information with historically-derived information to accelerate research, maximize the use of big data, and enhance knowledge intensive, evidence-based and context-specific decision making. Data and knowledge have to be better combined. As observed in our analysis, urban environment is a domain where vast amounts of data is available, but remains largely unused if not turned into knowledge. Ontologies and more specifically knowledge graphs have shown great capability to make the semantics of the data explicit by associating it with concepts that make data useful for decision support systems. However, the size of the underlying graph structures is causing scalability issues. Methods and tools for representing and consuming data and knowledge at appropriate scale are therefore urgently needed.

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4.5 Conclusion Issues of climate change, urbanization, and environmental degradation with serious consequences for biodiversity and human well-being is the foundation of the European Green Deal urgency, urging for new approaches to tackle these problems. Major improvements in the acquisition of data and sensing of the environment, facilitated the development of advanced methods for modelling, designing, building, and managing urban areas. In this paper, we reviewed existing works dealing with the application of computational ontologies to solve current problems of the urban environment domain. Ontologies are already having a great impact on the ways we share, communicate, integrate, manage, and capture urban data. However, the critical role that ontologies can play in facilitating the translation of data into knowledge, and generation and application of knowledge for solving complex urban sustainability and ecological urbanization challenges (Muñoz-Erickson et al. 2017) remains overlooked. Based on our analysis, we have identified current and future challenges that need to be met by the research community to promote and democratize the use of ontologies in this field to make the most out of the ever-growing amount of data.

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Psyllidis A, Bozzon A, Bocconi S, Bolivar CT (2015) A platform for urban analytics and semantic data integration in city planning. In: International conference on computer-aided architectural design futures. Springer, pp 21–36 Qi J, Ding L, Lim S (2020) Ontology-based knowledge representation of urban heat island mitigation strategies. Sustain Cities Soc 52(101):875 Rasmussen MH, Lefrançois M, Schneider GF, Pauwels P (2021) BOT: the building topology ontology of the W3C linked building data group. Semantic Web 12(1):143–161 Runting RK, Phinn S, Xie Z, Venter O, Watson JE (2020) Opportunities for big data in conservation and sustainability. Nat Commun 11(1):1–4 Santos H, Dantas V, Furtado V, Pinheiro P, McGuinness DL (2017) From data to city indicators: a knowledge graph for supporting automatic generation of dashboards. In: European semantic web conference. Springer, pp 94–108 Seto KC, Sánchez-Rodríguez R, Fragkias M (2010) The new geography of contemporary urbanization and the environment. Annu Rev Environ Resour 35:167–194 Singhal A (2012) Introducing the knowledge graph: things, not strings. Official Google Blog 5:16 Solecki W, Seto KC, Marcotullio PJ (2013) It’s time for an urbanization science. Environ Sci Policy Sustain Develop 55(1):12–17 Suárez-Figueroa MC, Gómez-Pérez A, Fernandez-Lopez M (2015) The neon methodology framework: a scenario-based methodology for ontology development. Appl Ontol 10(2):107–145 Teller J, Lee JR, Roussey C (2007) Ontologies for urban development, vol 61. Springer Terkaj W, Schneider GF, Pauwels P (2017) Reusing domain ontologies in linked building data: the case of building automation and control. In: 8th International workshop on formal ontologies meet industry, vol 2050 Vergara-Lozano V, Medina-Moreira J, Rochina C, Garzón-Goya M, Sinche-Guzmán A, BucaramLeverone M (2017) An ontology-based decision support system for the management of home gardens. In: International conference on technologies and innovation. Springer, pp 47–59 Vinasco-Alvarez D, Samuel J, Servigne S, Gesquière G (2020) From citygml to owl. PhD thesis, LIRIS UMR 5205 Wagner A, Bonduel M, Pauwels P, Rüppel U (2020) Representing construction related geometry in a semantic web context: a review of approaches. Autom Constr 115(103):130 Walliss J, Rahmann H (2016) Landscape architecture and digital technologies: re-conceptualising design and making. Routledge Wei L, Du H, Qa M, Al Ammari K, Magee DR, Clarke B, Dimitrova V, Gunn D, Entwisle D, Reeves H et al (2020) A decision support system for urban infrastructure inter-asset management employing domain ontologies and qualitative uncertainty-based reasoning. Expert Syst Appl 158(113):461 Zhong B, Wu H, Li H, Sepasgozar S, Luo H, He L (2019) A scientometric analysis and critical review of construction related ontology research. Autom Constr 101:17–31

Cédric Pruski is senior researcher at the Luxembourg Institute of Science and Technology. His research interests are Artificial Intelligence and knowledge representation and reasoning. He received an “Habilitation à Diriger des Recherches” from university Paris-Saclay and a PhD in computer science from both university of Paris-Sud and university of Luxembourg. He is the coauthor of more than 70 scientific articles and co-supervised 4 doctoral candidates. He successfully coordinated national and international research projects that have generated many publications in major conferences and peer-reviewed journals of the field Artificial In-telligence and knowledge representation as well as 2 PhD defenses. Defne Sunguroglu Hensel (AA Dipl RIBA II AA EmTech Ph.D.) is an architect, partner in the practice OCEAN Architecture|Environment and OCEAN net, and founding and steering member and coordinating manager of LamoLab Research Centre. She is Associate Professor of urban ecology and landscape architecture at the Architecture Internationalization Demonstration School,

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Southeast University of Nanjing, China. Post-doctoral researcher at Technical University Munich in the context of the H2020 FET Open project ECOLOPES as work-package leader WP 4—Data Acquisition and Information Modelling. Her work focuses on topics spanning ecological architecture and urbanization, green construction, data-driven design, knowledge-based decision support systems, urban agriculture. Previously university lecturer and post-doctoral researcher at Vienna University of Technology in the Special Research Area Advanced Computational Design in the context of the Centre for Geometry and Computational Design.

Chapter 5

Urban Adaptation—Insights from Information Physics and Complex System Dynamics Rui A. P. Perdigão

Abstract This chapter addresses and reframes urban adaptation through frontier coevolutionary information physics and complex system dynamic approaches, with an interdisciplinary perspective seamlessly articulating frontier natural, social and technical sciences into a coherent manner through a novel mathematical lingua franca articulating manifold disciplines. In doing so, insights on urban adaptation depart from rethinking to reframing adaptation into full-fledged system dynamic coevolution, thereby reshaping concepts, strengthening procedures, empowering choices to turn insights into actions, to bestow into urban adaptation a novel mathematically robust interdisciplinary framework able to brave the emerging challenges facing the urban socio-environmental dynamics along with its interdependencies across manifold scales and domains ultimately linking to the overall coevolutionary Earth System Dynamics. In methodological terms, a novel robust mathematical edifice is thus provided to both data based and process based system dynamic understanding, design, analytics and decision support. In this sense, the methodological foundations are set to empower a robust synergistic coalition among cutting-edge information physics, nonlinear data analytics and model design, along with expert knowledge of those on the field, leveraging and complementing their voices, insights and operations with innovative robust mathematical, physical, numerical, computational, novel tools and data analytics to drive the design, decision and operation processes in multissectorial urban adaptation terms. Keywords Complexity science · Information physics · Urban systems · Adaptation · Coevolution · Earth system dynamics · Interdisciplinary data analytics and model design

R. A. P. Perdigão (B) Meteoceanics Institute for Complex System Science, Vienna, Austria e-mail: [email protected] Universidade de Lisboa, Lisbon, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_5

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5.1 Introduction Adaptation is intrinsic to urban system dynamics, for it entails an active interplay and management of spaces and functions among humans and nature through technologies and infrastructures ranging from environmentally centric in green cities to technologically centric in industrial cities. There is a vast body of scientific literature on urban adaptation and the processes relative to which adaptation is devised, broadly compiled in such key references as the European Environment Agency reports on Urban Adaptation with a comprehensive review on pan-European challenges and opportunities in this regard (EEA 2016, 2020), along with an overview of the diversity of specific topical and case studies and references therein. Other relevant references include the IPCC (2014) report on climate change impacts, adaptation and vulnerability, namely global and sectoral aspects, strongly supported by a comprehensive underlying dynamic basis of climate change mechanisms in IPCC (2013). Other key references include Reckien et al. (2014, 2015, 2017, 2018), Feyen et al. (2020), Geneletti and Zardo (2016), Heidrich et al. (2016) on various facets of climate change impacts on the urban environment and adaptation perspectives, along with studies focusing from local (e.g. Aguiar et al. 2018) to global (e.g. Magnan and Ribera 2016) adaptation. A range of sectorial studies are also available including on nature-based solutions in the built environment along with the associated cost–benefit (e.g. Perini and Rosasco 2013), health-focused urban design solutions (Mueller et al. 2020) and challenges such as those associated with urban heat exposure (Milner et al. 2017), climate service solutions to tackle health impacts (Hunt et al. 2017) and landslide hazards (Mateos 2020), and participatory action from citizens and governing structures (Mees 2017; Mees et al. 2019), just to name a few. Notwithstanding the clear merits and insights of adaptation studies and strategies derived from them, in its essence adaptation deals with strategic and operational responses to system perturbations, rather than in rethinking the system per se. That is, classical adaption acts upon system processes but not on its fundamental dynamic architecture i.e. on variables rather than interactions, on observable consequences rather than underlying mechanisms.

5.2 Rethinking Adaptation to Leverage Its Effectiveness Traditionally adaptation reports and guidelines, from local to broader, are highly insightful products of a broad co-constructive review, reflection and analysis effort among a wide range of stakeholders ranging from academic, research, industry, service, decision-making all the way to end-user citizen communities. However, such approaches lack the fundamental mathematical framework needed to leverage a robust, efficient and scalable transition from heuristic reflections to effective workflows, from insightful words to concrete actions.

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That fundamental caveat has often resulted in valuable reports not being given the much necessary follow-up in downright implementation terms, being lauded but unceremoniously archived instead of being truly honoured with sound socioenvironmental engineering solutions to get things done. Without hard formal mathematical scientific formalisms underneath, even the most sophisticated adaptation concepts and insights can be dismissed for lack of clarity and technical rigour, due to a primacy of conjecture and superficial analysis over robustly sound scientific practice. Many such concepts find their ways to well-intentioned albeit poorly informed political activism rather than to a most crucial scientifically robust action to save our planet, from local to global. While adaptation involves human aspects including social values and free will that are elusive to traditional engineering sciences, the treatment of such crucial aspects to the detriment of the mathematical, physical and engineering aspects leads to inefficient or even downright impracticable solutions such as those ignoring the laws of physics, which govern everything including the energy and mass fluxes and interactions between humans and nature. The key to overcome these limitations resides in allying frontier natural, social and technical sciences ranging from cutting-edge geophysical, geo-ecological and socioecological sciences to emerging pathways in information sciences and technologies, through an overarching interdisciplinary framework—not limited to the heuristic rhetoric so common place in well-intentioned international projects and other initiatives—but also having tangible formulations derived and in place that actually enable adaptation agents to “get the job done” i.e. to leverage multi-sectorial credibility of adaptation initiatives, and further leverage the effective action towards a successful outcome. After much having been written about adaptation and countless local plans being made in municipalities across various countries, it is high time to focus on getting the mathematics right, across the entire value chain ranging from data to models, from conceptual strategies to operational workflows. As a fundamental effort towards meeting that goal, to leverage theory and practice with a rigorous universal language, the present contribution brings out emerging interdisciplinary pathways in mathematical and information physics, which leverages natural, social and technical sciences, and bridges data-based and process-based knowledge acquisition, production and processing. For that purpose, we introduce a complex system dynamic approach to urban adaptation challenges with an interdisciplinary perspective outlining how robust mathematical, physical, numerical, computational, novel tools and data analytics can drive the design process in architectural, environmental, social, physical, geophysical, biophysical and climate dynamic terms. This contribution thus focuses on new and emerging interdisciplinary analytics, model design and decision support frameworks grounded in mathematical physics, information physics, artificial intelligence and evolutionary cognition in a unified methodologically rigorous manner, with multi-scale multi-domain applications at the interface between frontier natural, social and technical sciences.

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5.3 From Adaptation to Coevolution The traditional concepts of Adaptation are essentially focused on handling symptoms of an adverse fate such as over the projected consequences of a changing climate across the domains and sectors in need to adapt. At the core resides the assumption that an adverse situation is or will be in place that requires coping (e.g. damage control) and dampening symptoms rather than actually daring to act on the system dynamic architecture per se. As a partial improvement over passively dealing with consequences, mitigation deals with trying to act on the underlying causes for the change triggering the adverse consequences. However, even then the system architecture causally connecting cause and effect remains invariant in such approaches, since they act on the variables, being they impact quantities in adaptation frameworks, or driving factors in mitigation frameworks. Moving beyond action solely on system variables be they drivers or consequences is crucial to actually produce optimal strategies for system functioning across scales and domains. That entails acting on the very structure of system dynamic interactions, on its functional structural architecture, and doing so beyond statistical, topologic and geometric concerns of society and the environment in a fundamentally information physical manner that brings physical consistency to information metrics and artificial intelligence (AI), and information capabilities (e.g. AI) to process and expert based model design frameworks. In this sense, information physics and information physical artificial intelligence provide promising emerging avenues for a synergistic alliance between data-based and process-based adaptation, at its theoretical core and in application to urban challenges.

5.4 Reframing Urban Worlds as Complex Coevolutionary Systems In the present chapter, we approach urban worlds as complex coevolutionary systems, fundamentally dynamically adaptive and deeply connected in their essence, entailing a coevolutionary structural–functional interplay among society, nature and the built environment. In its essence, complexity does not arise from a high number of involving processes in the system, but rather on the nonlinear entanglement structure of their interactions. This is fundamentally what leads to the emergence of collective features in the system dynamics that cannot be attributed to any individual or linearly combined parts. Unlike the structural invariance of classical dynamical systems governed by a set of rigid deterministic laws even with nonlinear couplings, in urban system dynamics such invariance is no longer so. In fact, the coevolutionary nature of cities brings out an adaptive feedback structure between the interference among bottom-up individual

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activities that reshape the overall system, and the top-down activities cascading from the collective structure (such as social regulations, transportation networks) all the way down to conditioning individual behaviors, (e.g. from how we use space to how we move across it). The coevolutionary reshaping of urban system dynamics is further enhanced by key transformations in the social and environmental fabric within and beyond the city boundaries. In this sense, a changing climate is undoubtedly a major driving mechanism to urban change, but by no means the only one. Social transformations stemming from political and economic transitions, ecosystem transformations from natural evolutionary mechanisms and socio-ecological interplay, are among the nonclimatic factors to pay close attention to in any systemic analysis, modelling and decision support approach to urban system dynamics. Typically, the complexity of these interactions is intuitively understood and discussed rather heuristically, often under the assumption that this problem lies beyond the grasp of the exact sciences. However, in reality such sciences provide a rigorous, interdisciplinary universal language that reaches far beyond traditional technical fields to embrace social principles and values without losing hold of an unbiased, rigorous language that seamlessly connects natural, social and technical sciences: the mathematical physics of information and complexity. As urban system dynamics undergo coevolutionary transformation, humans need to play their role as active agents in contributing to reshaping the evolutionary system architecture in articulation with social and technical concerns, taking a dynamically adaptive approach towards social, natural and built environments. For that to be fruitful not only to humans but fundamentally sustainable and well-balanced for the overall urban system, it is important to embrace a multiscale multidomain system-of-systems approach to investigate, analyse, model, design and implement novel solutions tailored to such coevolutionary reality. One that is far more than simple univariate climate change adaptation, but one that involves new and emerging paradigms for interdisciplinary systemic adaptation—including urban architecture and planning spanning built, natural and hybrid environments—in a coherent synergistic manner targeting the multivariate optimisation of the urban system per se, whilst keeping close attention to the fine articulation among individual processes and interactions that would easily fall below the radar in traditionally integration-byaggregation approaches. In a true complex system dynamics approach, the focus is not on a collective envelope that alienates the individual, but rather on a multiscale articulation linking the individual with the society, the home with the neighbourhood, the local with the global. And then articulating the urban system dynamics with the broader regional and larger scales, ultimately linking to the overall Earth system dynamics.

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5.5 Linking Local with Global The Earth System is a complex dynamical system undergoing structural functional coevolution across multiple spatiotemporal scales and domains. The diversity of players in Earth System Dynamics ranges from geophysical mechanisms to ecosystem functions and socio-economic processes. A changing climate is in itself a coevolutionary process emerging from the synergistic dynamics of these sources, and as such an emerging signature of complexity that is not reducible to the compound action of these intervening parts. One key feature in complex dynamical systems such as a changing climate entails the journey energy and momentum across scales, and the pervasive and deep process connectivity across an intricate nonlinear dynamic web of interactions. Global processes cascade down all the way to having local impacts, while local action has also a connectivity route towards contributing to global impacts— provided such connectivity is robust and cooperatively nurtured across a multilocal network of consistent constructive action. Geographically, urban environments are inherently local to regional with respect to the Earth system as a whole. However, the aforementioned system connectivity makes them crucial focal hotspots for both: (a) climate change impacts such as in terms of natural, health and infrastructural hazards arising from increasing temperatures, changing weather circulation regimes and consequent extreme meteorological events ranging from urban heat islands to hydro-meteorological criticalities; and (b) climate action in view of helping steer the overall system away from new climate change-induced environmental regimes adverse to our society and the environmental resources upon which it relies for its functional and structural survival. As far as climate change is concerned, the famous entertainment adage “what happens in Vegas stays in Vegas” no longer holds. Cities are open systems, articulated in the broader multiscale multi-sectorial Earth system dynamics, and as such interdependent on its processes and interactions. These need to be properly and carefully understood and taken into account in any dynamic analysis, modelling and decision support framework linking urban systems with climate and the overall Earth system dynamics, including but not limited to the investigation of urban impacts of climate change, and urban adaptation to such changes in view of a more resilient urban system, hoping and contributing towards the best (correction of adverse trajectories in climate change), but preparing for the rest (incorporating uncertainties, omissions, misconceptions and outright failures in designing and implementing measures, from local to global, in countering the adverse pathways in earth system dynamics in a changing climate).

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5.6 Urban Systems as Coherent Adaptive Dissipative Structures Civilizational constructs such as urban environments are intrinsically adaptive, reshaping their structure and function on a systematic optimization scheme, fundamentally grounded on core priorities upon which the civilizational backbone stand— with fundamental principles such as stability through self-preservation enforcing safe shelter, expedite access to resources and a coherent self-sustainable operation, and a progressive drive propelling innovation, production and prosperity in a forward targeted evolutionary drive. The built environments would thus ideally provide robust conditions for primary stability, productivity, and expedite conveyance of resources, aiming to set the urban engine running as a self-reliant organism. However, in the real world there are no isolated self-sustainable systems, for which reason the idealized Hamiltonian conservancy of energy and momentum is a distant mirage in the far from equilibrium dynamics of the earth system at all spatiotemporal scales. The Earth is itself the epitome of far from equilibrium optimality, its environmental dynamics essentially being highly efficient dissipative structures, ranging from large-scale ocean-atmospheric circulation systems to the very fabric of life and society. Dissipative systems prey on resources to boost their emerging complexity. In dynamical systems terms, a civilisation can be regarded essentially as a highly articulate dissipative system, the coherence and robustness of which embodies a dissipative structure not formally different from that of a coherent convective cell of turbulence. As such, classical optimality principles such as energy (resource) conservation and structural invariance would be optimal only in an elusive equilibrium that can never effectively happen in an ever-changing world. In a dissipative world, such as that of civilizational systems in general and urban ones in particular, the optimality needs to be grounded on the energetics of nonlinearly entangled coevolutionary systems, where far from equilibrium statistical physics and coevolutionary system dynamics play a crucial emerging role (Perdigão 2017, 2018a, b). In practice, by framing the city as an inherently dissipative structure, consumption and production, destruction and creation then become two sides of the same coin, allies in emerging and consolidating coherent system dynamic features that embody the city infrastructures, services and overall urban structural–functional life, not only for collective fulfilment but also for that of individuals and families as core functional units in the system dynamics. In order to achieve that, consumption needs to be coupled with production, i.e. have a systemic meaningful role, which in many cases is far from trivial such as in the case of the arts and entertainment. Still, even then there is a nonlinear conveyor between such processes and the direct constructive and productive effort, for the latter benefits from the impacts of the former onto the functioning of a core resource in cities, the human resources.

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As with every system, an overarching information physical thermodynamic optimality is key to framing the most efficient, sustainable and healthy structural–functional coevolution leading the system to fulfilling its mission. Idealised conservative equilibrium systems are framed on a classical optimality energy minimum and least action. In real-world systems, those are inherently dissipative, consuming energy and producing entropy, optimal system functioning and evolution entails robust, coherent systemic patterns as dissipative structures, the optimality of which fundamentally arise from the interplay between maximum entropy production (maximum rate of process mixing, of interaction, of innovation) and maximum dissipation rate (eroding tensions as fast and efficiently as possible). In architectural terms that entails optimising fluxes of what needs to efficiently flow (information, products, services) through efficient communication or transport infrastructures, whilst optimising recirculating processes and pathways to most efficiently convert consumption into production, destruction into creation. This frames widely accepted good practices such as recycling into an information physical perspective, benefitting from its interdisciplinary portability and universal applicability.

5.7 Empowering Urban Adaptation with Information Physics The emerging pathways in Information Physics (Perdigão et al. 2020) provide a formal interdisciplinary methodological suite and underlying theory to a coherent formal analysis, modelling and design of dynamical systems with evolutionary complexity, either from data-based analysis and inverse modelling, process-based forward modelling and combined perspectives. In this sense, they go beyond computer science in providing physical consistency to analytics and model design, whilst bestowing analytical and computational power and efficiency to tackle complex technical problems beyond the scope of information theory (Azmi et al. 2021). The technological arm of information physics in this regard brings us to the recently developed Information Physical Artificial Intelligence (Perdigão 2020a) as the fundamental approach that goes beyond the traditional dichotomy between naïve artificial intelligence and process expert knowledge. Rather than artificial intelligence, the emerging pathways in information physics empower a natural environmental intelligence from how nature works including the crucial aspects of causal attribution (Perdigão 2020c; Hall and Perdigão 2021), for even society lies bare its dependence on thermodynamics, for nothing can produce or consume more energy that it has at its disposal. From the theoretical perspective, the aforementioned emerging pathways are firmly grounded on the synergistic dynamic theory of complex coevolutionary systems and associated information physical machine learning methodology of

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Dynamic Source Analysis (Perdigão 2018a, b), further explored in coevolutionary climate system problems (Perdigão et al. 2019). The mathematical physics edifice further resides in the Perdigão (2017) treatise on the mathematical physics of non-ergodic coevolutionary complexity, bridging nonlinear statistical physics, analytical mechanics, functional analysis, theoretical thermodynamics, information theory and differential geometry in a novel unified framework. Consolidating these seminal advances in pedagogic contributions for academics and practitioners alike, come the empowerment and capacitation programs on Complex System Dynamics (Perdigão 2019) and Interdisciplinary Data Analytics and Model Design (Perdigão 2020b). The aforementioned advances and programs further empower a formal system dynamics approach to adaptation, redefined in Perdigão and Schwemmlein (2020) as “seeking new optimality in socio-environmental systems in the face of a reshaped objective function” such as from climate change, and extending classical approaches to empower “an algorithmic treatment in terms of objective optimization for concrete decision support”, bringing added value to the adaptation backbone of “inherent adjustment of the modus operandi of our societies, ranging from the everyday dynamics to the overall paradigms of development”. Ultimately, this links and empowers adaptation with the Perdigão–Hall– Schwemmlein (PHS) Nexus, “Polyadic Dynamic Nexus among Complex SocioEnvironmental Systems: from Earth System Dynamics to Sustainable Development”, Perdigão et al. (2020). The journey is still at its infancy but the mathematical physics are there to link expertise and techniques across frontier social, natural and technical sciences such as in the PHS complex system dynamical triad under the overarching framework of information physics.

5.8 Information Physics: From Physics Made of Information to Information Made of Physics An information-centric way of looking at Information Physics would be to see the physical world as a conceptual construct purely made of Information. However, deep within, Information itself is made of Physics, as it fundamentally entails a phenomenological basis. From electronic to photonic states underlying classical and quantum computing platforms, all the way to geophysical, ecosystem and astrophysical patterns and functions, information always entails the manifestation and characterisation of a set of discernible features. While the absolute amount of information depends on the reference basis and benchmark upon which it is being measured, crucially it is the relative information between observable states and anchoring reference states that serve as actual quantifiers—quite similarly as happens with Entropy, the fundamental physical basis to the concept of information. From a natural standpoint, physical Entropy

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entails the information content of a system, while statistical entropy entails the statistical information that needs to be obtained to characterise the system departing from the baseline of no a-priori knowledge. In essence, the emerging pathways of information physics position it as “the scientific quest for the fundamental nature of information” (Perdigão 2017; Perdigão and Hall 2020). This emerging field finds crucial relevance in tackling complexity in a way that for the first time overcomes the caveats of disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms crucial for analysis, modelling and decision support. The novel Non-Ergodic theory of Information Physics (Perdigão 2017, 2018a, b, 2020a) connects the dots in a synergistic coevolutionary manner, bestowing deeper process and system understanding, causality evaluation and predictive power across scales. Table 5.1 below summarizes the added value of Non-Ergodic Information Physics relative to the state-of-the-art Information Theoretical paradigms and equivalent approaches used across system sciences, machine learning and artificial intelligence. The reader is referred to the Appendix of this chapter for a more technical, mathematical discussion of Information Physics and associated treatments on information metrics, linking statistics with system topology and underlying kinematic geometry. Table 5.1 Comparison outline between key features in traditional information theories, classical or quantum, and the added value brought by non-ergodic theory of nonlinear information physics. Adapted from Perdigão (2018a, b, 2020b) Traditional information theories (IT), classical or quantum

Non-ergodic theory of nonlinear information physics (Perdigão 2017, 2018a, b)

Build upon Boltzmann–Gibbs–Shannon/Von-Neumann (BGS) Entropies

Vastly extends IT to a broader universe of applicability beyond BGS assumptions

Capture aggregate statistical links, whilst ignoring the fine print of the dynamics

Captures not only aggregate but also the fine print of the dynamics within

Multivariate and inferential links among aggregates but not among microstates

Captures coevolution and causation among underlying microstates

Maximum Entropy (ME) with microstate independence, local equilibrium

ME and IT laws reshaped by microstate interplay, linking micro to macro scales

Valid only in local equilibrium (granular like a perfect gas)

Valid also in far-from-equilibrium structural–functional coevolutionary systems

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5.9 Take Home Message for Adaptation The key is thus neither replacing human insights by computer science or vice-versa, but rather bringing these novel and emerging methodologies and insights to humans, computers and their interactions in a coherent, mathematically sound and physically (process-based) consistent manner. In the end, from human insights to experimentation, computation and action, complex systems and their dynamics can be analyzed, articulated and worked on with a lingua franca across scientific disciplines and practices, ultimately leading to the fundamental nature of information residing in the emerging field of interdisciplinary physics herein. Urban adaptation places itself in a privileged position to benefit from such assets, thereby opening new opportunities for data-based, model-based and even heuristic participatory adaptation techniques and insights to be combined in a mathematically consistent, computationally workable and credible communicable manner, with neither prejudices against social sciences nor against mathematics and physics, but rather an overarching system dynamic alphabet, language and workflow to improve system understanding, predictability and robustly informed decision support to empower adaptation with the rigor and credibility of the hard sciences upon which the core of our civilization stands, from our infrastructure to our social and technological systems. These were not built upon vague rhetoric: they were built upon mathematics in all its forms and flavors. In operational terms, our novel tools and services provide a crucial predictive edge and added value to early warning systems of natural hazards and long-term policies for climatic action. From a methodological side, our recent developments on information physics (Perdigão 2020a) enable early detection and attribution of causal features underlying natural hazards, including non-recurrent or unprecedented signatures elusive to training-dependent technologies such as classical machine learning and artificial intelligence, along with the dynamic modelling and prediction of the overall system dynamics (Perdigão 2020c). From a technological side, the information physical sensing technologies of the Meteoceanics Constellation bring new opportunities for retrieval of early warning high-resolution fingerprints of hydrological, meteorological and other geophysical hazards, empowered by a new breed of Quantum Technologies in the Earth and Space Sciences (Perdigão 2021). Crucially though, even with the most advanced science and promising technology, sound and seamless scientific communication and knowledge transfer are always fundamental to leverage decision support and societal action by all those entrusted with protecting our society and the environment. As in every complex system, the overall holistic mission can only be achieved with a synergistic articulation and cooperation among the intervening parties. When it comes to Urban Adaptation, or broader Climate Action for that matter, the future is in the hands of all of us.

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Appendix Linking Statistics with Topology and Underlying Kinematic Geometry Traditional statistical approaches to data analysis and modelling inherently entail some form of aggregation, summarizing information around classes of equivalence (e.g. some recurrent feature shared among individuals, embodying a pattern) at the cost of discarding information about particular diverse features not recurrent enough to statistically populate a class of equivalence. Any statistic drawn over a system population will inherently entail information loss, while enabling focus on representing and quantifying key dominant features with simpler descriptors than would be feasible with an extensive individual characterization of each microphysical component. In dynamical system terms, the aggregate system dynamics traditionally represented in statistical metrics find an elegant mathematical equivalent in topological terms. This is the case with classical ergodic dynamical system theories, wherein one of the most important definitions for information resides in Topological Entropy (Adler, Konheim and McAndrew 1965; Dinaburg 1970; Bowen 1971). References “Adler et al. (1965), Dinaburg (1970), Bowen (1971), Ruelle (1978), Borges and Roditi (1998), Kaniadakis et al. (2005), Perdigão et al. (2016)” are given in the list but not cited in the text. Please cite them in text or delete them from the list. Actually, these references were cited in the text but were missing in the reference list. This shortcoming is now overcome with the inclusion of the corresponding entries in the reference list (except for one which was replaced by an existing entry). In accordance with the definitions from Dinaburg (1970) and Bowen (1971), the topological entropy htop (f ) of a dynamical system f quantifies the exponential growth rate of the maximum number of distinguishable orbits of length n, Sep(f , n, ε), and can be expressed in the following form (Ulcigrai 2007): h top ( f ) = h top ( f, ε) = lim lim sup sup ε→0 n→∞

log[Sep( f, n, ε)] n

(5.1)

where ε is the arbitrary precision radius relative to which the aforementioned orbits are distinguishable. This definition of entropy is intrinsically related to the orbital density in phase space from the classical analytical mechanics take on thermodynamics. The orbits are assumed to be distinguishable and independent, even if in reality they are not necessarily so. In practice, this form of entropy can be seen as a measure of the overall exponential complexity of the orbit structure of f (Hasselblatt and Pesin 2008). Topological entropy is equivalently expressed as the supremum of the well-known Kolmogorov–Sinai metric entropies hm for all measures m:

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h( f ) = sup{h μ ( f )}

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(5.2)

where hm obeys the Margulis–Ruelle inequality (Ruelle 1978): hμ( f ) ≤

¨ 

(x)dμ(x)

(5.3)

ki+ (x)χi+ (x)

(5.4)

M

where 

(x) ≡

 χi+ (x)>0

is the sum of the positive Lyapunov exponents (which quantify the rate of exponential divergence of nearby orbits in phase space, thereby being an indicator of the presence of deterministic chaos). Detailed pedagogic reviews on Lyapunov exponents and chaos can be found in e.g. Ott (2002) and Nicolis and Nicolis (2007). When the supremum is an attainable maximum, the topological entropy is equivalent to the metric entropy through the Pesin (1977, 1997) equality: hμ( f ) =

¨ 

(x)dμ(x)

(5.5)

M

This means that ultimately the topological entropy comes down to the sum of the positive Lyapunov exponents of the dynamical system f , thereby characterizing the degree of dynamic complexity in the system. As such, topological entropy is an aggregate measure of entropy production in a dynamical system as evaluated through its kinematic-geometric properties in phase space. Beyond Classical System Dynamics with Non-Ergodic Information Physics Aggregating thermodynamic properties and information-theoretical measures capture only part of the information content in the system when there is microphysical coevolution at play. The existence of codependences among microstates reshapes the entropy functionals, from which generalized information-theoretical metrics have been derived by Perdigão (2018b) to yield nonlinear polyadic measures of information for multivariate systems involving polyadic links (nonlinear interactions among multiple variables), including the macrophysical signatures of microphysical codependences elusive to the traditional aggregate metrics from information theory, thermodynamics and system theories. The extra information of the generalized measures relative to the classical approaches is highlighted by the information measure S N among statistically independent variables (Perdigão 2018b):

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S N (Y _α) ≡ Hq,r (Y ) −



Hq,r (Yα )

(5.6)

α

where H q,r is the polyadic entropy generalizing the Borges and Roditi (1998) and Kaniadakis et al. (2005) entropies from nonlinear statistical mechanics to high-order nonlinearly entangled multivariate systems (polyadic Y), which is given by (Perdigão 2018b): Hq,r (Y ) ≡ k

 pr − pmq m q −r m−1

(5.7)

e.g. for triadic systems (Perdigão 2018b):    {Hq,r (Yα ) + (1 − q){Hq,r (Yi ) + Hr,1 (Y j ) Hq,r Yi , Y j , Yl = α∈{i, j,l}

   + Hq,r (Yi ) + Hq,r Y j Hq,r (Yl )}     Hq,r Y j + Hr,1 (Yi )    + (1 − r ) + Hr,1 (Yi ) + Hr,1 Y j Hq,r (Yl )   + (1 − q)2 Hq,r (Yi )Hq,r Y j Hq,r (Yl )   + (1 − r )2 H1,1 (Yi )Hr,1 Y j Hq,r (Yl )   + (1 − q)(1 − r )[Hr,1 (Yi )Hq,r Y j Hq,r (Yl )   + Hr,1 (Yi )Hr,1 Y j Hq,r (Yl )]}

(5.8)

where pm is the probability that the microstate ym occurs in the macroscale variable Y, M is the number of microstates, and k is a positive constant stemming from entropy being only defined up to an arbitrary multiplicative constant i.e. from only entropy differences being meaningful in reality. The entanglement parameters q and r reflect the existence of nonlinear microphysical codependencies (e.g. event codependence) within Y, and manifest in natural settings such as anomalous diffusion in continuum mechanics and more generally in nonlinear cross-scale coevolution in complex systems. As such, the entanglement parameters are associated to the coevolution index introduced in Perdigão and Blöschl (2014) and more generally to kinematic-geometric features in the coevolution manifold introduced in Perdigão (2017) and generalised in Perdigão (2018a, b). The S N measure vanishes under the particular conditions where classical thermodynamics, information theory and system theories operate, namely under ergodic balance such as in systems that are not undergoing structural–functional coevolution. For ergodic non-Gaussian nonlinear systems, the synergistic measure resorts to the interaction information in Pires and Perdigão (2015). A positive S N means that the entropy of the combined system exceeds the sum of entropies of the intervening parties, which physically means that upon mixing,

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there is collective emergence of system features not present a priori in the classical stochastic-dynamic models governing any of the intervening parties. Conversely, a negative S N means that there is redundancy among the intervening processes, even if they are statistically independent as highlighted in this formula. This means that the absence of statistical redundancy does not imply absence of interactions between processes. In fact, a negative S N quantifies the amount of information redundancy among statistically independent processes, resulting from farfrom-equilibrium microphysical coevolution that leave an information footprint in the nonlinear statistical physics of the system being captured here. In a system where neither synergies nor redundancy are at play, the measure SN becomes simply the generalized polyadic entropy or self-information of the system (H q,r ). The kinematic-geometric information metrics for non-ergodic dynamical systems have been derived (Perdigão 2017) to yield explicit formulas for microphysical dynamic interactions. Once integrated over a macrophysical domain, these lead to the Perdigão (2018b) metrics discussed in the present section. A particular case of such metrics for ergodic systems lead to kinematic-geometric information metrics corresponding to the entropy metrics discussed in the previous section.

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Rui A. P. Perdigão (Prof. Dr.) is chair professor and head of the Meteoceanics Institute for Complex System Science (MICSS), head of the North Atlantic Climate Centre, interuniversity chair in Fluid Dynamical Systems, Physics of Complex Systems and Climate Dynamics. Moreover, he serves as Editor in international scientific journals including the EGU journal Earth System Dynamics (ESD), section Editor-in-Chief at Climate, and is corresponding member in Physics at the prestigious Lisbon Academy of Sciences. Overall, he leads academic and research programs across the Complex System Sciences and Technologies, including flagships ESDI (Earth System Dynamic Intelligence) and QITES (Quantum Information Technologies in the Earth Sciences), having pioneered the quantum gravitational and electrodynamic constellation Meteoceanics QITES for geophysical and astrophysical sensing. His theoretical and technological breakthroughs then come into down-to-Earth use in the development of advanced interdisciplinary sensing, analytics, modelling and decision support frameworks for the institutions entrusted with the protection of our society and the environment. He holds an interdisciplinary set of invited appointments including at the Institute of Telecommunications, the Institute of Social Sciences, the Portuguese Quantum Institute, the Centre for Ecology, Evolution and Environmental Changes, and the Doctoral Program on Climate Change and Sustainable Development Policies from the two leading Universities in Lisbon.

Chapter 6

Decoding Cool Urban Forms: Using Open Data to Build a Dialogue Between Microclimate and Configurational Morphology in Urban Environments Ata Chokhachian

and Aminreza Iranmanesh

Abstract Cities are composed of a multitude of interconnected interactive layers and systems. The contemporary urban discourse has seen the utilization of Open data in decoding and understanding complex urban patterns that have eluded researchers for decades. Different layers of raw data from historical city cores up to the atmospheric climate have become more accessible, opening new horizons for multidisciplinary research. The rising complexity of cities calls for emerging approaches that can address the relationship between different layers of data—existing or emerging. In this regard, the current chapter is introducing and applying a methodology to use historical, spatial, and temporal datasets from Open Street Map (OSM) processed by Space Syntax superimposed on simulated urban microclimate dataset to find correlating patterns on how urban morphology has shaped the cities and the microenvironments over time. The outcomes for the case of Munich, illustrate the typologies that can be utilized in planning and developing design strategies to address micro-climate and accessibility in cities. Keywords Urban morphology · Urban climate · Space syntax · Microclimate · GIS

6.1 Introduction Urban form is the result of incremental transformation and co-evolution of society and space. The intrinsic hierarchical structure embedded in the morphology of the access network of cities is formed by the aggregation of choices that every commuter makes through the course of daily life. Some parts of the city are frequented by A. Chokhachian (B) Chair of Building Technology and Climate Responsive Design, School of Engineering and Design, Technical University of Munich, Munich, Germany e-mail: [email protected] A. Iranmanesh Faculty of Architecture and Fine Arts, Final International University, Girne, North Cyprus e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_6

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people more than others, the hidden logic behind these differences has been a source of scholarly discourse in contemporary literature and has been approached from numerous perspectives. This chapter addresses three influential dimensions in these everyday decision-making processes, namely, microclimate, accessibility, and visibility. All three are fundamentally morphological phenomena but are rarely being studied together. Climate is one of the primary factors that define how frequently people use public space and outdoor environments. The built and natural environments regulate their surrounding microclimates that vary in a high spatiotemporal resolution (Chokhachian et al. 2018). The urban microclimate is a shared domain between climatologists and urban designers, however looking at the history of urban climate research, we can see that they have dealt with this topic differently in terms of scale, relevant variables, metrics, and resolution. The climatologists were more interested in Urban Heat Island (UHI) phenomena on a mesoscale but the urban designers have been more concerned about the environmental forces on and/or between the buildings (Maronga et al. 2020). On the other hand, the study of urban morphology mainly focuses on the patterns of building footprints and access networks of cities. Although the climate is mentioned in large as a contributor to the orientation of urban forms, the smaller scale microclimate remains understudied. Two parallel streets with similar surroundings might render significant sensory experiences due to their microclimate. These differences are often overlooked in the field of urban morphology, however, recent development in computational tools, the increase in computational power and the availability of open data has made it possible to explore microclimate and urban morphology under one umbrella. This defines the main objective of this chapter which is to show practical possibilities regarding the fusion of two seemingly separate urban models of morphology and microclimate.

6.1.1 Microclimate and Urban Forms In recent decades, several studies have focused on the intersection of microclimate and urban settlements indicating that improved outdoor thermal conditions are in direct connection with how people conceive and use outdoor space (Chokhachian et al. 2020; Perini et al. 2017). Designing a place with optimum comfort level may lead to positive urban development such as encouragement for cycling and walking, attracting more people to comfort zones in the city, and turning this opportunity into range of business and tourist attractions to shift the area economically profitable (Nikolopoulou et al. 2001). Comfortable outdoor spaces could be designed with a set of strategies according to the context like, planting trees with the advantage of evaporative cooling and shading effect or adding man-made canopies using local materials (Chen and Ng 2012). The subjective human sensation of feeling thermally comfortable is generally understood to depend on several factors and parameters. Air temperature, humidity,

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wind speed, and radiation are the four basic physical environmental parameters impacting the body’s heat balance (Fanger 1970). However, perceived temperature gradients, fluctuating wind, and solar radiation variations in spatiotemporal resolution are the key differences between outdoor and indoor thermal environments. These variations outdoors are mainly driven by urban morphology, orientation and the materiality of the street canyons, plazas, and public environments. The question of interdependencies between human outdoor activities with urban morphology and microclimate has not been fully explored due to the complexity of each domain on its own. However, several researchers have been exploring to put into evidence the spatial variations of urban environments and their impacts on people’s behaviour on using urban public space. Decoding these patterns in the first place can help the urban planners to understand the synergies of complex urban systems, furthermore, it can inform the planning processes to design and program more liveable and walkable public spaces. Addressing the question of microclimate variations and their effect on human behaviour, a study by Xu et al. (2019) demonstrates that through comparing the microclimate simulation results at different times of the year with Multi-Agent System, there is a significant impact of microclimate (solar exposure) on human behaviour in terms of path choice. On the same topic, Ji et al. (2019) developed a quantitative study for geometric characteristics of urban space based on the correlation with microclimate (wind effect). The existing body of literature suggests a complex set of correlations between urban spatial geometries and wind environment. The influential nature of these geometries however, does not remain merely bound to microclimate and extend to socio-spatial characteristics of urban morphology.

6.1.2 Urban Morphology The first methodological approach to the field of urban morphology was established through the seminal work of Conzen (1960) addressing the incremental transformations of urban forms. In this framework, the built environment is considered an information field that can be documented and cross-examined in search of emerging typologies (Gauthier 2005). The incremental processes that shape urban forms are influenced by a wide spectrum of variables. One of the main approaches on which these variables can be analysed concerning urban morphology is established via mapping techniques (Gauthier and Gilliland 2006). Mapping is a way of quantifying and cross-referencing various manifestations of urban forms. These techniques have been used in addressing urban morphology through the windows of density (Pont and Haupt 2007), social processes (Gehl 2011), micro-economy (Hillier 1996), informality (Kamalipour and Dovey 2019), urban ecology (Marcus et al. 2019), and land use (Iranmanesh et al. 2021) to name a few. Recently, there has been a call for the integration of quantitative research approaches in urban morphology (Pont 2018). Development in Geographic Information Systems (GIS) has made it possible to superimpose different datasets addressing the potential relationship between diverse

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dimensions of urban form (Jiang and Claramunt 2002). GIS makes the visualization and comprehension of complex datasets easier to understand. Moreover, the availability of open data, satellite imagery has opened new horizons yet to be explored. The relationship between configuration aspects of urban form and climatic models remains understudied, therefore, the current chapter aims to provide a synapsis into the potential methodological approaches that can address both urban morphology and microclimate.

6.2 Methodology Following the scope of the chapter to overlap urban morphology information with microclimate conditions, a hybrid methodology is applied to deal with different sources of data and simulation results (Fig. 6.1). This hybrid methodology consists of Space Syntax analyses (See Sect. 6.2.1) and outdoor solar and wind exposure modelling (see Sect. 6.2.2). First, the building footprint data was collected from Open Street Map (OSM) and is cross-referenced with satellite imageries. Second, the data was analysed in Depthmap software regarding urban form (Varoudis 2012). Reported outputs include local and global integration, Visibility Graph Analysis (VGA), and an agent-based simulation model.

Fig. 6.1 Data extraction, simulation, processing and data fusion workflow

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Third, The microclimate domain focuses on two main parameters that have the biggest contribution to the perceived temperatures in urban environments: sun and wind (see Salvati et al. 2020). To quantify the solar exposure potential in urban areas, the seasonal radiation, sunlight hours maps are generated separately for summer and winter using the 3D models of the area as input for the Radiance simulation engine. The wind field is also simulated for the same area using OpenFOAM engine and Eddy3D tools (Kastner and Dogan 2020). The potential correlations between the raster results of the Space Syntax and microclimate modelling were analysed in QGIS program to decode, quantify and cluster the invisible qualities of outdoor environments driven by urban morphology. This model could be potentially adjusted and repurposed for future studies focusing on the relationships between configurational and microclimatic dimensions of different cities.

6.2.1 Space Syntax: A Configurational Approach Toward Urban Morphology Urban space is the creation of incremental and complex social processes that adapt to the transformations of everyday life. In turn, the social constructs are influenced and shaped by the spatial structure that contains them (Harvey 2010). This continuous feedback loop which is an ongoing dialectic between social and spatial forms is central to the configurational approaches aiming to explore cities (Hillier and Hanson 1984). Space syntax is one such approach; it is a set of configurational theories that explore the bottom-up processes that form and reform space as a socio-spatial entity (Hillier 1996). At its core, space syntax revolves around two concepts; first, space is not a mere passive background to human activities, it is rather an intrinsic part of it (Bafna 2003). Accordingly, society and space are interwoven phenomena neither of which can be fully understood without the other. Second, space itself can only be understood in its relationship with other spaces, hence, to fully analyse a single space, the entire network and all potential connections between its parts must be addressed (Hillier and Vaughan 2007). The latter is the pure configuration core of the theory, indicating the interconnectedness of human-made spaces and their integrated social logic (Hillier and Hanson 1984). Space syntax literature builds around the aforementioned core concepts to address phenomena such as ‘the theory of natural movement’ (Hillier et al. 1993), ‘cities as movement economies’ (Hillier 1996), ‘the multiscale city’ (Kaplan et al. 2021), and ‘dual grid’ (Hillier 2012). These concepts are well explored in different fields of urban morphology. The current paper focuses on the theories of natural movement exploring typologies of urban space in relation to thermal comfort. The theory of natural movement describes the natural processes of choosing and navigating the space on the basis of its configurational attributes (Hillier et al. 1993). This theory indicates that the aggregated sum of people’s presence in urban space could be influenced by its configurational properties. This idea is represented via

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various network analytical measures such as integration (to-movement), and choice (through-movement). Integration (to-movement) is an analytical measure of closeness on a dual graph. Considering all parts of a given network as a potential origin/destination, the integration measure shows how accessible each node is from all other nodes. The calculation can be done at the global level (all nodes) and the local level (with topological or metric proxies). Integration analysis is closely related to the “theory of natural movement” described by Hillier et al. (1993) indicating that the spaces with higher global integration tend to be more active in terms of co-presence and pedestrian movement (Baran et al. 2008; Read 1999). Local integration has also been shown to be a significant predictor of pedestrian movement (Iranmanesh and Alpar Atun 2020). Choice (through-movement) is an analytical measure of betweenness for network elements. Considering travels among all possible origin destinations in a network, through-movement shows which spaces are more likely to be used as vessels of movement (Freeman 1977; Hillier 2012). Choice has been shown to be a better predictor of longer movement (Hillier and Iida 2005). Visibility Graph Analysis (VGA) represents the field of visual affordance concerning walkable voids between buildings. The analysis starts by superimposing a grid on top of open spaces and calculating how many units can be observed from all other units (connectivity) (Turner et al. 2001). The size of the grid often corresponds to the average human step but larger grids can be used in larger urban settings. The analysis then takes the metric average from which each cell can be observed (point first/second moment). Various analyses can be performed based on this model including the Agent-based model. The agent-based model first introduced by Penn and Turner (2002) is built upon the principles of VGA. Agents are randomly introduced to the grid; they then move and make three turns and get terminated. Then, the accumulated encounters of agents with each unit are rendered as a heatmap. The analysis is usually done via two main methods of decision making by agents, the first method uses a random method and the second method developed by Turner and Penn (2007) is using the longest line of sight (LOS) to weigh agents’ decisions. This model has shown significant improvement over the original approach in predicting pedestrian movements at the local level.

6.2.2 Outdoor Solar and Wind Exposure Modelling Methodologically, there are two main approaches to evaluate and estimate thermal comfort conditions in cities: one is a simulation-based approach and the other is based on sensing techniques. The simulation-based methods have been always dependent on computational power available on the hardware system, however, with the advent of cloud computing resources, it is possible to simulate complex and multi-scale urban models with reasonable time effort (Nazarian et al. 2019). Despite that, the current efforts to simulate perceived pedestrian thermal comfort in urban settings is enormous, since the environmental conditions are highly localized and

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involve phenomena that are time and calculation-intensive to simulate, plus requiring substantial urban data inputs. To simulate urban climate, atmospheric models need to have an adequate representation of the influence of the city on the exchanges with the anthroposphere (Masson et al. 2020). In other words, to fully describe the thermal sensation of a person walking or sitting under a tree needs multiple interdependent simulation models. In cities and their associated public spaces, transient conditions are experienced when people move between conditions that are situated in spaces with a wide range of varying environmental conditions. This includes step changes, temperature drifts, and cyclic variations that influence thermal sensation and comfort. The transient nature of thermal comfort has been widely discussed in indoor studies (Vellei et al. 2021). However, such understandings of outdoor environments are generally lacking (Young et al. 2022). The transient conditions are mainly driven by wind forces and solar gains which this study uses as outdoor comfort measures. These two parameters have very different effects based on seasonal scenarios, where in summer the main driver of discomfort is solar exposure which can be compensated with light wind breeze. In contrast, for the case of winter, the intensity of wind takes over, causing discomfort for the pedestrians and any potential sun exposure could help to compensate for the cold stress on a daily commute (Santucci et al. 2017). For this study, these effects are simulated using the computational models for different seasonal scenarios for the study area.

6.2.3 Study Area The developed methodology and workflows are applied to a case in the city of Munich. The study area is selected based on the availability of 3D models, Open data computational capacity, and a variety of typologies. The area covers 6 square km including the neighbourhoods of Maxvorstadt and Schwabing-West with low-rise building blocks, parks, and plazas. For the selected area 3 main data nodes are acquired: OSM data for street network, 3D models for microclimate simulations, and vegetation cover (Fig. 6.2).

6.3 Results The results are organized into three parts. The first two parts address the raw output of space syntax and microclimate models. The study uses a resolution of three meters in all analyses (Space syntax and microclimate) in order to make the cross-examination of data more comparable. In the third part, the two models are superimposed and the patterns of correlation among the data layers are explored. The study builds a

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Fig. 6.2 Available layers of open data for the analyses

discussion addressing the strength and weaknesses of the isolated models and what can be learned by overlaying the results.

6.3.1 Space Syntax Space syntax analysis starts with integration and choice analysis on a road centre map adopted from the OSM data (Fig. 6.1). All obstacles that were identifiable from the satellite imageries were included in the model aiming to increase the accuracy and reliability of the outcome. The analyses are conducted at two scales, first, with a metric proxy of 800 m representing local accessibility of pedestrian movement, and second, at global scale representing the large-scale movement through the entire network. The urban grid in this case has two major grid systems, one constructed of major streets and one with smaller intricate footpaths in public spaces and also inside the courtyards. The integration analysis reflects this duality, the spaces with more footpaths show stronger potential for local pedestrian movement (Fig. 6.3: Top right), whereas the global integration prefers longer continuous streets. Choice analysis which is a better predictor of longer movements shows a similar tendency. The major traffic arteries are highlighted as the most in-between spaces on the global scale (Fig. 6.3 second row). Nevertheless, local choice shows stronger in-between paths close to parks and open public spaces where making shortcuts is a viable decision. The southeast part of the case study which is close to the historic core of the city and shows more organic patterns is highlighted in the analysis. It could be argued that these types of spatial networks improve the possibility of pedestrian movement

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Fig. 6.3 Space syntax analysis of the selected area

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when compared with the long grid structures which provide limited possibilities for shortcuts. Visibility Graph Analysis (VGA) shows the visibility relations among open spaces. The largest open spaces here are highlighted with the area in front of Propylaea being the most visible/connected part of the grid. It is interesting to see that although the large open park at the northern part shows high visual connectivity (Alter Nordfriedhof—Cemetery) when introducing metric distance into the model, it declines as a visually integrated part due to its surrounding inward-looking blocks with grid characteristics (Fig. 6.3 third row, right). The agent-based model is constructed on the idea that the immediate surrounding and the mere possibility of movement increases the possibility of interaction (Fig. 6.2: bottom row). The agents in this case are making simple decisions (random or based on the available LOS). Nevertheless, it has been shown that the aggregation of agents’ movements renders a reliable image of what the space can potentially offer. In this case, the agents seem to prefer open spaces that are connected at the local level. Three hotspots are visible in the random movement, and six on the improved model (LOS).

6.3.2 Microclimate In this study, the seasonal microclimate is simulated considering different times of the year for summer and winter cases. Additionally, the wind patterns are also simulated to capture the areas with wind discomfort especially relevant for the winter case. Based on the analysed climate data, the prevailing wind for Munich is mainly from east to west. Based on this input, the wind results show relatively high wind exposure in plazas and open areas which can contribute to strong cold stress in winter. Furthermore, the East–West street canyons project wind speeds between 3 and 5 m/s where North–South canyons are more protected with wind exposure up to 2 m/s (Fig. 6.4). The solar exposure maps for summer (July) and winter (January) cases show diverse patterns and potentials. The open areas and plazas in summer project up to 480 h of direct solar exposure in a month which for winter this number is almost half. However, the open areas in winter provide the opportunity for meeting points and gathering areas for a sunny day. The solar radiation maps also show the same patterns as sunlight hours with including the cloud cover and actual climate condition. As we see in Fig. 6.4, the potential amount of solar radiation falling on a person walking outdoors in winter does not go over 40 kWh/m2 which is mainly in open plazas. This seasonal and morphological diversity is crucial in urban environments to compensate and give comfort opportunities at different times of the year based on pedestrians’ preferences. In the scale of canyons, thanks to the compact morphology of the Maxvorstadt area, the streets stay well-shaded over the summer and the courtyards provide a pleasant microclimate for the buildings as well.

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Fig. 6.4 Microclimate analyses of the selected area

6.3.3 Data Fusion After processing the Space Syntax and microclimate models, all layers of data were superimposed as roaster files in QGIS software using a local coordinate reference system (EPSG:5243—ETRS89). Here, the study explores some of the significant

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Table 6.1 Exploring the correlation coefficients between radiation and selected space syntax measures Connectivity

PSM

Agent-based

Agent-based LOS

Winter radiation

r

0.728

0.567

0.562

0.656

Summer radiation

r

0.482

0.477

0.490

0.504

relationships among the two sets of data. It must be noted that exploring all possible relations will exceed the limitation of this paper and can be considered for further studies. The study was able to identify statistically significant correlations between the simulated wind and connectivity (r = 0.37), Agent count (r = 0.32), and LOS agents (r = 0.35). These finding are resulted from the open spaces being more visible, accessible, and simultaneously more exposed to the wind. This brings forth the necessity of taking wind into account for the design of urban open spaces. The most significant difference between the VGA and wind simulation occurs at the corners of the buildings, since the edges redirect and intensify the wind whereas the same barriers limit visibility. A similar set of correlations were identified regarding radiation and space syntax measures as well (Table 6.1). Two reasons can be identified explaining limited compatibility between climate models and space syntax analyses. First, the sun exposure is uniform, whereas the space syntax analysis has intrinsic variation depending on the spatial location of individual spaces. Second, space syntax measures are based on the abstract 2-dimensional figure-ground map (cut at eye level or knee level in different scenarios). This featureless representation of available visibility/movement sees spaces as nodes in a larger network disregarding any other qualities. It is evident here, that the two models can complement one another and there is a necessity to include 3D features of the built environment in analysing urban areas with the scope of configurational morphology. Differences between the two models (space syntax and microclimate) are clear when conducting a multiple regression model. To demonstrate this with an example, a regression model was constructed to explore the predictability of the LOS agentbased model by winter radiation + wind (LOS agent models was selected as outcome variable since it shows the highest internal correlation with all other space syntax measures). Although the regression model is significant at 0.01 and can explain 46% of the variation in LOS, the significant differences between the estimated map and residual call for a close inspection of various morphological patterns (Fig. 6.5). The problem seems to be the diverse spatial patterns within the dataset rendering urban spaces that score differently across the board. Accordingly, the study explored different variations in the dataset. The microscale analysis of the predicted variety in typo-morphological patterns (Fig. 6.5). In this part, some instances of different observed typologies and their attributes are extracted. The purpose here is to demonstrate intrinsic diversities of urban forms that might not be visible when using only one of the models (space syntax or microclimate). Six types were analysed using a selected number of variables, although, many more could be potentially explored the full spectrum of analysis. Nevertheless, these

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Fig. 6.5 Left: estimates Map, and right residual map of overlaying climate layers with space syntax analysis (R2 = 0.462)

examples showcase the potential of the current approach in reading multi-disciplinary complexities of urban forms. This method can be utilized for decision-making and design considering accessibility and climate models under one umbrella. From the extracted typologies illustrated in Fig. 6.6, Type A shows two streets that score similarly in space syntax measures but are significantly different in terms of their exposure to wind and sun. Type A1 is an East–west street that receives high winds and high solar radiation in summer, but very little exposure during the winter. This type might be among the most difficult to navigate during colder seasons. Type A2 which intersects with the A1 has protection from the wind and receives relatively high exposure both in summer and winter, it can be speculated that this type might be more difficult to navigate during the summer. The high similarity of the two types in terms of space syntax measures highlights the intrinsic shortcomings of the method and the necessity of utilizing complementary analyses when addressing urban forms. Type B represents a local walkable open public destination. This is not an inbetween space. It is relatively accessible, exposed to wind and sunlight both in summer and wintertime. Type C is the most accessible part of the urban network based on the space syntax analysis. This type represents major central public spaces. The open space shows high integration and choice at both local and global scales. It indicates that not only the space is a desirable destination, it is also a potential passage of movement when walking between other destinations in the network. Furthermore, it receives high winds and sun exposure throughout the year. These features make it a prime candidate for a vital urban space that needs to be designed properly for microclimate issues. Type D shows an instance of the courtyard spaces, the inward-looking sides of the urban block. This is an abundant type that represents the background residential spaces of the city. By nature, these spaces are not intended to have high integration or choice value. They are rather the calm and quiet open spaces serving the everyday life of residential buildings with the potential of opening to public. The enclosure

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Fig. 6.6 Instances of various spatial patterns cross-referencing climate models and space syntax measures

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that the surrounding buildings create, minimizes the wind and sun exposure, this, in turn, might create some desirable traits in summer, and protected from wind in winter. Type E is highly accessible north–south streets with minimum wind and maximum sun exposure. The difference between type E and A2 is their high accessibility both as a destination and an in-between space. The high potential that this street type shows for movement and the fact that it lacks wind and is highly exposed to the sun during summer requires attention in microclimate urban strategies.

6.4 Discussion Urban forms affect how people move, interact, and shape everyday life of cities. These forms create possibilities through which the citizens render their choices. Moreover, considering the right to the city as an intrinsic dimension of modern humanity (Lefebvre 1996), open public spaces as the nexus of these rights must create suitable settings through which urban life can flourish. Accessibility and comfort are among the major drivers influencing how people interact and produce the social life of the city. Within these frameworks, Space Syntax is among the most used methods in analysing the configurational aspects of urban form regarding accessibility. Although it has shown unprecedented efficiency in predicting aggregated movements in the urban grid, its monotonous approach in reading space leaves much to desire. As demonstrated in this study, spaces with similar configurational properties might show significant differences in terms of their microclimatic dimensions. Orientation and contextual microclimate patterns can create different experiences in two adjacent streets with identical integration and choice measures. This is not merely a shortcoming related to how Space Syntax explores space, it further highlights the fact that configurational analyses alone cannot provide a comprehensive reading of urban spaces. Furthermore, the temporal dimensions of urban space are often ignored in these analyses. The effects of seasonal peaks can influence movement beyond their accessibility measures. Similarly, the climate models alone cannot render a reliable reading of urban space for the same reasons. The study was able to identify spaces with very similar sun and wind patterns but extremely different potential accessibility (Type A and type E). Illustrated differences in these instances put emphasis on the necessity of developing multi-dimensional and multi-disciplinary design strategies with the capacity of taking these differences into account. Results also indicated the moderate but significant correlation between wind exposure, winter and summer radiation and space syntax measures such as agent-based models. For instance, parks and plazas are often highly visible and highly accessible and consequently, they are more exposed to the wind. This might be a preferable trait in summer, but design precautions for colder seasons need to be implemented.

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It could be speculated that the co-evolution and incremental transformation of space and people’s movement together often generates a level of compatibility between climate and urban spaces.

6.5 Conclusion A deeper understanding of urban morphological patterns calls for inter-disciplinary research that can bring diverse approaches together. This is essential as the city is a complex entity or as Jacobs (1961) puts it a problem of “organized complexity”. The quantitative methods of analysing and simulating urban forms have unveiled many hidden interconnected aspects and qualities of cities. This study has tried to showcase possibilities of reading urban spaces by integrating different layers of data from two disciplines. The study illustrates that the overlapping of two models improves both domains while highlighting their shortcomings simultaneously. Open data and advancements in computational methods have opened up new horizons for urban research. Accessibility to reliable data and resources has always been a barrier to scholarly activities. The area of WEB2.0 has made it possible to access open sources of data and explore understudied dimensions of cities. The current chapter used different sources of open data to create a model that can explore the potential accessibility of the urban open spaces and climate models together. The typologies that emerged from this study can be utilized in planning and design strategies addressing microclimate and accessibility. Urban destinations should provide a calm, comfortable and at the same time diverse experience. These spaces must be flexible and resilient to cope with the variations in different seasons. The nature of urban experience is in the possibilities that it provides for interactions among people; many of such interactions take place in these destinations. On the other hand, through-movement is highly critical in the vitality of the local micro-economy. Pedestrian movement and foot traffic economically activate the local shops and generates jobs. Spaces that score high in this regard are often those that function as vessels of movement. However, the study was able to identify types of spaces that scored highly in choice (betweenness) but showed significantly different microclimates (wind and sun exposures) due to their spatial orientation that can be spot to recover from thermal stress in different seasons. Space syntax analyses do not often include these attributes, but these findings indicate it is essential not to ignore them. The design of urban spaces needs to reflect the intricate complexities that are intrinsic to them and the city. This is only possible through the expansion of multidisciplinary knowledge of urban forms. Scholars of different disciplines within the domains of architecture, urban design, and planning have mainly focused on their special interests. This approach might be in contradiction with the reality of how cities work. Cities are complex by nature, therefore, analysing them which will lead to better design, planning, and policy-making must reflect those complexities and avoid mono-directional approaches. In order to facilitate incremental positive

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changes aiming at the betterment of future cities, contemporary urban discourse needs to encourage openness and multi-disciplinary approaches offered by wide range of data sources. This chapter showcased some possibilities in overlapping two models that are often explored separately, accordingly, it could be argued that more dimensions (economy, political, land use, infrastructure, transportation, social media, and culture to name a few) can be integrated using emerging open data sources and advancements in computational analysis. In the end, it must be noted that the current chapter has many limitations in its approach that might be addressed in future studies. First, the fusion stage was not able to cross-examine all variables. This was mainly due to the limited space reserved for the chapter; however, the study illustrated a model that can be potentially developed and used for future studies. Second, the study was not able to include and compute all vegetation surfaces in all models. During the initial stage, 6120 trees were identified in the studied area, but the currently available analytical and simulation tools to the authors made it unfeasible to fully integrate these details into all models.

References Bafna S (2003) Space syntax: a brief introduction to its logic and analytical techniques. Environ Behav 35(1):17–29 Baran PK, Rodríguez DA, Khattak AJ (2008) Space syntax and walking in a new urbanist and suburban neighbourhoods. J Urban Des 13(1):5–28 Chen L, Ng E (2012) Outdoor thermal comfort and outdoor activities: a review of research in the past decade. Cities 29(2):118–125 Chokhachian A, Ka-Lun Lau K, Perini K, Auer T (2018) Sensing transient outdoor comfort: a georeferenced method to monitor and map microclimate. J Build Eng 20:94–104 Chokhachian A, Perini K, Giulini S, Auer T (2020) Urban performance and density: generative study on interdependencies of urban form and environmental measures. Sustain Cities Soc 53:101952 Conzen MRG (1960) Alnwick, northumberland: a study in town-plan analysis. Trans Pap (Institute of British Geographers) (27):iii-122 Fanger PO (1970) Thermal comfort: analysis and applications in environmental engineering. Danish Technical Press, Copenhagen Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41 Gauthier P (2005) Conceptualizing the social construction of urban and architectural form through the typological process. Urban Morphol 9(2):83 Gauthier P, Gilliland J (2006) Mapping urban morphology: a classification scheme for interpreting contributions to the study of urban form. Urban Morphol 10(1):41 Gehl J (2011) Life between buildings: using public space. Island Press, Washington, DC Harvey D (2010) Social justice and the city. Vol. 1. University of Georgia press Hillier B (1996) Cities as movement economies. Urban Des Int 1(1):41–60 Hillier B (2012) Studying cities to learn about minds: Some possible implications of space syntax for spatial cognition. Environ Plan B Plan Des 39(1):12–32 Hillier B, Hanson J (1984) The social logic of space. Cambridge University Press, Cambridge Hillier B, Vaughan L (2007) The city as one thing. Prog Plan 67(3):205–230 Hillier B, Penn A, Hanson J, Grajewski T, Xu J (1993) Natural movement: or, configuration and attraction in urban pedestrian movement. Environ Plan B Plan Des 20(1):29–66 Hillier B, Iida S (2005) Network and psychological effects in urban movement. In: Paper presented at: international conference on spatial information theory. Springer

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Iranmanesh A, Alpar AR (2020) Reading the urban socio-spatial network through space syntax and geo-tagged twitter data. J Urban Des 25(6):738–757 Iranmanesh A, Cömert NZ, Ho¸skara SÖ ¸ (2021) Reading urban land use through spatio-temporal and content analysis of geotagged twitter data. GeoJournal Jacobs J (1961) The death and life of great american cities. Random House, New York Ji H, Peng Y, Ding W (2019) A quantitative study of geometric characteristics of urban space based on the correlation with microclimate. Sustainability 11(18):4951 Jiang B, Claramunt C (2002) Integration of space syntax into GIS: new perspectives for urban morphology. Trans GIS 6(3):295–309 Kamalipour H, Dovey K (2019) Mapping the visibility of informal settlements. Habitat Int 85:63–75 Kaplan N, Burg D, Omer I (2021) Multiscale accessibility and urban performance. Environ Plan B Urban Anal City Sci 23998083211024648 Kastner P, Dogan T (2020) A cylindrical meshing methodology for annual urban computational fluid dynamics simulations. J Build Perform Simul 13(1):59–68 Lefebvre H (1996) The right to the city. Writ Cities 63181 Marcus L, Pont MB, Barthel S (2019) Towards a socio-ecological spatial morphology: integrating elements of urban morphology and landscape ecology. Urban Morphol 23(2):115–124 Maronga B, Banzhaf S, Burmeister C, Esch T, Forkel R, Fröhlich D, Fuka V, Gehrke KF, Geletiˇc J, Giersch S, et al (2020) Overview of the palm model system 6.0. Geosci Model Dev 13(3):1335– 1372 Masson V, Heldens W, Bocher E, Bonhomme M, Bucher B, Burmeister C, de Munck C, Esch T, Hidalgo J, Kanani-Sühring F, et al (2020) City-descriptive input data for urban climate models: model requirements, data sources and challenges. Urban Clim 31:100536 Nazarian N, Acero JA, Norford L (2019) Outdoor thermal comfort autonomy: performance metrics for climate-conscious urban design. Build Environ 155:145–160 Nikolopoulou M, Baker N, Steemers K (2001) Thermal comfort in outdoor urban spaces: understanding the human parameter. Sol Energy 70(3):227–235 Penn A, Turner A (2002) Space syntax based agent simulation. In: Schadschneider S, Schreckenberg M, Deo S (eds) Pedestrian and evacuation dynamics. Springer-Verlag, Germany, pp 99–114 Perini K, Chokhachian A, Dong S, Auer T (2017) Modeling and simulating urban outdoor comfort: coupling envi-met and trnsys by grasshopper. Energy Build 152:373–384 Pont MB, Haupt P (2007) The relation between urban form and density. Urban Morphol 11(1):62 Pont MB (2018) An analytical approach to urban form. In: Teaching urban morphology. Springer, Berlin, pp 101–119 Read S (1999) Space syntax and the dutch city. Environ Plan B Plan Des 26(2):251–264 Salvati A, Palme M, Chiesa G, Kolokotroni M (2020) Built form, urban climate and building energy modelling: case-studies in Rome and Antofagasta. J Build Perform Simul 13(2):209–225 Santucci D, Chokhachian A, Auer T (2017) Impact of environmental quality in outdoor spaces: dependency study between outdoor comfort and people’s presence. In: Paper presented at: S ARCH 2017. Hong Kong Turner A, Doxa M, O’Sullivan D, Penn A (2001) From isovists to visibility graphs: a methodology for the analysis of architectural space. Environ Plan B Plan Des 28(1):103–121 Turner A, Penn A (2007) Evolving direct perception models of human behavior in building systems. In: Pedestrian and evacuation dynamics 2005. Springer, Berlin, pp 411–422 Varoudis T (2012) Depthmapx multi-platform spatial network analysis software. Version 030 OpenSource. https://varoudis.github.io/depthmapX/ Vellei M, de Dear R, Inard C, Jay O (2021) Dynamic thermal perception: a review and agenda for future experimental research. Build Environ 205:108269 Xu T, Tong Z, Xu S (2019) Integration of microclimate into the multi-agent system simulation in urban public space. Smart Cities 2(3):421–432 Young E, Kastner P, Dogan T, Chokhachian A, Mokhtar S, Reinhart C (2022) Modeling outdoor thermal comfort along cycling routes at varying levels of physical accuracy to predict bike ridership in Cambridge, MA. Build Environ 208:108577

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Ata Chokhachian (Dr. -Ing.) is a research scientist, educator, and advisor in the domains of building technology and urban climate, developing and employing computational decisionmaking processes, tools, and workflows for architects and urban planners. Since 2015, he has been appointed as a research associate at the chair of Building Technology and Climate Responsive Design, as well as chair for Architecture Informatics at the Technical University of Munich. In 2022, he defended his Ph.D. dissertation in developing experimental and simulation-based tools to quantify outdoor thermal comfort conditions in urban environments. In the summer of 2019, he was appointed as a visiting research fellow at the Sustainable Design Lab at the Massachusetts Institute of Technology. In January 2020, he co-founded Climateflux, a company offering platforms for data-driven and computational workflows for acquiring climatic knowledge. Aminreza Iranmanesh completed his PhD in Architecture at Eastern Mediterranean University in 2019. He is currently lecturing at the Faculty of Architecture and Fine Arts, Final International University. His studies address new dimensions of city form by cross-examining emerging layers of data such as user-generated geotagged social media or analytical simulations with traditional layers of urban morphology. His studies aim to construct nuances in methodological approaches toward the city which has been made possible by the advancements in computational methods and accessibility of open data. The changing dynamic of everyday life of the city after the advent of WEB 2.0 is often highlighted in his research. Furthermore, He has explored the pedagogical dimension of urban design and architecture in light of advancements in digital technology.

Chapter 7

From Amsterdam to New Amsterdam to Amsterdam: How Urban Mobility Shapes Cities Tom Benson , Fabio Duarte , and Carlo Ratti

Abstract Autonomous vehicles are anticipated to be a widespread and well-adopted aspect of urban infrastructure by the end of the decade. With research and development scaling up over the past few years, studies on autonomous technology’s built environment impacts are still in their infancy—but we can look to the history of urban mobility to inform our understanding of its future trajectory. In this chapter, we add to the discussion of how mobility has and continues to shape infrastructure in cities, presenting research projects from MIT’s Senseable City Lab along with their accompanying historical contexts. First, offering a brief overview of the urban planning histories of both Amsterdam and New Amsterdam, or present-day New York City, we then examine how software and hardware innovations in the AV industry could transform citizens’ movement through urban areas and interactions with physical infrastructure. Looking to Amsterdam’s famous canals, we investigate how the development of autonomous vehicles serves as an example of programmable infrastructure that responds in real-time to human behaviour. Following this, we propose how implementing autonomous ridesharing systems in cities could provide opportunities for repurposing present-day automobile infrastructure, i.e., parking. These case studies shed light on the possibilities AVs present for expanding infrastructure’s capabilities as dynamic, responsive conduits of city residents and resources, which raise questions about how we define infrastructure versus transit and whether such a distinction will exist in future urban mobility.

The original version of the chapter was revised by correcting the details of the reference “Benson and Duarte (2020)”. The correction to this chapter can be found at https://doi.org/10.1007/978-3-031-03803-7_12 T. Benson (B) · F. Duarte · C. Ratti Senseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA e-mail: [email protected] F. Duarte e-mail: [email protected] C. Ratti e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_7

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Keywords Complex systems · Data-integrated methods · Artificial intelligence (AI) · Urban mobility · Real time analytics/big data analytics

7.1 Introduction Over a century ago, a group of French artists was tasked with envisioning what technology, infrastructure and urban mobility would look like in the year 2000. The images (Fig. 7.1) published by science fiction writer Isaac Asimov in 1988 created public imagination with various technological inventions that seem possible in the age of science (Asimov 1988). Some visions included flying police, an underwater whale bus, automation in agriculture, mail carriers delivering post by drones or modern-day cleaning robots. However, the En L’An 2000 image series neglected to predict how the automobile would dominate urban settings for the last century. Cities have had a growing dependency on the car, leading to challenges, especially heavy traffic congestion, environmental and noise pollution, global warming, and high infrastructure costs. The alteration of city infrastructure for the car was a twentieth-century phenomenon that occurred across the world (Le Corbusier 1987). Continuous development and innovation of this infrastructure meant that the automobile was leaving its own physical mark. Cars replaced train transportation and assumed priority on previously pedestrianised streets, and influenced central areas of cities by implementing parking infrastructure. Additionally, the way we inhabit space is formed by infrastructure—the public spaces, construction regulations, and neighbourhood services. It has the capacity to act as the framework for prospering communities;

Fig. 7.1 The flying mail carrier image from the En L’An 2000 series that predicted urban mobility for the year 2000 (Côté 1899)

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however, it can additionally set harmful patterns if deployed inadequately (Lewin 1947). After the twentieth century, cars and infrastructure for their exclusive use had become a critical feature of urban planning in cities. Academic acknowledgement of this originated with scholars such as Peter Norton forming the tag ‘automotive city’, indicating the adaptation of urban design and spatial layouts to the rise of the automobile (Norton 2008). Cities such as Detroit, Melbourne, and Los Angeles saw a large intake of highways, transport corridors, and securing of land for parking infrastructure (Newman and Kenworthy 1996). This continued dramatically in South America when in 1950, Brasilia, Brazil’s capital, was designed from scratch with the idea that its residents would be travelling by car (Costa and Lee 2019). The spatial organisation and infrastructure had few sidewalks, no cycle lanes, no traffic lights, and six-lane streets were formulated to prevent traffic jams. With so much priority given to the car, the formal approach designed by Oscar Niemeyer and Lucio Costa failed to consider the other elements that influence how city systems work: human mobility, transportation, economics, public life, and commerce. Parking infrastructure is a prime case of how urban areas are automobile-centric. An average car parking slot takes as much space as a small studio flat, and in the US, the quantity of infrastructure allocated to parking is more than the extent of Puerto Rico (Eran Ben-Joseph 2012). Moreover, in the popular video game SimCity, the creators were compelled to fake that all parking infrastructure would be underground as the game would be “really boring if it were the true equivalent in terms of parking spaces” (BLDGBLOG 2021). The large demand that comes with car-oriented developments have dominated physical space, and simultaneously increased traffic congestion and reduced the livability of cities. Since the early 2000s, cities have seemed to embrace human-centric strategies with low-cost improvements such as the addition of bike lanes, the replacement of parking spots with cafe seating and parklets, and the pedestrianisation of streets (Duarte and Ratti 2018). Municipalities around the world are making strides to improve their cities through new policies and urban interventions. The city of Amsterdam launched a novel plan in 2019 that will dramatically decrease traffic in the city centre, striving to eliminate over 10,000 parking spots by 2025 (City of Amsterdam 2019). The city will aim to re-use these areas to increase sidewalk space, greenery, and bike lanes. In the UK, the city of Birmingham has historically been known as the country’s ‘motorway city’. The municipality is now developing plans to reduce the number of vehicles in its city centre, create a network of pedestrian streets, and allow more space for cycling and shared transport modes. The strategy included providing companies with incentives to relinquish their car parking spots, and the city plans to replace them with residential homes (Birmingham Transport Plan 2020). Nevertheless, despite these recent planning developments, automobile-specific infrastructure is still a highly prominent feature in cities. As a result, cities worldwide are still highly congested and contribute significantly to global emissions. The contribution of the automobile to emissions has been significant with the current climate crisis with air pollution—one of the most significant challenges facing humanity worldwide—at an all-time high, with vehicle traffic congestion a significant

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contributor to this in cities worldwide. Vehicle traffic produces nearly 20% of the anthropogenic emissions of nitrogen oxides, which increase fine particulate matter (PM2.5) and 38,000 yearly premature deaths worldwide (Anenberg et al. 2017). Exposure to ozone and PM2.5 can cause adverse health effects, including premature mortality due to cardiopulmonary diseases and lung cancer (Silva et al. 2016). With the increasing influence of digitisation on cities’ infrastructure and thus human behaviour, urban planners are viewing such technological innovation as critical to determining spatial and social urban structures (Batty 1997). The convergence of bits and atoms in this digital age provides ripe conditions for radically transforming urban mobility and reducing the significant transportation issues we face with technological advances in hardware and software such as the Autonomous Vehicle (AV) and Artificial Intelligence (AI) offer promise as parts of a solution (Ratti and Claudel 2016). This chapter speculates how cities can move beyond the current scenario with passive infrastructure with closed-based mobility with vehicles acting as individual elements, has affected city life and physical space, and the potential of introducing open-based mobility and the effect of having active infrastructure using vehicles in a connected fleet in land and water. Looking at research activities conducted at the Massachusetts Institute of Technology’s Senseable City Laboratory,1 we aim to shed light on the future of urban mobility by focusing on four AV and AI projects and look at how these software and hardware technological developments could transform how people move through cities and interact with physical infrastructure.

7.2 Amsterdam Amsterdam is a city well-known for its infrastructure, with some dating back to the 17th-century. Characterised by its layout of interconnected canals, the fundamental importance of these channels has gradually evolved throughout time. Initially, the canals were constructed for water management and defence. However, as the city expanded, these waterways were used to transport goods and acted as a critical feature for urban logistics. At the start of the twentieth century, as the car became a predominant feature in Amsterdam, the canals were under threat of being paved over to create space for the automobile and ease traffic congestion. In 1967, urban planner David Jokinen proposed the ‘Jokinen Plan’, seen in Fig. 7.2, for Amsterdam, aiming to revamp the city for the automobile by filling in the canals and replacing them with six-lane highways and high-rise buildings. Still, throughout the 20th-century, the canal area has been halved and paved over for the automobile, providing more space for a higher demand of street traffic. In this case, the canals also lost their importance in being an essential conductor for moving goods and services, which shifted to the streets. These changes resulted in increased traffic and related emissions, as well as higher congestion in the city centre (Cox and Koglin 2021).

1

senseable.mit.edu.

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Fig. 7.2 David Jokinen proposed the ‘Jokinen Plan’ in 1967 for Amsterdam that replaced the canals with highways to allow more space for vehicles (James 2019)

Although the canals lost half of their spatial area during the last 50 years, the canals are still seen as an iconic image for the Dutch city, making it a sought-out location for its residents and tourists. Today, the canals still take up 25% of Amsterdam, which leaves the potential for utilisation of the waterways to alleviate some of the city’s issues like traffic management. Moreover, the purpose of the canals has transformed into merely a space for social boating, and they are often occupied by tourist boat trips (Dai et al. 2019). The city of Amsterdam has an opportunity to reform its canal system, lessen street transportation issues, and utilise the area for various urban functions. Here, we

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Fig. 7.3 Artist image of Roboat (Leoni et al. 2021)

introduce Roboat,2 a research project between the Massachusetts Institute of Technology and the Amsterdam Institute for Advanced Metropolitan Solutions, which aims to bring back the importance of the canals to the city by deploying the first fleet of autonomous boats (Duarte et al. 2020). The project combines architecture, AI, robotics, environmental engineering, and computer science that allow the vessels to work as a single unit, such as for collecting floating trash dumpsters or moving people and goods (Park et al. 2021). In addition, the units are capable of combining into various formations and shapes to construct temporary bridges and platforms, utilising open-based mobility, allowing for urban infrastructure to be dynamic (Fig. 7.3). Roboat is capable of multiple configurations by using sensor technology with LiDAR and cameras. To self-assemble into these configurations, the project requires robotic developments such as multi-vessel coordination, predictive trajectory planning, obstacle avoidance, motion planning, and perception mapping (Park et al. 2019). This allows real-time responsive routing optimising the trajectories of the vessels (Gast et al. 2019). In addition to these robotic developments, Roboat requires two design features to construct temporary infrastructure. First, the physical design of the prototype requires modularity to allow for self-assembly and flexible spatial arrangements (Benson and Duarte 2020). Second, the units can be connected with other units and structures, utilising an embedded modular latching mechanism to build floating infrastructure (Mateos et al. 2019). The latching system enables vessels to efficiently

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roboat.org.

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Fig. 7.4 Render of the roundAround concept that connects the Nemo Museum and the Marineterrein through circling units—creating the world’s first dynamic bridge (Leoni et al. 2021)

attach and detach to other vessels, or to canal wall edges to allow people to access the infrastructure from the street edge. The investigation of the impact of Roboat on Amsterdam’s infrastructure has been explored further in a concept called roundAround3 (Leoni et al. 2021)—the world’s first dynamic bridge. RoundAround shows the potential of connecting two parts of Amsterdam: Marineterrein, an area thriving with start-ups and academic institutes, and the Nemo Science Museum, which is currently separated by a waterway and takes over ten minutes to walk to. Roboat could reduce this travel distance to two minutes by circling vessels from one edge to another edge, so people to hop on and hop off and be transferred to the other side. Moreover, the concept aims to provide valuable feedback loops between the citizens and visitors of the city and the city’s infrastructure, enabling them to coordinate dynamically and flexibly with citizens’ and the cities’ needs (Fig. 7.4). The roundAround concept reimagines the relationship between vehicle and infrastructure, unveiling the potential for the city to adapt dynamically to human needs. Currently, making adjustments to fixed bridges is a costly and lengthy process. However, through AI and Machine Learning, the units can construct mobile platforms and bridges that serve the city’s social and economic life by responding to citizen’s 3

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needs throughout the day. During morning rush hour, the vessels can combine to construct a bridge that elevates traffic management and decreases congestion on the historical narrow streets; while in the afternoon, they can form a plaza for food markets or cultural events, blurring the line between water and canal edge; and in the evening, they can create a social play space for families to gather. Roboat’s design shows the potential in open-based mobility with a fleet of AVs that can create adaptive environments, enabling flexible reshaping of the urban environment. The Roboat and roundAround projects reveal how the boundary separating computers as (1) an analytical tool that furthers our knowledge of cities and (2) an instrument to adapt infrastructure could become not only blurred but could practically disappear. As city planning continues to evolve, we will need to consider novel forms of connected networks and infrastructure in combination with traditional methods. These opportunities will bring new forms of spatial organisation and allow for unique social and physical interactions in the digital age.

7.3 New Amsterdam On June 16, 1903, Henry Ford founded the Ford company, and he had aspirations for it to revolutionise the automobile industry on a worldwide scale. Initial constructions of the car in the late 19th and early twentieth centuries were slow and costly; however, this changed with the realisation of the assembly line, which used state-of-the-art construction techniques for mass production. With the introduction of Ford’s Model T automobile in 1908, its main selling point was its accessible price for an ordinary working person, who could afford to buy Ford’s vehicle with a couple of months’ pay. The Model T was able to achieve a low manufacturing cost by making use of the assembly line which reduced the time spent to construct a vehicle down from 12 h to one hour. Consequently, this made the car accessible to millions of Americans, which, as expected, led to unprecedented purchases of over 15 million Model T’s due to the convenience it could bring to daily life (Brooke 2008). As years followed, the USA experienced a surge in new building construction, which was inevitably linked to the development of the automobile. Sociologist John Urry used the term ‘automobility’ to describe the automobile and all the elements included to maintain the car (Urry 2004). He saw the automobile’s influence more than merely getting from one location to another location but extended to its production, marketing, and its associated services, products, and infrastructure. Essentially, the car began to exert a transformative footprint on economic and spatial conditions worldwide. At the turn of the twentieth century, the streets of New York City (NYC), previously known as New Amsterdam, were fully pedestrianised, allowing for public activities and events. However, following the Model T’s rapid rise and the expanding influence of automobility in cities, NYC started to adapt its physical infrastructure accordingly. As sidewalk widths were halved and streets were converted into six lanes specifically for the automobile, the street became, in the words of Richard Sennet,

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‘a meaningless place’ (Sennett 1977). This shift in the city’s physical infrastructure was highlighted in 1946 when the state approved plans for a radical alteration to the city with the goal of reducing traffic congestion. The plan—known as Lomex and led by urban planner Robert Moses—involved cutting through and demolishing huge portions of neighbourhoods such as SoHo and Little Italy with a ten-lane, doubledecked roadway. Intended to connect Long Island to New Jersey, this intervention would only be accessible to private cars, vans, and public transit buses, and would serve an estimated 120,000 vehicles daily. The construction would cost upwards of $72 million and displace 2000 families from their homes, as well as nearly 800 local businesses (Flint 2011) (Fig. 7.5). The Lomex plans were not the only plans proposed by Moses that involved demolishing infrastructure and displacing residents in NYC. In 1952, he had proposed a four-lane roadway through Washington Square Park linking Fifth Avenue to West Broadway. It would have split the park into two sections and included a raised walkway over the roadway connecting the two sides. This led to opposition from local residents, which resulted in the new construction and ultimately, removing current traffic from the park entirely. Several protests were mobilised against the new developments, led by Jane Jacobs. Although not trained as an urban planner, Jacobs started her career as a freelance writer on urban development issues within cities for the Architectural Forum where she began to make a name for herself in the urban planning field. Her urban planning philosophy differed from Moses, as she

Fig. 7.5 Artist’s visual of Robert Moses’ proposed ten-lane, double-decked Lomex plans. The plans saw pushback from these communities led by Jane Jacobs (Christin and Balez 2018)

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focused on street life, human-centric cities and less on the car and infrastructurecentric cities by preserving the aspects of neighbourhood culture such as distinct, walkable, mixed-use spaces and preserving historic buildings (Scheper 2008). Moreover, she was an activist who frequently championed and led protests against the top-down approach and new urban developments as she aimed to promote vibrant street life bringing together communities in public space creating various opportunities for social interaction (Dory 2017). On the contrary, Moses—an urban planner by training at Yale, Oxford and Columbia universities—focused on the automobile and the redevelopment of infrastructure with a top-down philosophy. He focused on transforming the physical city to accommodate the rising number of private vehicles with wide roadways and newly constructed bridges. To do this, he envisioned the demolition of several neighbourhoods and communities for the new infrastructure and various residential towers (Flint 2011). As Moses worked in the NYCs urban planning department, he frequently clashed with Jacobs as she led protests against his planned developments within the city. Since 2007, it seemed Jacob’s philosophy of human-centric cities was coming to fruition in NYC with a new initiative to bring more extensive options for entertainment and relaxation for walkers. Bike-sharing, parklets and streets being pedestrianised became common throughout the city (Costa 2020). New pedestrian plazas were being constructed around the city as automobile-free spaces, which brought new life to places such as Madison Square, Times Square and Herald Square. This philosophy affected the sidewalks, as they were being redeveloped to widen, providing space for chairs so that cafes and restaurants could expand their capacity, which brought people and vibrancy to the streets (Gehl 2010). The benefits of a humancentric philosophy was acknowledged by the city as an essential role in the urban planning development of the city, along with reducing traffic and building a sustainable culture. Although Jacobs won countless battles with Moses, the streets of NYC today are still the most congested in the United States and third in the world behind Bogota and Bucharest (Inrix Research 2020), with a tremendous amount of time wasted by traffic congestion. The city is still dominated by the automobile and specifically taxi services, which serve as a vital part of the urban transportation network. Although the city officials have acknowledged the importance of human-centric cities, the automobile and its value have led to limitations to how much transformation could happen (Mattioli et al. 2020). However, with recent software and hardware advances with AI and AV, we may be approaching a radical solution to reduce urban mobility issues that we have not achieved thus far. Now, cities are data factors, generating billions of data points through sensor technology, providing the opportunity to design more efficient urban mobility systems (Ratti and Claudel 2016). Cities are releasing some of this data collected by implementing open-data platforms providing access to various datasets. In 2015, the current Mayor of NYC, Mike Bloomberg, made a dataset with all the 170 million taxi trips accessible for every taxi drop off and pick up to improve the cities’ urban mobility challenges (Nyc.gov 2018).

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Fig. 7.6 Data visualisation from the minimum fleet project showing the old taxi network (yellow), and the 40% reduced taxi network (blue) in Manhattan (Torre 2018)

In this context, the Minimum Fleet4 project asks the question, what is the minimum number of cars needed to serve the mobility matter with no additional delay in New York City. By applying a mathematical framework, the Minimum Fleet model constructs more optimised trip assignments that decrease the vacant time between trips without a rider on board. The Minimum Fleet Network model can decrease vacant travel times, reducing the number of taxis needed to serve New York City by 40% compared to the present situation (Vazifeh et al. 2018). The project seeks to resolve the puzzle of finding the optimal size—the minimum fleet problem— provided a certain level of demand for individual mobility. Therefore, a taxi can serve more passengers within the same timeframe at their desired place and time, and the overall fleet size is minimized (Fig. 7.6). The findings from this research provide a platform for taxi operations to function in real-time based on demand; as AV develops and open-based mobility becomes a 4

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reality in urban environments, the mathematical frameworks here can take advantage of a fleet of connected systems to become more relevant. Moreover, it is predicted that a shift from private car ownership will shift into shared mobility, which has begun to see a rise with sharing economy platforms such as Uber, Lyft and Airbnb, and it predicted that every shared vehicle could remove up to 15 privately owned vehicles (Transport and Environment 2017). The shift towards sharing mobility, combined with self-driving cars, should blur the distinction between privately owned cars and car-sharing and should afford the potential to reduce urban mobility related emissions and play a significant role in reducing air pollution in cities. For example, a single car could be shared between a family, friends, neighbourhood or even a city: taking someone to school in the morning then providing transport for elderly relatives during the day and picking someone up from work in the evening. Since various people would occupy a single vehicle, fewer cars would be required, and this, in turn, would generate less congestion on the streets and a reduced environmental impact. Currently, cars are parked 95% of their time, and a single car typically takes up two car parking spaces: one at work and another at home. A result of this has meant parking infrastructure has become a dominant feature throughout cities. The project Unparking5 aims to understand and quantify parking infrastructure demand if cities have fully autonomous shared mobility. The project measures two scenarios using data from Singapore: the current state where a car uses two car parking spaces with one at work and one at home and a state when cars are fully shared with AVs. The results from the research found that 86% could be made available in Singapore’s parking infrastructure as spaces will become vacant (Kondor et al. 2020). Consequently, precious parking infrastructure space in cities will be repurposed into spaces that could be used for various applications. These changes have started to happen already in cities such as Seattle, Chicago, and Hamburg, as they celebrate the annual international parking day by transforming car parking infrastructure into areas for relaxation and various one-day public spaces for citizens (Southworth 2013). The concept allows to raise awareness and to employ civic participation to test new ideas for reusing space in their cities in a bottom-up approach. Although, as the Unparking results, these radical urban infrastructural transformations can happen on a much larger and permanent scale. This section outlines the impact urban mobility can have on physical space, communities and residents. The projects employ data-driven dispatching showing how NYC can use open-based mobility systems to improve the social and economic life of the city. As the adoption of AVs becomes a closer reality, cities can see a shift in urban mobility to a real-time responsive automobile system. The developments of AVs and AI provide a platform for NYC to move beyond a car-centric city and give the streets opportunities to return to vibrant spaces. Implementing open-based mobility in cities could also serve Jane Jacobs urban planning philosophy towards human-centric design through the generation of constant feedback loops.

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7.4 Conclusion This chapter has attempted to shed light on how technological developments in hardware and software and its potential benefits to alleviate global urban mobility challenges and transform the physical space in cities. The benefits of autonomy in cities move far beyond allowing people to move around more efficiently but can dramatically affect physical features in the built environment. In the section Amsterdam, Roboat and roundAround showcased the opportunity to use AV in waterways to create on-demand infrastructure and enable the city’s infrastructure to respond to human behaviour in real-time building open-based mobility. Minimum Fleet revealed in NYC that with advancements in computational tools combined with the rise of the autonomous vehicle and sharing mobility, we could optimise fleets of taxis with fleet communication and reduce the number of cars on streets. UnParking speculated further on sharing mobility and autonomous vehicles by measuring the impact on parking infrastructure. Reducing the number of private cars in cities and the time vehicles will be idle, the redundant car parking can be re-designed and re-proposed into social functions to create more vibrant cities. Although it seemed cities were upon an authoritarian style planning philosophy with the Jokinen Plan for Amsterdam to remove nine-tenths of the canals and the Lomex plan, led to the displacement of many local residents and businesses, the Jane Jacobs led social movements towards a human-centric philosophy has begun to make steps forward. Autonomous technology can aid in these steps providing a future of opportunities that can optimise mobility fleets, free the streets from traffic congestion, significantly reduce carbon emissions and provide mobility services for all. Despite the potential social, environmental, and economic benefits of introducing land or water based autonomous vehicles in urban environments, implementing such technology is more complicated than one may think. Citizens worldwide remain sceptical about the introduction of AI and AVs, in cities. As the smart city hype increases, one of the main aspects people remain concerned about is automation and robots replacing the need for humans in jobs. The relationship between machines/robots and human beings has been a consistent discussion between people since the industrial age. In 1942, Issac Asimov set three laws of robotics that were created to defend humans from interactions with robots (Asimov 1942). The laws involved legislation that the robots do not injure a human being or that the robot must obey human orders. When Asimov introduced these laws, he saw a future of robots more as androids, and they would act as slaves to humans. However, since the publication, significant technological advancements have occurred and it may help to revisit these laws in line with developments such as the AV. The projects in this chapter showcase how we might be dealing with robots, machines and AI on a more consistent basis in the near future. In this case, it is necessary to consider and define their social and political identities and understand deeper their civic status and responsibilities. Questions will need to be answered

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as we develop with and deal with machines; for instance, who owns robots? Who controls the AI? and who is responsible if a robot hurts someone? However, predicting human/ robot interaction, and more specifically, the future of cities and urban mobility, is challenging, as was found in the En L’an 2000 image series. Instead, we should focus on human-centric design and a collaborative and step-by-step approach that requires continuous feedback loops between city officials and the requirements and aspirations of residents. Using a human-centric design approach, the future planning of urban mobility can become crowdsourced—planners producing plans and receiving feedback from the public in debates on topics such as the implementation of AVs. By bringing in more voices into planning the future of urban mobility, we hope this will move cities and civilisation into the most desirable result.

References Anenberg SC, Miller J, Minjares R, Du L, Henze DK, Lacey F, Malley CS, Emberson L, Franco V, Klimont Z, Heyes C (2017) Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets. Nature 545(7655):467–471 Asimov I (1942) Runaround. Astounding science fiction Asimov I (1988) The best science fiction of Isaac Asimov. NAL, New York, N.Y. Batty M (1997) The computable city. Int Plan Stud 2(2):155–173 Ben-Joseph E (2012) Rethinking a lot—the design and culture of parking Benson T, Duarte F (2020) Senseable Cities. Nationellt möte 2020 Tillämpad stadsbyggnad Kris och transformation Birmingham City Council (2020) Birmingham transport plan. [online] Birmingham City Council. file:///C:/Users/tomjb/Downloads/Draft_Birmingham_Transport_Plan.pdf BLDGBLOG (2021) Sim city: an interview with Stone Librande. [online] https://www.bldgblog. com/2013/05/sim-city-an-interview-with-stone-librande/. Accessed 15 June 2021 Brooke L (2008) Ford model T: the car that put the world on wheels. Motorbooks, St. Paul City of Amsterdam (2019) Number of available parking permits down. [online] Township Amsterdam. https://www.amsterdam.nl/nieuwsarchief/persberichten/2019/persberichtensharon-dijksma/aantal-beschikbare-parkeervergunningen Christin P, Balez O (2018) Robert Moses: the master builder of New York City. Nobrow, London, New York Corbusier L (1987) The city of tomorrow and its planning. Dover Publications Costa C, Lee S (2019) The evolution of urban spatial structure in Brasília: focusing on the role of urban development policies. Sustainability 11(2):553 Costa E (2020) Humane and sustainable smart cities Côté J (1899) A 19th-century vision of the year (2000). [online] Publicdomainreview.org. https://pub licdomainreview.org/collection/a-19th-century-vision-of-the-year-2000. Accessed 21 Jan 2022 Cox P, Koglin T (2021) The politics of cycling infrastructure: spaces and (in)equality. Policy Press Dai T, Hein C, Zhang T (2019) Understanding how Amsterdam City tourism marketing addresses cruise tourists’ motivations regarding culture. Tourism Manage Perspect 29:157–165 Dory J (2017) Clash of urban philosophies. J Plan Hist 17(1):20–41 Duarte F, Ratti C (2018) The impact of autonomous vehicles on cities: a review. J Urban Technol 25(4):3–18 Duarte F, Johnsen L, Ratti C (2020) Reimagining urban infrastructure through design and experimentation. In: Willis K, Aurigi A (Eds) The Routledge companion to smart cities. Routledge

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Flint A (2011) Wrestling with Moses: how Jane Jacobs took on New York’s master builder and transformed the American city. Random House Trade Paperbacks, New York Gast T, Atasoy B, Duarte F, Rus D (2019) An adaptive large neighbourhood search for real-time waste collection inventory routing with autonomous vessels. White paper Gehl J (2010) Cities for people. Island Press, Washington, DC Inrix Research (2020) INRIX 2020 traffic scorecard report. [online] Inrix. https://inrix.com/scorec ard/ James S (2019) What happened when the Netherlands imported American road engineers? A true story. [online] Sandy James Planner. https://sandyjamesplanner.wordpress.com/2019/ 12/19/what-happened-when-the-netherlands-imported-american-road-engineers-a-true-story/. Accessed 21 Jan 2022 Kondor D, Santi P, Le D, Zhang X, Millard-Ball A, Ratti C (2020) Addressing the “minimum parking” problem for on-demand mobility. Sci Rep 10(1) Leoni P, Johnson L, Duarte F, Ratti C (2021) roundAround. [online] https://senseable.mit.edu/rou ndaround/. Accessed 15 June 2021 Mateos L, Wang W, Gheneti B, Duarte F, Ratti C, Rus D (2019) Autonomous latching system for robotic boats. In: IEEE international conference on robotics and automation (ICRA) Mattioli G, Roberts C, Steinberger J, Brown A (2020) The political economy of car dependence: a systems of provision approach. Energ Res Soc Sci 66:101486 Newman PW, Kenworthy JR (1996) The land use—transport connection. Land Use Policy 13(1):1– 22 Norton P (2008) Fighting traffic. MIT Press, Cambridge, Mass Nyc.gov (2018) About TLC—TLC. [online] https://www1.nyc.gov/site/tlc/about/tlc-trip-recorddata.page Park S, Kayacan E, Ratti C, Rus D (2019) Coordinated control of a reconfigurable multi-vessel platform: robust control approach. In: International conference on robotics and automation (ICRA), pp 4633–4639. https://doi.org/10.1109/ICRA.2019.8794075 Park S, Cap M, Alonso-Mora J, Ratti C, Rus D (2021) Social trajectory planning for urban autonomous surface vessels. IEEE Trans Rob 37(2):452–465 Ratti C, Claudel M (2016) The city of tomorrow. Yale University Press, New Haven Scheper GL (2008) A divergence of modernities: Jane Jacobs, Robert Moses, and the re-visioning of New York City. In: Community college humanities review Sennett R (1977) The fall of public man. Knopf, New York Silva RA, Adelman Z, Fry MM, West JJ (2016) The impact of individual anthropogenic emissions sectors on the global burden of human mortality due to ambient air pollution. Environ Health Perspect 124(11):1776–1784 Southworth M (2013) Public life, public space, and the changing art of city design. J Urban Des 19(1):37–40 Torre ID, Zhang S, Duarte F, Ratti C (2018) Minimum fleet. [online] https://senseable.mit.edu/Min imumFleet/. Accessed 15 June 2021 Transport and Environment (2017) Does sharing cars really reduce car use? [online] Transport and Environment. https://www.transportenvironment.org/sites/te/files/publications/Doessharing-cars-really-reduce-car-use-June%202017.pdf Urry J (2004) The ‘System’ of automobility. Theory Cult Soc 21(4–5):25–39 Vazifeh MM, Santi P, Resta G et al (2018) Addressing the minimum fleet problem in on-demand urban mobility. Nature 557:534–538

Tom Benson is a Research and Business manager at the MIT Senseable City Lab. Currently based in Amsterdam, he is interested in analysing the built environment for improving people’s access to social and economic opportunities. He is a member of the Urban AI community and has previously worked for the British Army, Buro Ole Scheeren, and Foster + Partners.

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Fábio Duarte is a Principal Research Scientist in the MIT Senseable City Lab, and lecturer in transportation and planning at MIT’s Department of Urban Studies and Planning. His book Urban Play: Make-Believe, Technology and Design, was launched in 2021, by MIT Press. Carlo Ratti is a Professor at MIT, Director of the Senseable City Lab, and found-ing partner of international design office Carlo Ratti Associati. He is a member of the World Economic Forum Global Future Council on Cities and special adviser on Urban Innovation to the European Commission. Ratti earned his M.Phil. and Ph.D. from the University of Cambridge, UK.

Chapter 8

Urban Microclimate Spatiotemporal Mapping: A Method to Evaluate Thermal Comfort Availability in Urban Ecosystems Daniele Santucci Abstract Attractive urban space is fundamental for creating safe and healthy cities. In the context of the climate crisis, microclimate becomes a determining factor for the use of public space in pursuing a just and equal society. Shifting to non-motorized modes of individual transport has manifold effects on the quality of urban environments in terms of safety, health, and spatial justice. Considering the need for quantifying microclimatic conditions in urban space, this chapter presents a methodology applied to a case study in the Boston Back Bay Area that develops a factor to indicate spatiotemporal outdoor comfort availability. The factor is based on a simulation workflow that generates datasets and maps, to be employed to quantify outdoor comfort availability at the pedestrian level with a high spatiotemporal resolution in adaptive spatial domains. The maps can be employed to compare different scenarios and neighbourhoods, and can serve as a base to put into evidence the influence of comfort and to formulate indications to increase outdoor thermal comfort in urban ecosystems. For promoting a tangible improvement of the city, pleasant environmental conditions are fundamental to accommodate pedestrian flows and to facilitate the implementation of social justice and public health. Keywords Urban microclimate · Urban morphology · Urban mobility · Human health and well-being · Big data analytics

8.1 Introduction Creating vibrant urban spaces is one of the most demanding tasks for architects, urban planners, and administrators: attractive, liveable and salubrious urban space is the result of a huge array of parameters that converge and intersect inducing an immense complexity that has been increasingly investigated in the past centuries of urban and health studies (Mumford 1961; Jacobs 1961; Whyte 1980; Galea and Vlahov 2006; Gehl 2011). This work focuses on defining a method to quantify D. Santucci (B) Climateflux, Lindwurmstrasse 11, 80337 Munich, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_8

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microclimatic conditions in urban space, setting the base to understand the influences of the environmental conditions on human activity and people’s use of outdoor space as well as to the implications of creating more liveable public spaces. Moreover, the most urgent issues of our time, as declared by the United Nations with the formulation of the Sustainable Development Goals, impose to frame the significance of the quality of public space: from climate change’s radical alteration of architecture’s basics to the deep transformations that data science and technology are causing, the built environment is essential to every challenge and opportunity our planet is facing (United Nations, SDG). The impacts of climate change result in a high amount of challenges for public space, manifested in urban systems in a variety of direct and subtle ways, by more extreme and frequent weather events, posing severe health hazards for urban populations, in particular to some of the poorest and most vulnerable and placing extreme stress causing serious impacts on the everyday lives and well-being of hundreds of millions of people around the globe (Hasan et al. 2013). In his book, The City and the Coming Climate, Brian Stone states: “the world’s largest cities are warming much more rapidly than the planet as a whole” (Stone 2012). Cities develop in relation to urban society, cultural characteristics and local climate (Hebbert and Jankovic 2013). Fostered by these relations, public space represents the place for social inclusion, diversity, environmental justice and equal access, throwing in sharp relief the sui generis relation between its use and its environmental qualities, since it is co-shaped by environmental and socio-technical conditions.

8.1.1 Outdoor Thermal Comfort Outdoor thermal comfort in an urban environment is a complex issue with multiple layers of concern. The environmental stimulus (i.e., the local microclimatic condition) is the most important factor affecting the thermal sensations and comfort assessments of people. Thus, people’s sensation of thermal comfort is greatly affected by the local microclimate, particularly pedestrians are directly exposed to their immediate environment in terms of variations of sun and shade, changes in wind speed and other climatic characteristics. Analysing and assessing these conditions demands innovative computational methodologies and metrics. Urban climatic studies are often conducted at three scales: mesoscale, local scale and microscale in descending order (Oke 1997). However, mapping the spatial distribution of meteorological parameters at the microscale is even more important because urban microclimate mapping reveals how microclimate behaves within urban districts and between buildings (Oke 1988; Erell et al. 2011), which contributes to a better understanding of its impacts on the outdoor thermal environment (Kántor and Unger 2010). Mapping microclimatic spatial variations provides useful information for public health research and management, identifying the hotspots of thermal discomfort so that individual thermal impacts can be

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evaluated. Increasing efforts are being made to generate spatially continuous data that is essential to explore the intra-urban variations of microclimatic conditions. However, acquiring these data through large-scale field measurements and long-term monitoring is time-consuming and expensive (Oke 2004; Li and Heap 2008). In their synesthetic mode of capturing, humans are unable to sense each individual meteorological quantity. Indeed, they feel the thermal effect of their environment caused by several meteorological parameters integrally through the skin and the blood temperature in the thermoregulatory system of the hypothalamus (Höppe 1993; Tromp 1980 in Fröhlich and Matzarakis 2020). Therefore, thermal comfort cannot fully be described by individual parameters but needs to be approximated through thermal comfort indices considering all relevant conditions. The more sophisticated indices are based on the approach of equivalent temperatures and are relying on the evaluation of the human energy balance or heat flux models (e.g., Błazejczyk et al. 2012; Fanger 1970; Gagge et al. 1936; Höppe 1993).

8.1.2 Mapping Urban Microclimate Developing this line of research, the present work develops a methodology to evaluate microclimatic conditions in a spatiotemporal way by providing a scalable metric that can be adapted to any kind of urban system. The metric attempts to give a synthetic value that can be used to reshape urban space and for enhancing the quality of public space to counteract extreme microclimatic conditions in cities. This research in fact develops a metric that allows to verify and to quantify microclimatic quality in urban space. To do so, it generates and tests a methodology employing a specifically developed workflow to create evidence from meteorological data, urban geometry, material and georeferenced data with the primary aim of addressing urban planning solutions that underscore a more human-centered urban environment and promote urban health, well-being and life quality. One of the most essential impacts of this research is on human mobility, since it attempts to measure and understand the effects of urban form, materiality and vegetation on microclimate: these characteristics are crucial to facilitate walking and to increase accessibility without the need for individual motorized mobility. Walking as an everyday practice has an enormous impact on health, wellbeing and carbon emissions as well as societal implications on practices of inclusion and cohesion.

8.2 Methodology The present research develops a novel workflow that captures spatial and temporal variations of microclimate and connects them. Its aim is to provide meaningful results that can be used to confront different scenarios or variations across urban spatial domains.

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So far, the existing body of research has used several ways to map the spatial distribution of meteorological parameters and obtain continuous data at different spatial scales, including scale model experiments such as wind tunnel tests and remote sensing-based methods or numerical modelling, such as computational fluid dynamic simulation, and geographical mapping. This research employs UTCI as a biometeorological index to evaluate microclimates. UTCI was developed conceptually as an equivalent temperature for a person with a constant metabolic rate of 2.3 MET walking at 4 km per hour (Bröde et al. 2012). For any combination of air temperature, wind, radiation and humidity, UTCI is defined as the air temperature in the reference condition which would elicit the same dynamic response of the physiological model. It is based on a 187 node model (Fiala et al. 2012; Kampmann et al. 2011) of thermal regulation and a dynamic clothing model that imitates human behaviour based on air temperature input (Havenith et al. 2012). UTCI was conceptually developed (www.utci.org) as an equivalent temperature allowing for the interpretation of the index values on a familiar scale with unit °C. For any combination of air temperature, wind speed, radiation and humidity, the UTCI values were further categorised into ten categories of thermal stress ranging from “extreme cold stress” to “extreme heat stress” (Bröde et al. 2012), giving significance to the whole range of heat exchange conditions of thermal environments in all climates, seasons and scales (Jendritzky et al. 2008). It assesses the outdoor thermal environment for biometeorological applications by simulating the dynamic physiological response with a model of human thermoregulation coupled with a clothing model (Blazejczyk et al. 2012). The UTCI has been already employed and tested in our past and current research activity and has demonstrated to be the most reliable and accurate metric for evaluating outdoor comfort so far (Reinhart et al. 2017).

8.2.1 Microclimatic Modeling As the input for the microclimatic model the author employed the weather data collected from the Weather Underground historic data database. The dry bulb temperature (DBT) and relative humidity (RH) hourly values were directly taken from the weather data, while the wind speed at the pedestrian level and the Mean Radiant Temperature (MRT) required additional separate modelling techniques. Research on wind speed modelling around buildings has generated a large body of methods and theories. Their applications are manifold since wind speed affects not only comfort and people’s health but also the energy consumption of buildings, particularly in tropical climates. The author excluded from the beginning to employ wind tunnel experiments, that require large detailed physical models and accurate measures on the model, the employed method bases on a simplified CFD simulation. Large Eddy Simulation (LES) is an alternative strategy for modelling fluid-flow behaviour in which

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time-dependent predictions are computed. In this case, the relevant measure is the wind speed at the pedestrian level, which is influenced by the building geometry and orientation to the wind direction and the roughness of the urban canopy layer. Understanding the urban canopy flow has generated a large body of research since it has particular relevance to the issues related to air pollution (and abatement strategies), energy usage in cities and pedestrian comfort at the neighbourhood scale (Height 100 m to 1 km) (Hamlyn and Britter 2005). The employed simulation model was recently developed by Patrick Kastner and Timur Dogan at Cornell University, who created a simplified method to incorporate ventilation analysis into early design stages (Kastner and Dogan 2019). Complex urban environments models require computationally extensive Fluid Dynamics (CFD) analysis. The new method is streamlined in order to reduce the time to produce actionable results and can be easily integrated in the proposed workflow, since it is compatible with the employed tools. CFD is a numerical methodology to calculate desired flow variables on a number of grid points within a simulation domain by solving discretised Navier–Stokes equations (NSE). The usual steps of a recurrent CFD analysis for an optimisation process for the built environment consist of: 1. 2. 3. 4.

Modelling the building geometry with CAD software; Meshing the building geometry and topography; Simulating the problem with appropriately assigned boundary conditions; Post-processing the variables of interest, likely followed by design alterations and referring back to the first step, based on the results obtained. (Kastner and Dogan 2019).

Besides the air velocity, the mean radiant temperature (MRT) is among the most important variables affecting human thermal comfort in an outdoor urban space (Lindbergh and Thorsson 2007). MRT is the uniform temperature of an imaginary enclosure in which radiant heat transfer from the human body is equal to those in the actual non-uniform enclosure (ISO 7726; 1998). It is the composite mean temperature of the body’s radiant environment. Compared with convection or evaporation, radiative energy exchange accounts for a large share of human heat transfer (Folk 1974) and is closely correlated with outdoor thermal comfort as well as pedestrian activities (Nikolopoulou and Lykoudisb 2006). For this reason, there is a practical need to model radiative heat transfer between human body and the built environment to assess the human body heat balance, which enables us to calculate UTCI (Huang et al. 2014). In open outdoor space conditions, the consideration of radiant heat exchange between the human body and its environment in general must take the following long-wave and short wave radiant fluxes into account (Kessling et al. 2013): 1. 2. 3. 4. 5.

thermal radiation from the ground and other surrounding surfaces thermal radiation into the atmosphere direct solar radiation diffuse solar radiation reflected solar radiation

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The influence of these radiant fluxes to the MRT of a human being has to be determined for every possible situation as it depends on the person’s exposure to surrounding surfaces and the sun (Kessling et al. 2013). The UTCI model employed in this study calculated an hourly averaged value for each grid point. However, the significance and the objective of this work is to map microclimate in a spatiotemporal resolution at the pedestrian level. Therefore, to reduce computation time, the author calculated the values for the surfaces corresponding to the sidewalks with a linear grid of 5 m. This step was fundamental to generate consistent adherent data to the relevant areas of public space that correspond to the places where people walk. This research design allows to depict microclimatic conditions with their variations on that occur at the street level, influenced by: 1. 2. 3.

varying radiation levels due to sky conditions, building geometry and material; presence of greeneries also according to seasonality; dynamic perception influenced by the moving subjects.

8.2.2 UTCI Data Fusion The novel metric has been developed to be employed in the applied workflow is called STOCA (Spatiotemporal Outdoor Thermal Comfort Availability) and is conceived as a measure for assessing outdoor comfort in a given space throughout a specific time stamp using the UTCI assessment. The calculation is defined as follows: t,end 1  OT C ST OC A = mp t,st

mp t,st t,end

measure points time, start time, end  OT C =

r

(8.2.1)

tr ue i f U T C I ∈ r f alse i f not

(8.2.2)

selected UTCI range

Compared to similar metrics (Nazarian et al. 2019) the proposed one presents the following additional advantages: 1.

First, it allows to match to an adaptive spatiotemporal domain. This property allows to evaluate outdoor thermal comfort considering temporal and spatial variables with respect to a maximum comfort availability or to the frequency

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of comfort conditions in the observed domain. Unlike previous metrics, the STOCA can refer to varying spatial and temporal domains enabling to identify comfortable conditions within specific seasonal scenarios and comparing proximal spatial domains. Second, it is defined basing on the concept of availability and it does not refer to annual ranges and schedules and is therefore fully adaptive including the specificity of seasonal climatic baselines and does not compensate comfort conditions on an annual basis.

With this granularity, thermal stress can be defined as a possible condition since the values that correspond to thermal comfort refer to a set of baseline climatic conditions. This metric allows to map spatiotemporal comfort within a range of microclimatic conditions that occur according to seasonality. In urban space, in fact, people move “through diverse and dynamic microclimates, causing them to experience spatial gradients and temporal transients in temperature as well as other climatic parameters including humidity, wind speed, and radiation “(Liu et al. 2020). Therefore, the STOCA metric defines a baseline condition that depicts spatiotemporal microclimatic conditions, opening up new possibilities for comparative and correlational research, as it will be further developed, allowing to evaluate the following parameters: 1. 2. 3.

confronting different design scenarios; variables introduced by seasonality; Quantifying and mapping the impacts of urban morphology on microclimate at the pedestrian level.

8.3 Application In order to verify the proposed methodology, the author applied it on a case study in the Boston Back Bay area.

8.3.1 Focus Area The focus area has been chosen in the context of other research activities performed by the author in the city of Boston: it offers a variety of functions, morphological characteristics and presence of greenery in a dense urban environment. As the largest city in Massachusetts, the city of Boston has a total population of about 692,000, according to the recent census data (American Census Estimates 2019 data, https://www.census.gov/quickfacts/bostoncitymassachusetts). Boston is located in the northeast of the United States, with a land area of 125 km2 , with

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Fig. 8.1 Focus area

spatially varied street canyon types ranging from skyscrapers in the downtown area and the low-lying residential area in the periphery. The Back Bay area in Boston is characterised by an orthogonal west–east oriented grid and by low and mid-rise buildings, built mainly in the second half of the nineteenth century (Fig. 8.1). Originally the buildings were purely residential, today the buildings host also offices and shops, especially in Newbury Street and Boylston Street. The focus area includes Commonwealth Avenue, Newbury Street and Boylston Street—three streets that present different widths, aspect ratios and characters. The segment of Commonwealth Avenue incorporates a linear park in its central part, the Commonwealth Avenue Mall, that links the Boston Common and Public Garden to the city’s great park system.

8.3.2 Microclimate Modeling The UTCI results were fed into the STOCA metric to quantify spatiotemporal microclimate patterns. The factor gives a measure of comfort availability on a given day at a specific time that makes different locations comparable. One of the most relevant choices was to choose a resolution that combines a high granularity with acceptable computing time. The focus area was modelled using the commercial NURBS 3D modeller Rhinoceros 6 consisting of the 24 blocks within the area of interest, including the blocks surrounding the area of interest for the purposes of CFD modelling.

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The author ran four directions CFD simulations to map the variations of airflow patterns within the site. Due to the prevailing wind directions occurring in the selected area, the author reduced the simulations to the four prevailing directions (N_E_S_W 0, 90, 180, 270). The resulting data provides a high resolution of wind direction patterns. The simulations were carried out with the Eddy3D software, a Grasshopper interface for the OpenFOAM Computational Fluid Dynamics (CFD), RANS based simulation engine. To model the mean radiant temperature, the author calculated the surface temperature of the buildings and the ground and the sky heat transfer. These calculations were performed using the EnergyPlus engine. The author used a solar distribution setting of FullExteriorWithReflections to ensure that a correct portion of solar energy was calculated for each urban surface on a 1 h time step. Each building was divided into zones with a 3 m floor height. For the external walls, the author used a 30 cm brick without insulation. In order to compute a mean radiant temperature (MRT) for the outdoor comfort model, a base long wave MRT was computed using the surface temperatures of the previous step and following formula (Thorsson et al. 2007):  M RT =

N 

1/4 Fi Ti

4

(8.2.3)

i=1

where F is the fraction of the spherical view occupied by a given indoor surface, T is the temperature of the surface. View factors (F) to each of the EnergyPlus surfaces were calculated using the ray-tracing capabilities of the Rhino 3D modelling engine. The long-wave temperature of the sky was estimated using the horizontal infrared radiation contained within the TMY data along with the following formula (Blazejczyk 1992): Tsky =

La (εerson σ )1/4

(8.2.4)

where L α is the downwelling long wave radiation from the sky in W/m, e is the emissivity of the human (assumed to be 0.95), and s is the Stefan-Boltzmann constant (5.667 × 10−8 ). To account for shortwave solar radiation that falls on people, the SolarCal model was used to produce an effective radiant field (ERF) and corresponding MRT delta that was added to the base longwave MRT (Arens et al. 2015). Published in the ASHRAE-55 standard for thermal comfort (2016), the SolarCal model offers advantages over other models to estimate shortwave radiation falling on people. Notably, it allows for inputs of seated vs. standing among other variables. The formula to calculate the ERF with SolarCal is as follows:     E R Fsolar = 0.5 f e f f f svv Idi f f + IT H R f loor + A p f bes Idir /A D (αSW/αL W ) (8.2.5)

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where f eff is the fractional of the body that can radiate heat (0.725 for a standing person), f svv is the sky view bfactor (computed here through ray-tracing) and f bes is a 1/0 value indicating whether direct sun is on the person (computed by tracing the sun vector). I diff is the diffuse sky radiation, I TH is the global horizontal radiation, and Idir is the direct radiation. Ap and AD are geometry coefficients of the human body, which are computed based on sun altitude and azimuth. Finally, Rfloor is the reflectivity of the ground (assumed to be 0.25) and the a values refer to the absorptivity and reflectivity of the person’s clothing. This ERF is converted into a MRT delta using the following equation: E R F = f eff h r (M RT − TL W )

(8.2.6)

where hr is the radiation heat transfer coefficient (W/m2 K) and T LW is the base longwave MRT temperature (o C) (Mackey et al. 2017). To reduce the simulation time and optimise the efforts to the scope of this work, the view factors (F) were chosen on the sidewalks’ centrelines, with an interval of 5 m. (Fig. 8.2).

Fig. 8.2 Simulation model

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8.3.3 Tree Modeling To model the linear park in Commonwealth Avenue, the author employed the newly developed tool PANDO. PANDO is a numerical process-based simulation tool that enables the user to model individual trees and canopies as a Rhino Grasshopper interface. This plugin simulates radiation fluxes in three different wavebands of photosynthetic (PAR), near infrared (NIR) and thermal (Chokhachian & Hiller 2020). The PANDO tool uses the MAESPA simulation engine and it imports files directly from the 3D Rhinoceros model. The tree geometries were generated according to the existing park array, using a 10 × 10 grid to model the four lines of American elms, with a trunk diameter of 0.5 m, a height of 5 m, and a crown height of 8 m. The author simulated the annual perceived temperatures with the Universal Thermal Climate Index (UTCI) for a shaded person under the tree (Blazejczyk 1992). The Mean Radiant Temperature (MRT) modelling integrates exposure to shortwave and longwave radiation in a three-dimensional environment assuming that the radiant heat transfer from the human body is equal to the radiant heat transfer in the actual non-uniform enclosure (Thorsson et al. 2007). To calculate MRT, the process is divided into shortwave fluxes (direct, diffuse and reflected solar radiation) and longwave radiation (urban surfaces, trees and sky) using the afore mentioned weather data. To be accurately represented, direct and diffused shortwave solar radiation is simulated using radiance raytracing with Daysim’s hourly irradiation method. For the longwave fluxes, the surface temperatures are estimated based on air temperature, wind velocity, net all wave radiation and Bowen ratio (Oke 2002). The sky temperature is calculated based on the method developed by Martin and Berdahl (Martin and Berdahl 1984), taking into account the emissivity model introduced by Duffie, Beckman and Worek (Duffie et al. 2013). The author used PANDO to model the leaf surface temperature and the shading effect according to a seasonal phenology model. The average leaf temperature of the tree canopy is extracted from the PANDO model for every hour. Having radiant fluxes from the adjacent surfaces based on the view factors from the body position, the MRT component is calculated for each hour of the year based on the Stefan– Boltzman law (Matzarakis et al. 2010; Chokhachian and Hiller 2020). Combining the annual hourly MRT values with local wind speed, air temperature and humidity gathered from the epw file, the perceived temperatures expressed with the UTCI were calculated for a point aligned to the park’s central path, assuming no variations along the path (Fig. 8.3). To obtain a full yearly dataset, the author calculated UTCI values (for all 8760 h composing a year) for the view factors points in a 5 m interval, for each sidewalk of each street. This choice allowed to reduce the simulation efforts and generates only the relevant data, corresponding to the areas where people actually walk. The outcome of the simulated data is contingent and represents outdoor thermal comfort conditions with a high resolution.

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Fig. 8.3 Tree model

8.4 Results In order to get a spatiotemporal mapping of comfort conditions, the hourly results calculated for each sidewalk were fused into the STOCA factor. The factor allows to analyse comfort potential according to the overall climatic conditions and seasonal variations depicting very specific conditions and compare them in a high resolution, such as different sides of a street canyon or different streets within a neighborhood. The selected periods consist each of 14 days corresponding to the four seasons that were defined according a previous detailed climate analysis: the winter period was defined considering the days from February 9 to 22, the spring period from April 13 to 26, the summer period from September 1 to 14, the autumn period from November 3 to 16. The winter and summer periods include respectively the lowest and highest air temperature. To calculate the STOCA factor, the author chose the comfort ranges according to the different seasonal scenarios: besides the “no thermal stress category” (from 9 to 26 deg ET) which is valid for all seasons, for the winter scenario, the author included the “light cold stress” category (0–9 deg ET), in summer, the “light heat stress” (26–32 deg ET), according to the definitions provided by the UTCI scale. This decision was made to include the acclimatisation component in summer and winter when the “no thermal stress” conditions are rarely reached. The STOCA factor includes sun exposure allowing to draw clear distinctions between sides of the street with different orientations and between one street and the other. In this sense, it allows to characterise comfort in a defined spatial domain referring to the maximum available comfort within given climatic conditions. The factor is expressed in a value between 0 and 1 and is calculated for each hour in a

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14 days’ time interval representing each season. It was calculated separately for each sidewalk of the three streets, as shown in Fig. 8.4. The spatiotemporal comfort maps represent the hourly comfort availability in the selected spatial domain expressed as STOCA factor. Figure 8.5 shows an exemplary plot: the y-axis maps the 24 h values (from 12 am to 11 pm), the x-axis the 14 days’ period. Each pixel corresponds to a factor value (between 0 and 1) for the entire sidewalk length, where 0 means no availability and 1 means full availability within the selected spatial domain. The maps (Figs. 8.6, 8.7, 8.8, 8.9 and 8.10) represent the comfort availability, calculated using the STOCA factor, for each of the selected periods and for each sidewalk. The juxtaposition highlights similarities and differences between streets, between the different sides of the street and between the seasons. In the winter season (Fig. 8.6) we observe a very similar cxpattern between the street sides that is reflected also when comparing the different streets. Only the central sidewalk in the park has a radically different pattern. The factor was generated considering, in addition to the “no thermal stress” UTCI classification, the “light cold stress” category (0–9 deg ET). Nevertheless, the winter has a high number of hours

Fig. 8.4 Spatial and temporal schedule

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Fig. 8.5 Exemplary microclimatic map representing the STOCA factor

Fig. 8.6 Microclimatic Map (STOCA)—winter

without any comfort availability throughout all the streets. As represented in the Delta maps, the difference between the sides of the streets has a rather minor impact, while the south sidewalk in Commonwealth Avenue has a higher availability than the park.

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Fig. 8.7 Microclimatic map (STOCA)—spring

In Spring (7) we observe a similar trend. In this case, the factor was generated considering only the “no thermal stress” UTCI classification (9–26 deg ET). Compared to the winter, we observe a very high comfort availability on all streets and sidewalks. The exception is, as already seen in winter, the central sidewalk of Commonwealth Avenue, that still provides comfortable conditions occasionally during daytime. The delta map, in fact, highlights the strongest difference in Commonwealth Avenue. During the summer period, the effect of the linear park becomes evident. The factor for the summer season was generated considering, in a first step, only the “no thermal stress” UTCI classification (Fig. 8.8). The centre sidewalk of Commonwealth Avenue has by far the highest comfort availability, while all other segments have similar patterns, with a lower availability in Commonwealth Avenue on the north and south sidewalks. In the central sidewalk, we see four days without any comfort availability that correspond to the hottest days of the year and two nights at the end of the selected period with light cold stress. In a second step, the “moderate heat stress” category (26–32 deg ET) was added. In this case (Fig. 8.9) we observe the highest comfort availability throughout the area, with a conspicuous effect of the linear park: out of the 336 h that compose the period, the central sidewalk has only 18 h with

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Fig. 8.8 Microclimatic map (STOCA)—summer

light cold stress during the night hours, while the other guarantee full comfortable conditions. Similar to spring, autumn (Fig. 8.10) was evaluated considering only the “no thermal stress” UTCI classification (9–26 deg ET) and presents a similar pattern. The discomfort occurrence can be caused both by moderate heat stress as well as by light cold stress, a specific characteristics of Boston’s varying weather conditions. Also in this case we recognise the reoccurring patterns with a lower comfort availability in the central Commonwealth. Avenue. Summarising, the two more evident tendencies are visible in Commonwealth Avenue and Boylston Street: comparing the trends for each street separately, we notice a high fluctuation on comfort conditions in Commonwealth Avenue, comparing the south and central sidewalk, while in Boylston Street we see the lowest differences between one side and the other. This effects can be assigned to the aspect ratio of the streets: Commonwealth has the lowest (0,25) exposing the central path to the highest extent, while Boylston has the highest (0,62), reducing the exposure to the maximum. The higher aspect ratio that corresponds to the lowest SVF reduces the differences between the sides because it reduces solar access. However, the presence of the wide central strip in Commonwealth Avenue that hosts the park increases

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Fig. 8.9 Microclimatic map (STOCA)—summer extended

comfort in summer when the foliage guarantees shading and evaporative cooling but reduces it in winter and spring when the higher exposure generates discomfort. The influence of the linear park is particularly interesting because it creates a high difference in comfort availability within a few meters in the dense urban tissue, requiring additional space.

8.5 Conclusion and Application to Practice The influence of climate on urban practices and on the use of public space has already generated a wide body of research. However, the pressure that climate change is putting on urban environments intensifies the urgency to provide precise actions underpinned by evidence. Trying to characterise the intrinsic microclimatic character of each place, the STOCA factor allows to describe the conditions that occur in the city with a high spatiotemporal resolution: this achievement allows to compare microclimatic

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Fig. 8.10 Microclimatic map (STOCA)—autumn

conditions in the way they are shaped d by urban morphology, materiality and vegetation. As many cities across the world will most likely experience a decline in the quality of their thermal environment due to increasingly extreme weather conditions as an effect of climate change, quantifying the thermal quality of the built environment becomes a crucial step to formulate mitigation and adaptation strategies. In fact, linking urban design to public health is fundamental for the spatial reconfiguration of the practices of daily life and to address the most urgent topics of our time.

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Bröde P, Fiala D, Blazejczyk K, Holmér I, Jendritzky G, Kampmann B, Tinz B, Havenith G (2012) Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int J Biometeorol 56:481–494 Chokhachian A, Hiller M (2020) PANDO: Parametric tool for simulating soil-plant atmosphere of tree canopies in grasshopper. In SIMAUD 2020 conference proceedings, Vienna. Duffie JA, Beckman WA, Worek W (2013) Solar engineering of thermal processes, vol.3. Wiley Online Library. Erell E, Pearlmutter D, Williamson T (2011). Urban microclimate. Design the spaces between buildings. Earthscan, London–Washington, DC Fanger P (1970) Thermal comfort. Danish Technical Press, Copenhagen Fiala D, Havenith G, Bröde P, Kampmann B, & Jendritzky G (2012) UTCI-Fiala multi-node model of human heat transfer and temperature regulation. Int J Biometeorol 56(3):429–441. Folk G (1974) Texbook of environmental physiology. Lea & Febiger, Philadelphia Fröhlich D, Matzarakis A (2020). Calculating human thermal comfort and thermal stress in the PALM model system 6.0. Geosci Model Dev 13(7):3055–3065 Gagge AP (1936) The linearity criterion as applied to partitional calorimetry. Am J Physiol 116:656– 668 Galea S, Vlahov D (Eds) (2006) Handbook of urban health: populations, methods, and practice. Springer Science & Business Media. Urban Health Gehl J (2011) Life between buildings: using public space. Island Press, Washington, DC Hamlyn D, Britter R (2005) A numerical study of the flow field and exchange processes within a canopy of urban-type roughness. Atmos Environ 39:3243–3254 Hasan S, Schneider CM, Ukkusuri SV, Gonzalez MC (2013) Spatiotemporal patterns of urban human mobility. In: Springer Science + Business Media; J Stat Phys 151(1):304–318, 2012, New York Havenith G, Fiala D, Błazejczyk K, Mß R, Bröde P, Holmér I, Rintamaki H, Benshabat Y, Jendritzky G (2012) The UTCI-clothing model. Int J Biometeorol 56(3):461–470 Hebbert M, Jankovic V (2013) Cities and climate change: the precedents and why they matter. Urban Stud 50(7):1332–1347 Höppe P (1993) Heat balance modelling. Experientia 49(9):741–746 Huang J, Cedeño-Laurent JG, Spengler JD (2014) CityComfort+: A simulation-based method for predicting mean radiant temperature in dense urban areas. Build Environ 80:84–95 ISO (1998) ISO 7726: ergonomics of the thermal environment-instruments for measuring physical quantities. International Standard Organization, Geneva Jacobs J (1961) The death and life of Great American cities. Random House, New York Jendritzky G, Havenith G, Weihs P, Batschvarova E, DeDear R (2008) The universal thermal climate index UTCI goal and state of COST action 730. In: 18th international conference on biometeorology, Tokyo Kampmann B, Broede P, Jendritzky G, Fiala D, Havenith G (2011) The universal thermal climate index UTCI for assessing the outdoor thermal environment. In: 4th International conference on human-environment system, Japan Kántor N, Unger J (2010) Benefits and opportunities of adopting GIS in thermal comfort studies in resting places: an urban park as an example. Landsc Urban Plan 98(1):36–46 Kastner P, Dogan T (2019) A cylindrical meshing methodology for annual urban computational fluid dynamics simulations. J Build Perform Simul 13(1):59–68 Kessling W, Engelhardt M, Kiehlmann D (2013) The human bio-meteorological chart. In PLEA Li J, Heap AD (2008) A review of spatial interpolation methods for environmental scientists Liu S, Nazarian N, Niu J, Hart M, de Dear R (2020) From thermal sensation to thermal affect: a multi-dimensional semantic space to assess outdoor thermal comfort. Build Environ Mackey C, Galanos T, Norford L, Roudsari MS (2017) Wind, sun, surface temperature, and heat island: critical variables for high-resolution outdoor thermal comfort. In: Proceedings of the 15th international conference of building performance simulation association, San Francisco, USA

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Daniele Santucci (Ph.D. - Dr. Ing.) is a scientist, educator and advisor for decarbonisation strategies working at the intersection of research, practice and pedagogy. His investigations focus on climate, urban data ecosystems and design, employing environmental engineering, low carbon design, and computational work-flows targeted to achieve carbon neutrality in the built environment across all scales. He is deputy professor for Building Technology at the Faculty of Architecture at RWTH Aachen University. In 2017 and 2018 Daniele has been appointed visiting researcher at the Senseable City Lab at the Massachusetts Institute of Technology. In 2020 he cofounded Climateflux, a company that consults architecture firms, public institutions, and private companies on strategies and design solutions to increase outdoor comfort in urban space.

Chapter 9

Urban Ecosystems and Nature-Based Solutions: The Role of Data in Optimizing the Provision of Ecosystem Services Katia Perini Abstract Growing urban areas are facing significant challenges in terms of ecosystem health, human wellbeing, and environmental quality. Ecosystems are composed of biotic and abiotic components and their interactions. Healthy urban ecosystems can provide numerous so-called ecosystem services, which are benefits provided by nature to society and the economy. They are classified into provisioning services, regulating services, cultural services, and support services. As demonstrated by several studies and stated by the European Commission, nature-based solutions, depending on urban areas’ characteristics, can enhance the provision of relevant ecosystem services in cities. Therefore, nature-based solutions can be planned, designed, and integrated as a means to improve urban ecosystem health. However, a systematic approach to optimizing ecosystem service provision is frequently overlooked. Data collection and modeling allow for the evaluation of biotic and abiotic interactions, as well as the effects of anthropogenic activities and modifications. In this field, the chapter mainly focuses on the role of data in an integrated design process, aimed at the optimization of the performance of nature-based solutions to improve urban ecosystem health. Keywords Urban adaptation · Urban microclimate · Urban green infrastructure · Resilience · Sustainability

9.1 Introduction Growing urban areas are facing significant challenges in terms of ecosystem health, human wellbeing, and environmental quality (Benedict and McMahon 2001; Nelson et al. 2013; McDonald et al. 2020). Almost 75% of European citizens currently live in cities, a trend projected to increase and reach over 80% in the next decades (83.7% by 2050) (European Commission 2021).

K. Perini (B) Università Degli Studi Di Genova, Genova, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_9

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It is worth highlighting that several studies demonstrate the advantages and disadvantages of urbanization in a wide range of fields (Chokhachian et al. 2019), such as mobility (Churchman, 1999), land/resource use (Alexander et al. 1988), social equity and diversity (Tomalty and Komorowski 2010), the economy (Hitchcock et al. 1994), green space (Tratalos et al. 2007), morphology (Kamal-Chaoui and Robert 2009), and energy (Breheny 1992). For example, when it comes to land use, advantages include better resource utilization (protection of underdeveloped lands and habitats), while disadvantages include restrictions in terms of recreational opportunities and availability of public space (Alexander et al. 1988). The reduction of emissions from private transportation and building energy use should also be mentioned as one of the most important advantages (Churchman 1999; Hamin and Gurran 2009). Most urban areas face relevant issues, including poor air quality (European Environment Agency 2018), the urban heat island phenomenon due to the lack of natural areas/surfaces and building density (Oke 1982; Taha 1997), and rain water management issues (Ahem 1996; Alectia 2016). In the fourth MAES report (Maes et al. 2016a, b), the European Commission recognizes urban ecosystems as the type of socio-ecological system where most of the human population lives. Urban ecosystems are composed of green and built infrastructure and physical and biological components that interact with each other, such as industrial, commercial, and transport areas, urban green spaces, rivers, etc. Healthy urban ecosystems ensure a good living environment for citizens and urban biodiversity (Maes et al. 2016a, b). This implies, for example, good air and water quality, a high level of diversity of urban species, as well as a sustainable supply of regulating, provisioning, and cultural ecosystem services (Maes et al. 2016a, b). In this field, social, environmental, and economic aspects of urban environments and their relationships and interactions are fundamental (Interreg Europe 2020). Furthermore, it is important to acknowledge that ecosystem services can help achieve some of the UN Sustainable Development Goals (SDGs) (Wood et al. 2018). Nature-based solutions (NbS) are inspired and supported by nature; they provide environmental, social, and economic benefits and help build resilience (DirectorateGeneral for Research and Innovation, European Commission 2015). Nature-based solutions can significantly improve the environmental quality of dense urban areas by reducing the Urban Heat Island (UHI) effect (Onishi et al. 2010; Rizwan et al. 2008), improving air quality (Hirabayashi and Nowak 2016; Krüger et al. 2011) and the energy performance of buildings (Ascione et al. 2013; Coma et al. 2018), managing storm-water (Perini and Sabbion 2017), fostering biodiversity (Atkins 2018; Köhler and Ksiazek-Mikenas 2018; Mayrand et al. 2018), and improving wellbeing (Mavoa et al. 2019) as a result of climate change adaptation in resilient cities. As the presented case studies show, data collection, analysis, and modeling facilitate the evaluation of biotic and abiotic interactions, the effects of anthropogenic activities and modifications, and the performance of nature-based solutions. The aim of the chapter is to evaluate how data are used in recent studies and research projects focusing on urban ecosystem health and, more specifically, on ecosystem service provision for climate change adaptation and resilient urban environments.

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9.2 Methodology In order to evaluate the role of data in the optimization of ecosystem service provision by means of nature-based solutions, the chapter includes an overview of ecosystem services and their role in improving urban ecosystem health to achieve the UN Sustainable Development Goals (United Nations 2015). Case studies with a specific focus on urban areas, ecosystems (services), (in)direct relationships with nature-based solutions, urban greening, and green infrastructures are presented. Four of them are research papers selected by means of the Scopus database (search keywords: ecosystem, ecosystem services, and data). The other two were identified from among recent EU-funded research projects. All case studies focus on two or more of the ecosystem services identified by TEEB (2011) and are presented in the following section. A short overview of all case studies is presented, as well as a brief description of the methodology and use, and type of data. Finally, the ecosystem services considered in each case study for the different types of nature-based solutions are evaluated and discussed in relation to the use of data modeling, collection, and mapping in the different phases of the design process.

9.3 Ecosystem Services Healthy urban ecosystems, as described above, play a key role in fostering biodiversity and improving citizens’ quality of life (Maes et al. 2016a, b). A healthy ecosystem can be defined as a sustainable ecosystem able to maintain its structure (organization) and function (vigor) over time in the face of external stress (resilience) (Costanza and Mageau 1999). On the other hand, unhealthy urban ecosystems can cause environmental degradation, economic and social problems, disconnection from nature, etc. Urban ecosystem health relies on several challenges (Interreg Europe 2020), such as climate change adaptation, which is critical in dealing with the high pressure caused by the heat island effect, and noise pollution mitigation, which is necessary to reduce negative impacts on citizens’ wellbeing. Ecosystem services are classified into four categories: (1) provisioning services, such as the supply of food and other raw materials; (2) regulating services, e.g., mitigation of air pollution and climate regulation; (3) support services, such as biodiversity; and (4) cultural services, such as social relations, health, and wellbeing (Millennium Ecosystem Assessment [Program] 2005). According to TEEB (2011), the most relevant ecosystem services in the first category for urban areas include food, raw materials, fresh water, and medicinal resources. All regulating services are highly relevant in terms of climate change adaptation, mitigation, and urban resilience, such as: local climate and air quality regulation, carbon sequestration and

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storage, moderation of extreme events, waste-water treatment, and erosion prevention and maintenance of soil fertility. The most relevant support services for urban areas are related to pollination, biological control, habitats for species, and maintaining genetic diversity. Finally, among the cultural services, recreation and mental and physical health, tourism, aesthetic appreciation and inspiration for culture, art, and design, and spiritual experience and sense of place are listed. All the ecosystem services listed play a key role in improving urban areas’ ecological and environmental conditions and in achieving a wide range of UN Sustainable Development Goals (SDGs). Figure 9.1 outlines the role of ecosystem services in achieving the UN Sustainable Development Goals (SDGs). Through an expert survey, Wood et al. (2018) identified the contribution of ecosystem services for achieving 41 targets across 12 SDGs. The study shows possible cross-target interactions, claiming opportunities for synergistic outcomes across multiple SDGs. For example, local climate and air quality regulation support (strong perceived support according to the experts involved in the study) SDGs 3 (Good health and wellbeing), 11 (Sustainable cities and communities), and 13 (Climate action); moderation of extreme events supports SDGs 2 (Zero hunger), 6 (Clean water and sanitation), 13 (Climate action), and 14 (Life on land).

Fig. 9.1 Relation in terms of strong-perceived support between ecosystem services (classification according to TEEB 2011) and Sustainable Development Goals, based on (Wood et al. 2018)

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9.4 Nature-Based Solutions The role of nature-based solutions in protecting, managing, and restoring ecosystems is widely recognized by the European Commission (2015) and by the IUCN (2016). NbS addresses a range of relevant challenges for human wellbeing and biodiversity increase. Several funding opportunities increased interest in NbS, resulting in the development of guidelines, the construction of pilot projects, etc. (UNaLab 2017; “Urban GreenUP” 2017), such as the EU-Horizon 2020 Programme which, since 2014, has provided support to demonstration projects for the assessment and deployment of nature-based solutions, funding projects on ecosystem services, restoration, and NbS using green infrastructure (GI) to address societal challenges. Nature-based solutions provide a wide range of ecosystem services. Figure 9.2 illustrates the main ecosystem services provided by a selection of NbS; it represents an overview of the role played by built structures, such as green roofs, land media, trees, water media, and rain gardens.

Fig. 9.2 Ecosystem services provided by Nature-based Solutions, classified according to Babí Almenar et al. (2021), for built structures according to: Coma et al. (2018), Köhler and KsiazekMikenas (2018), Kotzen (2018), Palla and Gnecco (2018), Rowe (2018), Magliocco (2018), Mayrand et al. (2018), Pérez et al. (2018a, b), Harada and Whitlow (2020); for land media (Akbari et al. 2001; Atkins 2018; Lazzari et al. 2018; Perini et al. 2018); for water media (Ballard et al. 2007; Perini and Sabbion 2017). The gradient of colour represents the most relevant (dark blue) to the less relevant (light blue) for each NbS

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The potential role of nature-based solutions in urban areas depends on several factors, among which are weather conditions, urban morphology, and the main environmental issues to be addressed. (Data-driven) multi-scalar modeling, analysis, and simulation enable the evaluation and quantification of NbS performance, such as thermal comfort and air quality improvement (Lazzari et al. 2018; Perini et al. 2018). However, it is quite challenging to simultaneously consider a range of performances in the design processes due to computational complexity, the number of fields/disciplines involved, the need to adopt a wide range of different methods and tools, etc.

9.5 Case Studies As shown by the six case studies presented in this section, data are collected or generated to drive the design and decision-making processes and to monitor NbS performance in terms of ecosystem health and ecosystem service provision (Table 9.1 and 9.2). All the case studies presented focus on urban areas and ecosystems (services), (in)direct relationships with nature-based solutions, urban greening, and green infrastructure, involving several fields of study with integrated approaches to consider a wide range of services (Fig. 9.3). Table 9.1 Overview of the selected H2020 projects with short description, methodology and use and type of data. Ongoing and recent projects (starting from the most recent) Title/name of project/funding

Short description

Methodology in brief

Use and type of data

ECOLOPES—ECOlogical building enveLOPES: a game-changing design approach for regenerative urban ecosystems (ECOLOPES 2021); H2020 FET OPEN

ECOLOPES develops the technology to plan and design multi-species building evelopes with an integrated ecosystem approach

The ECOLOPES information model integrates ecological and architectural knowledge, data and models into a data-integrated design recommendation system

Data driven design Data collection and modelling mainly related to occurring plants, animals, and microbiota and on human comfort and wellbeing

UNaLaB Genoa pilot project monitoring (UnaLab 2017); H2020 research and innovation programme

Monitoring activities are implemented to evaluate the performances in terms of ecosystem services provision of the pilot projects Ex Caserma Gavoglio Park

An interdisciplinary group is monitoring the performances of NbS in improving ecological, environmental and economic condition of a district

Monitoring and data collection related to environmental conditions, real estate, wellbeing, water management, biodiversity, etc. (full list in Table 9.3)

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Table 9.2 Overview of the selected papers with short description, methodology and use and type of data. Ongoing and recent projects (starting from the most recent) Title/reference

Short description

Methodology in brief

Use and type of data

Urban regeneration. Benefits of nature-based solutions (Perini et al. 2021)

Identification of a combination of NbS to optimise ecosystem services provision (microclimate and psychological wellbeing)

Comparison and selection of the most performing NbS and design scenarios by means of simulations; surveys to evaluate design scenarios in terms of physical and mental well-being

Data driven design (selection of performing NbS) Data collection (psychological benefits) and modelling (thermal comfort)

Spatial ecosystem service-based decision analysis of green roofs in Barcelona (Langemeyer et al. 2020)

Development and application of a spatial multi-criteria screening tool to select the sites where green roofs should be applied for ecosystem services provision and identification of green roof type for optimization

Selection of the best alternative for green roof design by means of a spatial multi-criteria screening tool which considers estimated ecosystem service provision by different green roof design alternatives and 15 selected spatial indicators

Decision making for site selection Data collection and mapping (ecosystem service provision)

A performance-based planning approach integrating supply and demand of urban ecosystem services (Cortinovis and Geneletti 2020)

Development and test of a performance-based planning approach to assess ecosystem service supply and demand

Assessment of balance between positive and negative impacts of planning thanks to a scoring system linking the indicators of two maps: the “combined ES supply” map and the “integrated ES demand” map

Decision making for site selection and identification of best design strategy Data collection and mapping (ecosystem services demand and supply)

A digital workflow to quantify regenerative urban design in the context of a changing climate (Naboni et al. 2019)

Development of a prototype workflow to evaluate regenerative performance and receive visual feedback on various aspects of regenerative urban design, for an evidence-based urban design process

Integration of a series of existing and customised plugins into a multi-parametric workflow based on the Grasshopper visual programming interface

Data driven design/modelling (human health, wellbeing and energy)

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Fig. 9.3 Nature-based Solutions and ecosystem services considered in the H2020 projects analysed (Table 9.1). * Ecosystem services not included in TEEB 2011 mentioned in the case studies

ECOLOPES focuses on building envelopes designed as multi-species living spaces for four types of inhabitants: humans, plants, animals, and microbiota. Data are collected and generated to drive the design process through a data-driven design recommendation system. The development of ECOLOPES relies on strong collaboration between different disciplines and expertise, such as computational modeling and digitization, data-driven environmental simulations, architecture and decisionmaking, 3D software and system architecture development, plant ecology, animal ecology, environmental microbiology, landscape architecture, and urban planning. ECOLOPES goes beyond the provision of ecosystem services by proposing a radical new approach to regenerate the urban ecosystem. The most relevant ecosystem services are presented in Fig. 9.3, acknowledging the limits of such classification. Indeed, in this case, it is worth highlighting that the approach is not human-centered and inhabitants play a key role in the design process. UNaLab aims to develop smarter, more inclusive, more resilient, and increasingly sustainable societies through innovative nature-based solutions. Genoa (Italy) is among the three front-runner cities in the project, with the Ex-Caserma Gavoglio Park as its pilot project. It mainly includes water and land media NbS, given the relevant issues that the city is facing due to rain water management issues (Magliocco et al. 2020). The selected NbS provide a wide range of provisioning, regulating, support, and cultural services (Fig. 9.3). Data are collected, generated, and analyzed in order to validate the NbS’ effectiveness based on selected Key Performance Indicators (KPIs) (Table 9.3). Some of the data, such as the data for the KPIs “reduction in the Urban Heat Island effect” and “the concentration of PM10 , PM2.5 , NO2, and O3 in ambient air”, are collected through monitoring stations (continuous monitoring). Others are collected through surveys (e.g., to evaluate citizens’ wellbeing) or

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Table 9.3 UNaLaB Genoa pilot project monitoring activities developed by the University of Genoa (Dipartimento di Fisica DIFI, Dipartimento Architettura e Design DAD, Dipartimento di Scienze della Terra dell’Ambiente e della Vita DISTAV, Dipartimento ingegneria civile chimica e ambientale DICCA): summarization of Key Performance Indicators and related measure unit, ecosystem services and field of study Key performance indicators

Measure unit

Ecosystem services

Field of study

Reduction in urban heat Island effect

Change in local temperature UHI (°C)

Regulating services Local climate and air quality regulation

Environmental physics

Concentration of PM10 , PM2.5 , NO2 and O3 in ambient air

Change in local concentration of PM10 , PM2.5 , NOx , O3

Citizen wellbeing associated with environmental conditions (microclimate, psychological wellbeing, social cohesion)

N° of people, % satisfaction

Cultural services Recreation and mental and physical health Aesthetic appreciation and inspiration for culture, art and design Social cohesion

Architecture/psychology

Active involvement N° of people of citizens involved, % people Willing to participate

Architecture/psychology

Real estate value increase of buildings

% increase

Cultural services Aesthetic appreciation and inspiration for culture, art and design

Architecture/real estate evaluation

Biodiversity increase

Shannon’s index (H ), F%, NP/P

Habitat or Supporting Environmental and services Applied botany Biological control Habitats for species Maintenance of genetic diversity

Increase of pollinator insects

Shannon-Weiner index (H ), Pielouindex (J)

Habitat or supporting services Pollination

Zoology

Increase evapotranspiration

ET0 (mm/day)

Regulating services Local climate and air quality regulation

Environmental and applied botany

Increased carbon sequestration

% increase; t/ha/year

Regulating services Carbon sequestration and storage

Environmental and applied botany

Increased stormwater retention

mm (water volume per unit surface area), % water retained

Regulating services runoff control/mitigation*

Hydraulic engineering

(continued)

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Table 9.3 (continued) Key performance indicators

Measure unit

Ecosystem services

Field of study

Water used for irrigation

mm (average volume per unit surface area), m3/year

Regulating services runoff control/mitigation

Hydraulic engineering

through field monitoring campaigns (for the increase in biodiversity and the increase in insect pollinators). Laboratory experiments are also used as a means of quantifying increased stormwater retention. It should be pointed out that ex-post monitoring activities (i.e., at the end of the design phase, during construction, and after the project’s completion) provide relevant data on NbS performance, albeit within the limits of site specific conditions. Perini et al. (2021) rely on data modeling and collection to drive a design process for urban regeneration in order to optimise the benefits of NbS. In this case, the focus is on microclimate regulation for thermal comfort improvement and cultural service provision (Fig. 9.4). The study shows that quantitative data on microclimate regulation provides very important results, but also that perception and psychological benefits should also be considered in the design process. Langemeyer et al. (2020) developed a spatial and multi-criteria screening tool for green roof design and optimization, and more specifically for the site selection and

Fig. 9.4 Nature-based Solutions and ecosystem services considered in papers analysed (Table 9.2). * Ecosystem services not included in TEEB, 2011 mentioned in the case studies

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identification of the best green roof solutions (intensive, extensive, etc.) to obtain a wide range of ecosystem services, such as food provision, local climate and air quality regulation, runoff control, habitat for species, and social cohesion. Although the approach is quite intriguing, the citywide scale may not be the ideal scale for all the aspects considered. Cortinovis and Geneletti (2020) also worked on a similar scale to develop a performance-based planning approach. The study is based on the assessment of ecosystem service supply and demand, and it seeks to balance the positive and negative impacts of planning. Finally, Naboni et al. (2019), although with a limited focus on NbS since only trees are mentioned, developed a workflow to assess the performance of regenerative urban design at the building and district scales. The study connects key performance indicators related to human health, wellbeing, and energy in the context of a changing climate, with the aim of achieving a holistic approach for data-driven design. As shown in Figs. 9.3 and 9.4, all the case studies described focus on local climate and air quality regulation, recreation, and mental and physical health, although with different objectives, methodologies, and data use. In general, the different studies and projects take a wide range of ecosystem services into account, with the prevalence of the environmental and social aspects of urban environments. The case studies analysed show that data modeling, collection, and mapping can effectively drive the design process in different phases, from site selection to the identification of the best design outcome or the generation of design solutions. Such approaches make it possible to consider a wide range of variables in order to exploit naturebased solutions to improve ecosystem health. It is worth mentioning that, in the case studies examined, only larger projects (in terms of available time and budget, such as the H2020 projects mentioned) focus on several, but not all, of the wide range of ecosystem services that NbS can provide. In addition, both H2020 projects rely on a strong interdisciplinary approach. Therefore, research projects, such as the ECOLOPES project, could also focus on developing tools and methodologies able to support projects with lesser interdisciplinarity in order to address the complex issues connected to the improvement of urban ecosystem health.

9.6 Conclusion Notes Urban ecosystems are socio-ecological systems that play a key role in ensuring a good living environment for citizens and urban biodiversity. Improving urban ecosystem health requires an interdisciplinary approach and is highly relevant for facing global challenges, as demonstrated by the strong connection between ecosystem service provision and Sustainable Development Goals. The literature shows that naturebased solutions provide a wide range of relevant ecosystem services for cities and help build resilience and climate change adaptation. The case study analysis outlines a wide range of approaches to the field, based on the use of different data-sets, scales, methods, and tools, with a focus on different ecosystem services. Some main

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challenges requiring further research can be identified. These include the matter of scale of observation: the same ecosystem service can be addressed at both a citywide scale and a building scale, showing that a multi-scalar or transcalar approach represents a relevant challenge. The majority of case studies use a single scale (e.g., citywide or district scale), which is not always appropriate for the different ecosystem services considered. Indeed, ecosystem services such as local climate and air quality regulation have to be evaluated primarily at the district scale, while the moderation of extreme events and biodiversity-related services require a multi-scalar approach (from local to regional). Another key challenge concerns the coexistence of different methodologies in the same research project. Monitoring activities, decision-making, and data-driven design can provide vital tools for an information-driven process for the improvement of urban ecosystem health. Overall, the chapter demonstrates the relevance and potential of NbS, the importance of data, and the interdisciplinary and holistic approach adopted by the majority of the case studies analyzed.

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M, Grêt-Regamey A, Lillebø AI, Malak DA, Condé S, Moen J, Czúcz B, Drakou EG, Zulian G, Lavalle C (2016a) An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst Serv 17:14–23. https://doi.org/10.1016/j.ecoser.2015. 10.023 Maes J, Zulian G, Thijssen M, Castell C, Baró F, Ferreira AM, Melo J, Garrett CP, David N, Alzetta C, Geneletti D, Cortinovis C, Zwierzchowska I, Louro Alves F, Souto Cruz C, Blasi C, Alós Ortí MM, Attorre F, Azzella MM, Capotorti G, Copiz R, Fusaro L, Manes F, Marando F, Marchetti M, Mollo B, Salvatori E, Zavattero L, Zingari PC, Giarratano MC, Bianchi E, Duprè E, Barton D, Stange E, Perez-Soba M, van Eupen M, Verweij P, de Vries A, Kruse H, Polce C, Cugny-Seguin M, Erhard M, Nicolau R, Fonseca A, Fritz M, Teller A, European Commission (2016b) Mapping and assessment of ecosystems and their services—urban ecosystems 4th report Magliocco A (2018) Vertical greening systems: social and aesthetic aspects. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. Butterworth-Heinemann (Elsevier), Oxford, United Kingdom, pp 263–271 Magliocco A, Perini K, Sabbion P (2020) Infrastrutture verdi per l’adattamento ai cambiamenti climatici. Infrastructures écologiques pour l’adaptation aux changements climatiques, ModusOperandi Editore Mavoa S, Davern M, Breed M, Hahs A (2019) Higher levels of greenness and biodiversity associate with greater subjective wellbeing in adults living in Melbourne, Australia. Health Place 57:321– 329. https://doi.org/10.1016/j.healthplace.2019.05.006 Mayrand F, Clergeau P, Vergnes A, Madre F (2018) Vertical greening systems as habitat for biodiversity. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. Butterworth-Heinemann (Elsevier), Oxford, United Kingdom, pp 227–237 McDonald RI, Mansur AV, Ascensão F, Colbert M, Crossman K, Elmqvist T, Gonzalez A, Güneralp B, Haase D, Hamann M, Hillel O, Huang K, Kahnt B, Maddox D, Pacheco A, Pereira HM, Seto KC, Simkin R, Walsh B, Werner AS, Ziter C (2020) Research gaps in knowledge of the impact of urban growth on biodiversity. Nat Sustain 3(1):16–24. https://doi.org/10.1038/s41893-0190436-6 Assessment ME (Program) (eds) (2005) Ecosystems and human well-being: synthesis. Island Press, Washington, DC Naboni E, Natanian J, Brizzi G, Florio P, Chokhachian A, Galanos T, Rastogi P (2019) A digital workflow to quantify regenerative urban design in the context of a changing climate. Renew Sustain Energy Rev 113:109255. https://doi.org/10.1016/j.rser.2019.109255 Nawaz R, McDonald A, Postoyko S (2015) Hydrological performance of a full-scale extensive green roof located in a temperate climate. Ecol Eng 82:66–80. https://doi.org/10.1016/j.ecoleng. 2014.11.061 Nelson EJ, Kareiva P, Ruckelshaus M, Arkema K, Geller G, Girvetz E, Goodrich D, Matzek V, Pinsky M, Reid W, Saunders M, Semmens D, Tallis H (2013) Climate change’s impact on key ecosystem services and the human well-being they support in the US. Front Ecol Environ 11(9):483–893. https://doi.org/10.1890/120312 Oke TR (1982) The energetic basis of the urban heat Island. Quart J R Meteorol Soc (Scientific Research Publish) 108:1–24 Onishi A, Cao X, Ito T, Shi F, Imura H (2010) Evaluating the potential for urban heat-island mitigation by greening parking lots. Urban for Urban Green 9(4):323–332. https://doi.org/10. 1016/j.ufug.2010.06.002 Palla A, Gnecco I (2018) Green roofs to improve water management. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. Butterworth-Heinemann (Elsevier), Oxford, United Kingdom, pp 203–213 Pérez G, Coma J (2018) Chapter 3.10 - Vertical greening systems to improve water management. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. ButterworthHeinemann, pp 191–201. https://doi.org/10.1016/B978-0-12-812150-4.00018-5 Pérez G, Coma J, Cabeza LF (2018a) Chapter 3.7 - Vertical greening systems for acoustic insulation and noise reduction. In: Pérez G, Perini K (eds) Nature based strategies for urban and building

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sustainability. Butterworth-Heinemann (Elsevier), Oxford, United Kingdom, pp 157–165. https:// doi.org/10.1016/B978-0-12-812150-4.00015-X Pérez G, Coma J, Cabeza LF (2018b) Chapter 3.1 - Vertical greening systems to enhance the thermal performance of buildings and outdoor comfort. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. Butterworth-Heinemann, Oxford, United Kingdom, pp 99–108. https://doi.org/10.1016/B978-0-12-812150-4.00009-4 Perini K, Chokhachian A, Auer T (2018) Green streets to enhance outdoor comfort. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. ButterworthHeinemann (Elsevier), Oxford, United Kingdom, pp 119–129 Perini K, Mosca F, Giachetta A (2021) Urban regeneration. Benefits of nature-based solutions. AGATHÓN | Int J Archit Art Des 9:166–173. https://doi.org/10.19229/2464-9309/9162021 Perini K, Sabbion P (2017) Urban sustainability and river restoration: green and blue infrastructure. Wiley Rizwan AM, Dennis LY, Liu C (2008) A review on the generation, determination and mitigation of Urban Heat Island. J Environ Sci 20:120–128 Rowe B (2018) Green roofs for pollutants’ reduction. In: Pérez G, Perini K (eds) Nature based strategies for urban and building sustainability. Butterworth-Heinemann (Elsevier), Oxford, United Kingdom, pp 141–148 Taha H (1997) Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy Build 25(2):99–103. https://doi.org/10.1016/S0378-7788(96)00999-1 TEEB (2011) TEEB manual for cities: ecosystem services in urban management Tomalty R, Komorowski B (2010) The monetary value of the soft, benefits of green roofs, Canada Mortgage and Housing Corporation (CMHC). Canada, Toronto Tratalos J, Fuller RA, Warren PH, Davies RG, Gaston KJ (2007) Urban form, biodiversity potential and ecosystem services. Landsc Urban Plan 83(4):308–317. https://doi.org/10.1016/j.landur bplan.2007.05.003 UNaLab (2017) Urban Nature Lab–H2020. https://unalab.eu/en. Accessed 22 Sep 2021 United Nations (2015) The 17 goals. Sustainable Development [WWW Document]. https://sdgs. un.org/goals. Accessed 20 Sept 2021 Urban GreenUP (2017) New strategies for renaturing cities through nature-based solutions - H2020. https://www.urbangreenup.eu/. Accessed 22 Sep 2021 Wood SLR, Jones SK, Johnson JA, Brauman KA, Chaplin-Kramer R, Fremier A, Girvetz E, Gordon LJ, Kappel CV, Mandle L, Mulligan M, O’Farrell P, Smith WK, Willemen L, Zhang W, DeClerck FA (2018) Distilling the role of ecosystem services in the sustainable development goals. Ecosyst Serv 29:70–82. https://doi.org/10.1016/j.ecoser.2017.10.010

Katia Perini is assistant professor at the Architecture and Design Department, Polytechnic School of the University of Genoa (Italy). Main research interests: effects and performances of nature based solutions in the field of environmental and economic sustainability in (of) urban areas and building/urban design. Katia Perini obtained the EU Ph.D. label in 2012 at the University of Genoa. She was visiting student at the Delft University of Technology and visiting scholar at Columbia University (NY, USA, Fulbright grant) and at the Technische Universität München (TUM, DAAD award). Over 80 publications (peer reviewed journals, books, etc.).

Chapter 10

Smart Urban Forestry: Is It the Future? Stephan Pauleit , Natalie Gulsrud , Susanne Raum, Hannes Taubenböck , Tobias Leichtle, Sabrina Erlwein , Thomas Rötzer , Mohammad Rahman , and Astrid Moser-Reischl

Abstract The urban forest, i.e. the stock of urban trees, is a major component of urban green spaces. It can make significant contributions to urban sustainability and climate change adaptation. Urban forest governance and management play a key role in the extent to which these contributions are realized for good. This chapter presents a selection of promising new technologies in support of urban forestry. Techniques and applications are introduced in the domains of remote sensing, modeling and citizen S. Pauleit (B) · S. Raum · S. Erlwein · M. Rahman School of Life Sciences, Chair for Strategic Landscape Planning and Management, Technical University of Munich, Emil-Ramann-Str. 6, 85354 Freising, Germany e-mail: [email protected] S. Raum e-mail: [email protected] S. Erlwein e-mail: [email protected] M. Rahman e-mail: [email protected] N. Gulsrud Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg C, Denmark e-mail: [email protected] H. Taubenböck · T. Leichtle German Aerospace Center (DLR), Earth Observation Center (EOC), Münchener Str. 20, 82234 Weßling, Germany e-mail: [email protected] T. Leichtle e-mail: [email protected] H. Taubenböck Department of Remote Sensing, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany T. Rötzer · A. Moser-Reischl School of Life Sciences, Chair of Forest Growth and Yield Science, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_10

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science. These technology-driven developments offer new potentials for ‘smart’ urban forestry but may also create new risks of a shift towards techno-managerialism as opposed to more open and democratic processes. Keywords Urban green infrastructure · Urban tree growth · Remote sensing · Ecosystem services · Urban microclimate · Governance · Social-ecological-technological system

10.1 Urban Forestry and Smart Technologies—An Emerging Relationship Global challenges of urbanization and climate change create a growing need for more sustainable and resilient cities with a high quality of life. Successfully addressing these challenges will often require major transformations of existing urban areas and a novel conception of future urbanization, not least in support of the change of contemporary urban life-styles. Along these lines, global and EU policy frameworks promote nature-based solutions for climate resilience (EEA 2021), including in urban areas, with a focus on an integrated approach to balancing ecological, social, and digital methods (Bai et al. 2018; Farinea 2021; Galle et al. 2021). Policymakers are demanding not only ‘smart’ cities managed through information and communication technology, but also ‘smart’ urban nature that serves as an “urban green infrastructure” (Pauleit et al. 2017) with enhanced ecological and social benefits delivered through digital solutions (Gulsrud 2018). The “urban forest”, i.e. the entire stock of urban trees (Randrup et al. 2005), is a major component of urban green infrastructure. It can cover significant proportions of the urban land surface (Nowak and Greenfield 2012), and provide important environmental, social and economic benefits such as provisioning of food, wood and fibers; cooling of air temperatures, reducing stormwater runoff, absorbing air pollutants and storing carbon; as well as being places for recreation, education, aesthetic and spiritual experience (Rahman et al. 2020; Dobbs et al. 2017; Willis and Petrokofsky 2017; Nesbitt et al. 2017). The governance of urban forests, i.e. “efforts to direct human action towards common goals, and more formally as the setting, application and enforcement of generally agreed rules” (Wirtz et al. 2021:1), plays a key role in the extent to which and how these contributions are realized (Wirtz et al. 2021). Improved local information on urban forests is key for successful urban forest governance and management (e.g. Konijnendijk 2012; van der Jagt and Lawrence 2019; Ordóñez et al. 2019, 2020; Campbell et al. 2021; Wirtz et al. 2021). Accordingly, the urban landscape community is ripe with discussions regarding visions for digital approaches to increase forest information for the enhancement of the governance of urban green infrastructure, nature-based solutions and urban forests (e.g. A. Moser-Reischl e-mail: [email protected]

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Gulsrud et al. 2018a; Nitoslawski et al. 2019; Matasov et al. 2020; Goddard et al. 2021; Ghahramani et al. 2021; Prebble et al. 2021; Galle et al. 2021). It is hoped that an “Internet of Nature” will lead to a new form of “ecosystem intelligence” generated by a seamless network of applications from multiscale and multitemporal remotely sensed monitoring of urban trees, complemented by continuous and spatially dispersed ground surveys and measurements that would feed into advanced modelling, e.g. for assessment of ecosystem status health and dynamics, and the related delivery of ecosystem services. Management of big data, e.g. from social media platforms can give detailed information on human behaviour, perceptions and values related to urban trees. Moreover, digital tools would provide venues to increase citizen participation via e-governance (Galle et al. 2021) to the benefits of an increasingly diverse society. There are, however, also potentially unintended negative consequences associated with the use of emerging smart technologies for such ‘smart urban forest management’ which need further critical exploration (Gulsrud et al. 2018a). These may include impacts of exposure to high frequency millimeter waves on human and tree health (Waldmann-Selsam et al. 2016), and changes in insect behaviour (Thielens et al. 2018). It may also compound the digital divide and environmental justice issues (Gulsrud et al. 2018a; Gabrys 2020) and there are ethical concerns surrounding data usage and privacy of citizens (Viitanen and Kingston 2014). Potentially, the remote control of data by global corporations may influence local decision-making and participation (Gabrys 2020). There are also concerns about the effectiveness of technologies as these rarely appear to be used to the full extent originally anticipated (Schröter et al. 2016) and achieve their stated goal of improving environmental policy and practice (Raum et al. 2019). This is frequently due to a lack of involvement of end users and/or inadequate knowledge transfer and exchange, amongst other reasons (Raum et al. 2019). In this chapter, we will first present some of the key technologies and tools that have become available recently and hold promise to fulfill the expectations of smart urban forestry: remote sensing to provide detailed information on the stock and the dynamics of urban trees; modelling of tree growth and ecosystem services. How can these and related technologies contribute to successfully addressing the governance needs at strategic, tactical or operational levels (Church et al. 2000)? How will advanced technologies interact with the social and the ecological domain of urban systems under a future perspective (Markolf et al. 2018)? And can such technologies promote the adoption of new forms of co-governance and address issues of environmental justice (Rutt and Gulsrud 2016; Kotsila et al. 2020) or will they further alienate people from nature and reinforce a technocratic, dehumanized approach to urban forestry?

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10.2 Smart Technologies for Urban Forestry Monitoring and Modelling 10.2.1 Advanced Monitoring of the Urban Tree Resource Despite their unique capabilities and diverse positive effects on the urban ecosystem, detailed data on urban trees has remained widely scarce (see, e.g. Pauleit et al. 2005; Nowak and Greenfield 2012). Existing municipal urban tree inventories are often not publicly accessible and cover only part of a city (Feltynowski et al. 2018). In particular, they mostly capture only trees on public grounds in view of their municipal responsibilities (e.g., tree management and maintenance, traffic safety, etc.). In most cases, the data for such tree inventories still are collected by time-consuming and costintensive terrestrial surveys conducted by experts and are therefore only suitable for limited spatial coverage (Bancks et al. 2018). Furthermore, existing urban tree inventories are mostly heterogeneous among different cities (Nielsen et al. 2014). Lack of knowledge on the complete tree population (i.e. area-wide on public and private ground) obstructs a comprehensive assessment of ecosystem services provided by trees in cities for their strategic management. Recent advances in remote sensing technology offer suitable capabilities for consistent and area-wide assessment of land cover, also regarding vegetation and trees in cities (Tigges et al. 2013; Nowak and Greenfield 2020; Taubenböck et al. 2021). Especially in heterogeneous and dynamic urban environments, remote sensing data with high (HR) and very high spatial resolution (VHR) can be a valuable source of information with respect to vegetation and urban trees (Fig. 10.1) (Li et al. 2019). Depending on the scope of the analysis, airborne as well as spaceborne imagery at different wavelengths, airborne as well as terrestrial Lidar (Light detection and ranging) for three-dimensional characterization, as well as street-view imagery can be considered for mapping urban trees. In addition, remote sensing facilitates the monitoring of urban tree stock through repeated and consistent image acquisition (Deng et al. 2019). Information retrieval on urban trees is an active and ongoing research topic and various approaches have been proposed based on different types of remote sensing data (Fig. 10.2). Remote sensing data with high spatial resolution (HR) is collected by multispectral satellite systems like Landsat (15–30 m) or Sentinel-2 (10 m) or the currently developed hyperspectral sensor EnMAP (30 m). Due to their lower spatial detail, these data are only capable of mapping individual urban trees with very large crowns and contiguous patches of trees (Shojanoori and Shafri 2016). However, these data provide the advantage of continuous large area or global coverage (e.g. Weigand et al. 2020). For instance, the Copernicus Street Tree Layer (STL) (Copernicus 2018) offers consistent information on urban trees across 788 Functional Urban Areas in 39 countries of Europe based on Sentinel-2 imagery. However, it is limited to a spatial resolution of 10 m and only covers contiguous rows or patches of trees covering at least 500 m2 with a minimum width of 10 m.

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Fig. 10.1 Remote sensing data sources for comparison of HR (a), VHR (b) and DSM (c) data. While only clusters of trees can be identified in the HR data, individual trees can be recognized in the VHR and nDSM data. Image source German Remote Sensing Data Center, German Aerospace Center (DLR-DFD)

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Fig. 10.2 Information retrieved from different remote sensing data sources for comparison of vegetation fraction mapping based on HR data (a), vegetation classification based on VHR data (b) and individual tree detection and crown delineation based on DSM (c). Image source German Remote Sensing Data Center, German Aerospace Center (DLR-DFD)

A suitable method to capture even smaller patches of urban vegetation including trees is spectral unmixing for vegetation fraction mapping (Fig. 10.2a), which has been demonstrated using multispectral Sentinel-2 (Schug et al. 2020) or hyperspectral EnMAP data (Okujeni et al. 2017). Multispectral VHR imagery is collected by spaceborne sensors like QuickBird or WorldView which offer spatial resolutions up to 30 cm as well as unmanned aerial vehicle (UAV) and airborne imagery with even higher spatial detail. Such data was employed for object-based classification of urban environments, including the detection and identification of urban trees and their species (e.g., Taubenböck et al. 2010; Zhang and Hu 2012; Li et al. 2015) (Fig. 10.2b). Three-dimensional information in terms of a digital surface model (DSM) captured by Lidar or airborne stereo imagery can also be used for urban tree detection, crown delineation, or species identification (Fig. 10.2c) (e.g., Zhen et al. 2016; Leichtle et al. 2021). Owing to the detailed spectral information, airborne hyperspectral observations are particularly suited for tree species classification at the individual tree level in combination with airborne Lidar (e.g., Alonzo et al. 2014; Liu et al. 2017). A recent and innovative source of data are acquisitions from the “street-view” perspective, which include mobile (MLS) and terrestrial (TLS) laser scanning as well as street-view images (Fig. 10.3). Similar to VHR remote sensing, MLS data is used for identification of single trees (Luo et al. 2021) as well as for derivation of

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Fig. 10.3 “Street-view” data of urban trees. Point cloud data acquired by TLS (a) as well as images from the street perspective (b, c). Image source Chair of Forest Growth and Yield Science, Technical University of Munich (TUM)

additional characteristics like tree height, diameter at breast height, foliage height, etc. (Safaie et al. 2021). Even more detailed structural tree attributes regarding stem and crown dimensions, as well as crown surface and volume properties have been delineated from TLS (Bayer et al. 2018). In contrast to MLS and TLS point cloud data, which is usually collected in the course of cost-intensive measurement campaigns, street view data is publicly available from different image databases (e.g. Google Street View). These images have proven their utility for cost-effective quantification and mapping of urban trees using computer vision (Seiferling et al. 2017) and deep learning techniques (Branson et al. 2018; Lumnitz et al. 2021). To date, the accuracy of information on urban trees derived from remote sensing typically ranges in the order of 80–90% overall accuracy, partly even higher. However, each source of remote sensing data provides specific capabilities as well as inherent limitations. For instance, “top-view” remote sensing (i.e. aerial and satellite images) only allows the derivation of tree attributes visible from above, while “street-view” data is unable to represent the crown adequately. Therefore, the combination of multisource, multi-view, and multi-temporal data is crucial for improvement of urban tree mapping and monitoring (Alonzo et al. 2014; Li et al. 2015; Branson et al. 2018). Also, other sources of data like volunteered geographic information (Roman et al. 2017) or the combination of remote sensing techniques with existing urban tree inventories (Wallace et al. 2021) bear high potential for improving the accuracy of information on the urban tree stock as well as additional perspectives of thematic information on urban trees (e.g., tree position, % of canopy cover). Munich—Detection and Delineation of Urban Trees In this case study of Munich, we showcase the capabilities of remote sensing data for the assessment of the urban tree stock based on remote sensing technology. VHR airborne stereo imagery and a derived canopy height model (CHM) were utilized for detection and delineation of urban trees. To do so, local maximum (LM) filtering was combined with marker-controlled watershed segmentation (MCWS) for detection of tree positions as well as delineation of tree crowns. Compared to reference trees from

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terrestrial measurements, the accuracy of tree detection lies well above 90%, while tree crowns were delineated with 88% overall accuracy. In this survey, the entire stock of trees was recorded for Munich, whereas previously only an estimate of the trees in public spaces was mapped and known through official data sets. According to our analysis, there are 1.54 million trees in Munich (Fig. 10.4a), roughly corresponding to one tree per person. Results can easily be transferred to administrative spatial units for urban forest management (Fig. 10.4b). Compared to population numbers (Fig. 10.4c), the number of trees per person (Fig. 10.4d) can be estimated as a spatial indicator for sustainable urban forest management and informed decision-making in cities. Results show the wide variation of tree density and per-capita provision of trees within the city, raising issues of environmental justice. Moreover, comparison with a dataset from 2011 revealed areas of tree gains and losses.

Fig. 10.4 Urban trees of Munich. a Urban trees per hectare, b Trees per hectare on administrative units, c Population per hectare on administrative units, d Trees per inhabitant on administrative units

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10.2.2 Smart Measurement and Multiscale Modelling of Urban Tree Growth and Ecosystem Services The knowledge generated from remote sensing about the location, quantity and other statistical parameters of city trees is not yet sufficient for targeted planning and management of urban forests. Knowing how much cooling potential a tree has, how much water can be retained by the greenery of a square, how much space trees occupy after 30 years, or how much biomass is accumulated by different tree species (and thus how much CO2 is removed from the atmosphere) helps planners and green managers in making decisions for strategic planning of the urban forest in ensuring sustainable and climate resilient cities. For example, as part of the Center for Urban Ecology and Climate Adaptation (see www.zsk.tum.de), a project was launched in the city of Würzburg/Germany (see www.klimaerlebnis.de) in which tree ecophysiological and micro-scale meteorological data were continuously recorded with sensors and transmitted via mobile modems. In combination with the improved surveying methods using remote sensing, it is possible to measure the environmental benefits of urban trees and deliver a data basis for research e.g. for parameterizing tree species in growth models. At the same time, these “talking trees” can inform citizens via displays on tree growth, water consumption and their ecosystem services and thus raise awareness and interest to maintain the local environment. Increasingly more powerful software is being developed for simulating the growth and dimensional changes of the urban green; for determining the provision of ecosystem services such as the cooling potential, the CO2 fixing capacity and the runoff reduction of trees at a specific site even under future climate conditions (Rötzer et al. 2020); or for modelling the urban microclimate under different tree planting scenarios (Zölch et al. 2016; Erlwein et al. 2021a, b). Most of the urban tree growth models such as UrbTree or CityGreen are empirical, which means that growth and ecosystem services are calculated based on allometric equations derived from statistical relationships that are only valid for the respective study region (Rötzer et al. 2020). Process-based models, on the other hand, are based on generally valid physical, chemical, and biological processes, i.e., they are not subject to spatial or temporal constraints. Hybrid models occupy an intermediate position. An example for the hybrid type is the i-Tree model family which is now widely applied for assessments at city to site scales (Nowak et al. 2008; Hand and Doick 2018; Pace et al. 2018). Similarly, an example for a process-based model is City Tree (Rötzer et al. 2019). This model simulates the growth of tree individuals based on environmental conditions. The City Tree model also allows to simulate net primary productivity, carbon fixation, actual evapotranspiration, interception and run off based on the given environmental conditions. Thus, tree growth during a year can be simulated depending on climate, soil and urban site conditions. Along with the estimation of ecosystem services of urban trees such as carbon storage, reduction of rainwater runoff, shading, and cooling by transpiration, CityTree is able to dynamically model tree growth and

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provision of ecosystem services for a changing environment, e.g. under future climate conditions (Fig. 10.5). Ecosystem services such as filtering of particulate matter or disservices like the emission of biogenic volatile compounds (BVOCs) or the release of allergens, have not yet been considered in most models (Rötzer et al. 2020). Also, cultural ecosystem services cannot be included in these models, e.g. recreational qualities of the urban forest, or identity and spiritual values. CO2-fixation [kg CO2 year-1 ] City: mean of south German cities

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10.3 Advanced Participatory Approaches for Smart Co-governance of the Urban Tree Resource In some parts of the world, urban foresters are turning to smart governance platforms and e-tools to deliver novel forms of collaborative management, co-creation and stewardship. This section outlines two brief case studies of such “smart” urban forestry approaches and engages the tensions between open digital co-creation, the risks of enforcing the digital divide, and elite capture through proprietary exclusion of technology and data.

10.3.1 Melbourne—Digital Social-Ecological Stewardship of the Urban Forest The city of Melbourne, Australia is actively working with a “smart” approach to urban forestry governance to legitimize diverse values of nature and increase resident sense of well-being by strengthening green placemaking. This is done by integrating diverse local and traditional knowledge into their urban forest strategy (UFS) as the city aims to increase their urban canopy by 40% by 2040 against the odds of community contestation and increasingly unpredictable climatic conditions (City of Melbourne 2012). An on-line digital platform called the Melbourne Urban Forest Visual engages citizens in discussions of ecosystem services through an invitation to explore the “big tree data” of the publicly-managed urban forest providing a platform to monitor the health and predicted life-duration of Melbourne’s approximately 70,000 publiclyowned trees. This system situates every municipal tree on an interactive map with rich place-specific data monitoring the current tree diversity, tree canopy cover, and health performance of Melbourne’s urban forest—information that until 2012 was not available to the public. The Urban Forest Visual gives residents a tool to visualize and better understand the diverse values of the city’s urban forest and the accompanying risk of massive tree-death if new tree plantings are not undertaken (http://melbourne urbanforestvisual.com.au; https://www.melbourne.vic.gov.au/community/greeningthe-city/urban-forest/Pages/urban-forest-strategy.aspx). Residents are able to track the progress of the implementation of neighborhood tree planting plans. Significantly, residents have also been given the ability to celebrate and mourn the current transformations in the urban forest. Each publicly-owned tree in Melbourne has been given an email address and residents have sent so called love letters to the trees in droves. This has given the City of Melbourne the opportunity to collect information from citizens regarding their personal social-ecological perceptions of trees and celebrate the diverse and subjective appreciation for trees in Melbourne. These digitallycollected place narratives have been used by City of Melbourne council members to argue for additional funding and political backing for the UFS by drawing on the power of the diverse socio-cultural understandings associated with the urban forest.

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Another digital tool used by decision makers in the urban forest strategy is an online mapping effort to demarcate prominent Aboriginal place markers in local forests and gardens. The city is actively working with local Indigenous groups to consistently update these place markers through online interactive mapping with GIS as a means of legitimizing new understandings of aboriginal Melbourne (City of Melbourne 2012). The City of Melbourne’s digital approach to UGI governance engages residents’ sense of place and expert knowledge regarding their local green heritage and landscapes, and draws on citizens’ mental models of community-based climate resilience and well-being. Residents are therefore challenged to move beyond a long-standing debate regarding Australian vs. European tree species to focus on optimal planting preferences and locations for climate resilient-trees in their neighborhoods. In this regard, a digital approach to UGI governance can be seen as integrating sociocultural and scientific knowledge to successfully promote and achieve higher-levels of urban biocultural diversity (Buizer et al. 2016; Gulsrud et al. 2018b). This case also suggests that an automated approach to UGI governance can successfully integrate “other knowledge systems outside of modern science” such as local and Indigenous place-based perspectives (Williams 2014; Gulsrud et al. 2018b) and can actively facilitate pluralistic views of landscape based in hybrid understandings of place. Melbourne’s collaborative and place-based UGI governance process diffuses power to many groups; however, citizens that fall outside of local interest groups, welleducated ranks, and the digitally-empowered, such as lower socio-economic populations, children, and the elderly, could be constrained from participation or a sense of ownership over the implementation process. de Fincher et al. (2016) demonstrate that citizens from marginalized communities in the City of Melbourne do not feel empowered despite being engaged in place-based community design processes. Nguyen and Davidson (2017) aptly highlight this problem as an issue of green “techno-apartheid.”

10.3.2 Stockholm—Sounding the Voices of Nature. Cyborg Trees and the Proprietary Challenges of Digital Natures Scientists in Stockholm are using ecological-technological linkages and interactions to give a voice to nature through sensors and artificial intelligence (A.I.) applications, thereby empowering more-than-human actors like urban trees in the design and overall governance of the built environment (https://smartergreenercities.com/ our-research/case-studies/). The aim of this project is to experiment with different ways in which smart technologies can strengthen feedback between nature-based solutions (NBS), biodiversity and people and thereby advance a notion of SMARTer and Greener Cities. Wireless sensors which are connected to trees and their watering beds monitor the real-time ability of the ecosystem functions provided by the urban

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forestry and urban greening. Recent technological advances have made meteorological stations affordable and portable while preserving high measurement quality. This provides an opportunity to measure atmospheric and soil conditions (humidity, rainfall, temperature, windspeed, soil moisture) at a very local scale of individual courtyards, squares, playgrounds, and parks. Scientists are using this novel form of harnessing data to understand the performance of NBS across seasonal variation and under extreme weather conditions in terms of their direct impact on the local climate. Here urban trees play a large role in demonstrating why NBS is critical in the smart city agenda and overall sustainable urban development. Preliminary data from the sensors demonstrates that ecosystems support increased human health and wellbeing through air pollution removal, stormwater absorption and urban cooling thus creating resilient cities for liveable futures. This project leverages established local networks with practitioners and decision makers working on improving and expanding NBS for climate resiliency, human wellbeing, and ecosystem integrity. Project scientists are also actively engaging private sector actors to bring smart technology into urban nature. This project thereby brings local engagement, co-design, and co-development into smart city research and practice while also expanding the smart city agenda to encompass urban trees and other forms of nature-based approaches and systems-based understanding of UGI impacts and opportunities. Solutions can potentially be scaled to other cities. These sensors also generate data that address the challenge of how to account for the values of non-human nature in NBS and smart technology design. While multiple studies have reported on the instrumental values and benefits of nature-based or smart solutions (Caird and Hall 2019; Raymond et al. 2017), few have considered how they can jointly support the intrinsic values of a given area’s biodiversity and ecosystem services. Results promise to account for power imbalances in sustainable urban development and smart city schemes where more-than-human actors such as trees, birds, and insects are left out of decision making despite their critical contributions to overall urban resilience through ecosystem services (Garbys 2020; Gulsrud 2018). There is, however, uncertainty regarding the technological proprietorship of data linked to public–private partnerships. The proprietors of such technology stand to gain enormous financial sums, an interest that most likely will clash with those of citizen and public-sector consumers over time (Viitanen and Kingston 2014). Finally, in Singapore, cyborg supertrees have replaced indigenous forests, illustrating the risk that ecosystems with lower social and economic value could be replaced with landscapes that can literally pay the rent (Gulsrud 2018). Who owns smart nature? And who benefits from the economic and social gains from the technological outputs?

10.4 Opportunities and Risks of Smart Urban Forestry This chapter presented a small selection of key new technologies with applications in Europe, Asia, and Australia that demonstrate some of the potentials and risks of new sensors, data and methods for developing smart and green cities. Although

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not yet perfect, the data derived from high spatial, temporal and spectral resolution remote sensing can significantly increase the capacity to map, inventory, assess and monitor urban forests (e.g. Leichtle et al. 2021). It may also serve as a foundation for modelling of ecosystem services from single trees to entire stands by tools such as iTree and CityTree (Nowak et al. 2008, Rötzer et al. 2019). Ongoing developments in remote sensing, largely driven by increased data availability, by the development of new sensors, and by improved image processing techniques in the domains of A.I. will further increase the utility of this technology in urban forestry. Some sources of data (especially VHR data) are commercial and not publicly/freely available; potentially disadvantageous to less well-resourced cities, but there is a trend towards free and open data. Still, methods need to be advanced, e.g. for the automated identification of tree species. In addition, remote sensing still provides limited information on the growth conditions of urban trees, such as water availability, and their health status and functional attributes such as tree transpiration. High potential lies in the combination of different sources of data and information, e.g. a combination of “top-view” and “side-view” remote sensing data (Branson et al. 2018). To date, analyses based on remote sensing achieve accuracies in the order of 80–90% overall accuracy. Remaining misclassifications must be communicated transparently to support evidence-based decision-making and actions. In addition, it should be mentioned that the new applications of remote sensing and related geospatial fields also offer further potential for thematic applications in the context of urban forests. Examples include noise attenuation modeled by land-use regression using remote sensing data, where trees and vegetation naturally have a dampening factor (e.g. Staab et al. 2021). The examples of Würzburg and Stockholm have shown how the growth and functioning of street trees can be measured and transmitted via mobile modems. Also, recording of e.g. stem diameters and further tree attributes by street view tools can facilitate such assessments, but data privacy issues need to be carefully considered before their widespread employment. Moreover, approaches based on citizen science may be a viable option for acquisition of some less complex urban tree attributes (Roman et al. 2017) and enhance the participation of stakeholders in urban forest governance. The Melbourne Urban Forest Visual demonstrates how citizens can be effectively informed on the values of urban trees and the progress of Melbourne’s urban forest strategy. Also, tools were employed to collect information on diverse cultural values of trees and places. Therefore, the building blocks of an ‘Internet of Nature’ (Galle et al. 2021) are already there but are still waiting for their widespread systemic adoption in practice. Close interactions between ecological and technological as well as social and technological systems were observed in the case studies. While remote sensing and ground measurements of trees are improving our understanding of the urban forest composition and its functioning, their application in practice will mostly serve to reach the goals of urban forest governance more effectively and efficiently, e.g. by introducing (semi-) automated tree monitoring, precision irrigation systems of even robots for tree maintenance works.

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The deployment of these technologies may come at a price, though. Key issues that emerge concern the role of humans in designing and managing the computational systems driving autonomous landscapes. Such intelligent landscapes build on algorithms, step-by-step sequences of tasks which execute automated decisions. While the computational performances of smart natures are frequently innovative and well executed, they can also lead to damaging social and environmental consequences. The computational technology behind intelligent systems such as automated vertical farming, citizen science apps, and e-plants are dependent on the assumptions inserted into the algorithms and the data that provide the foundation for their training. If such data are flawed or biased, which has been shown in both the finance and crime prevention sectors, the results of the algorithm can be misleading, with widespread consequences. For example, the traditions, knowledge systems and cultures represented in heavily automated urban landscapes will be dependent upon who controls the technology of automation in their management (Gulsrud 2018). Landscape planners and urban forest managers also need to be aware that automation which actively engages local communities may not necessarily serve social justice or public health and well-being outcomes at the city scale, because only local interests, as programmed into the algorithms, will be considered. Important cultural and social justice issues also need to be addressed to build trust in automated systems, such as whose knowledge and customary traditions will be drawn on to manage urban landscape and how smart natures will be designed to manage issues of social exclusion and allow a range of voices to be considered in ecosystem management. The case of Melbourne showed that establishment of information systems alone cannot address issues of power imbalances and marginalization. More information, modelling tools and new tools for e-governance may not reduce, but rather increase the demands for resources and skilled personnel for their full use in urban forest governance, from strategic planning to its day-to-day management. Therefore, smart urban forestry should be conceived first of all as an investment into the quality and performance of urban forest governance. As an outcome, it should serve the socially-inclusive development of multifunctional urban forests, the benefits of which are fairly accessible to all. Still, cost savings may be realized, e.g., when digital tools are developed in a user-friendly way and in conjunction with end-users, to better address their actual needs and resources. Moreover, while new technologies and tools to assess, model, monitor, and value ecosystems have proliferated, few have been found to cause direct changes in policies and practices (Schröter et al. 2016; Stewart et al. 2013) or rarer still actual change to the environment (Ruckelshaus et al. 2015). This raises the question as to whether such environmental decisionmaking technologies and tools and the abundant information on the urban forest, its functions and dynamics, derived from them, find their way into governance at the strategic, tactical and operational levels. In recent years, there has been a growing realisation that traditional ‘knowledge transfer’ is insufficient, and that interaction and ongoing engagement with technology end-users is a fundamental requirement for impact generation, i.e. to make them really useful (Morton 2015). Finally, it needs to be stressed that this chapter could only give a first glimpse into the emerging field of smart urban forestry. We are at a stage where new technologies

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are becoming available but their uptake in practice is still only in the beginning. Currently, research still is mostly conceptual whereas empirical studies are very rare. Therefore, we do not understand sufficiently well how the application of smart technologies in urban forestry will influence the relationships between society and urban nature, and consequently, how to regulate this field of policy-making. Given the speed of technological development driven by strong political and economic interests, there is an urgent need for further consideration of these issues that should closely involve expertise from social, ecological and technological fields in a socioecological-technological systems approach and closely collaborate with end-users (McPhearson et al. 2016; Gulsrud et al. 2018a, b). Such research should both reduce our knowledge gaps of the potentials and limitations of smart technologies under current regimes of urban forestry governance but also scrutinize ways for the transformation of governance regimes for an enhanced uptake of smart urban forestry. Therefore, we argue for reflexive smart urban forestry as a field of research and practice. Acknowledgements Parts of this chapter arise from ideas and concepts of the project “Ökosystemleistungen des Urbanen Forsts” (ecosystem services of the urban forest), funded by the German Federal Environmental Foundation (DBU), AZ 37076/01.

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Zhang K, Hu B (2012) Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles. Remote Sens 4:1741–1757 Zhen Z, Quackenbush LJ, Zhang L (2016) Trends in automatic individual tree crown detection and delineation—evolution of LiDAR data. Remote Sens 8:333 Zölch T, Maderspacher J, Wamsler C, Pauleit S (2016) Using green infrastructure for urban climateproofing: an evaluation of heat mitigation measures at the micro-scale. Urban for Urban Green 20(1):305–316. https://doi.org/10.1016/j.ufug.2016.09.011

Stephan Pauleit (Dr.) is professor and holds the Chair for Strategic Landscape Planning and Management at the Technical University of Munich (TUM). He received a diploma degree in landscape architecture and landscape planning at TUM where he also obtained his doctoral degree. He held positions at Wye College, the University of Manchester and the University of Copenhagen. Urban ecology, green infrastructure planning, adaptation strategies to climate change in the urban environment, urban forestry and trees are his main areas of research. Stephan Pauleit is the director of the Center of Urban Ecology and Climate Change Adaptation awarded by the Bavarian Ministry of the Environment and Consumer Protection. Natalie Gulsrud (Dr.) is an Associate Professor at the University of Copenhagen, Department of Geosciences and Natural Resource Management, Section for Landscape Architecture and Planning. She studies the governance of urban green infrastructure to advance sustainable and just pathways to climate resilience. This research engages social, ecological, and technological interactions in the “gray to green” infrastructure of cities from bicycle pathways to parks, trees, and community gardens. She coleads the Swedish government research council for sustainable development (FORMAS) “A sustainable spatial planning framework for engaging diverse actors and citizens in revitalising inbetween spaces for social inclusion, biodiversity, and wellbeing” (VIVAPLAN) in Denmark and Sweden. Other projects include the FORMAS funded “Planning with Youth: a tool and framework for engaging meaningful and forward-looking participation of youth in shaping attractive and sustainable living environments”. Susanne Raum (Dr.) is a Marie Sklodowska-Curie Action Fellow at the Chair for Strategic Landscape Planning and Management at the Technical University of Munich. She has degrees in Geography (B.A.) and Environmental Management (M.Sc.) and a doctoral degree from Imperial College London (Ph.D.). She held positions at Imperial College London (Centre for Environmental Policy), SOAS University London (Environmental Law Institute), and Forest Research (Social and Economic Group), the UK government’s forest research institute. She also coordinated the Air Pollution Research in London network (2008–2010). Her research focuses on the dynamics of human-environmental relationships in order to understand how people relate to and value nature, and how this information can inform policy and management practices. Currently, she investigates the growing threats of urban tree pests and diseases under the EU Horizons 2020 funded project ‘TREEPACT’. Hannes Taubenböck received the Diploma in geography from the Ludwig-Maximilians University München, Munich, Germany, in 2004, and the doctoral degree (Dr.rer.nat.) in geography from the Julius Maximilian’s University of Würzburg, Würzburg, Germany, in 2008. In 2005, he joined the German Remote Sensing Data Center (DFD), German Aerospace Center(DLR), Weßling, Germany. After a postdoctoral research phase with the University of Würzburg (2007–2010), he returned in 2010 to DLR–DFD as a Scientific Employee. In 2013, he became the Head of the “City and Society” team. In 2019, he habilitated at the University of Würzburg in Geography. His current research interests include urban remote sensing topics, from the development of algorithms for information extraction to value adding to classification products for findings in urban geography.

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He is an urban ecologist, and his major interest revolves around the quantification of ecosystem services such as cooling, human thermal comfort at outdoor settings, runoff reduction, carbon storage and sequestration across scales and climate and built environment gradient. He is interested in any topic related to the urban ecosystem structure, function, management, and design and planning in retrofitting our existing cities towards sustainable development. Tobias Leichtle (Dr.) received the B.Sc. degree in geography and the M.Sc. degree in geospatial technologies in 2009 and 2013, respectively. In 2020, he received the Ph.D. degree (Dr.rer.nat.) from the Humboldt University of Berlin, Germany on change detection in VHR remote sensing imagery as well as application in the context of urban geography. From 2013 to 2018, he was employed as a research associate at the Company for Remote Sensing and Environmental Research (SLU), Munich, Germany. In 2019, he joined the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR), Weßling, Germany. His research interests include the development of machine learning methods for information extraction from multimodal earth observation data, change detection in VHR remote sensing imagery as well as the analysis of urban green infrastructure in cities. Sabrina Erlwein (M.Sc.) is currently employed as a research associate at the Chair for Strategic Landscape Planning and Management at the Technical University of Munich. She received her B.Sc. degree in geography at the Philipps-University Marburg (2014) and the M.Sc. degree in Ecological Engineering and Environmental Planning at the Technical University of Munich (2016). From 2016 to 2018, she has worked as a city planner and project coordinator for a city and landscape planning office, before returning to university for conducting her Ph.D. research on the possibilities for climate change adaptation through urban greenery in growing cities, including assessment of human thermal comfort and city planning processes. Thomas Rötzer (Dr.) is a professor at the Technical University of Munich. He has been working on ecosystem modelling for more than twenty years. His research deals with the growth dynamics of forest ecosystems and urban trees at tree, stand and landscape level. His focus is on the effects of climate change on plant growth and the adaptation of cities by changing green structures. He is deputy head of the Center for Urban Ecology and Climate Adaptation. He published more than 150 peer reviewed articles and book chapters. Mohammad A Rahman (Dr.) is a Research Associate at the Chair for Strategic Landscape Planning and Management, TU Munich. He did his Ph.D. in urban ecology from the University of Manchester, UK and worked as a Humboldt Post-doctoral Fellow at the TUM (2015–2018). Astrid Moser-Reischl (Dr.) studied biology at the University of Regensburg with focus on ecology, nature conservation, botany and zoology. She did her Ph.D. on growth and ecosystem services of common urban tree species at the Technical University of Munich. Her research interest is especially the connection between site conditions, growth and ecosystem service provision of urban trees with their specific species characteristics. She works as a coordinator of the Center for Urban Ecology and Climate Adaptation on research questions related to the climateadapted city of tomorrow as an interface between research and practice. She is included in further projects such as “Carbon smart forestry—CARE4C” (EU funded) and “CUT—Climate and Urban Trees. Effects of trees on urban climates during climate change” (awarded by the German Research Foundation).

Chapter 11

Big Data and Decision Support in Rural and Urban Agriculture Defne Sunguro˘glu Hensel

Abstract 21st century agricultural production faces major challenges including climate change, environmental degradation, land use change, and population growth leading to food shortage. Addressing these challenges can benefit substantially from inclusive approaches that consider both rural and urban agriculture. As the exchange of insights and approaches in food production between rural and urban contexts intensifies it is useful to map related development in both contexts, with the aim to highlight present overlaps, exchanges, and research foci, as well as to identify research gaps in developing novel solutions to the existing challenges. In this context this chapter focuses on charting the increasing role of Big Data, data-related methods and decision support in seeking to face these challenges. Keywords Urban Agriculture · Agriculture · Sustainability · Big Data · Data-related Methods · Decision Support

11.1 Introduction 21st century agricultural production faces major challenges that include adaptation to climate change (Howden et al. 2007; Venkatramanan et al. 2020), environmental degradation caused by or impacting upon agriculture (Hossain et al. 2020; Leighton 2021), land use change caused by or impacting upon agriculture (Houghton 1994), and food shortage due to population growth (Slavin 2016) with a projected global population of 9 billion people in 2050 needing to be supplied with food (FAO 2009). Complex agricultural systems need to be better comprehended to successfully tackle these challenges (Kamilaris et al. 2017). This includes understanding agriculture as a socio-ecological system (SES) that “consists of a bio-geo-physical unit and its associated actors and institutions” (Glaser et al, 2008). Furthermore, this entails foregrounding sustainability by promoting increased diversity across farms, and by

D. S. Hensel (B) Architecture Internationalization Demonstration School, Southeast University, Nanjing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_11

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collaborating with social, agricultural and economic scientists to establish the fundamental role of ecology within food systems (Norton 2016). For urban agriculture this points towards a fundamental link with urban ecology. Big Data and related applications are gaining increasing importance in agriculture (Bronson and Knezevic 2016; Kamilaris et al. 2017) and in agro-environmental science (Lokers et al. 2016). Big Data was defined by Nessi, the European Technology Platform for Software, Services and Data, as “encompassing the use of techniques to capture, process, analyse and visualize potentially large datasets in a reasonable timeframe not accessible to standard IT technologies” (Nessi 2012). In the context of industrialised rural agriculture, a broad range of data-based technologies and methods are developed and utilized across a wide spectrum of problems and tasks. This includes, growth modelling and yield monitoring (Basso et al. 2001), the use of global navigation satellite systems (GPS) (Borgelt et al. 1996), geographic information systems (GIS) (Bill et al. 2011), remote sensing (Atzberger 2013; Khanal et al. 2020), cloud computing (Anandhi et al. 2020), cloud-based management systems (Kaloxylos et al. 2014), Internet of Things (IoT) (Castrignanó et al. 2020), information and communication technologies, as well as decision support (Perini and Susi 2004; Rose et al. 2016; Castrignanó et al. 2020; Naud et al. 2020; Zhai et al. 2020). Different data-based methods are often combined such as, for instance, assessment of land suitability potentials for agriculture using remote sensing and GIS (Bandyopadhyay et al. 2009), of GIS in combination with multi-criteria decisionmaking to assess site suitability for agriculture (Saha et al. 2021). Furthermore, agentbased models are used to support life cycle assessment for agriculture (Marvuglia et al. 2018), etc. The exchange of knowledge and approaches between rural and urban agriculture can enrich the understanding of related systems, actions and practices that can be adapted for use in different contexts. For this reason, it is useful to be aware of the development in both contexts, thereby uncovering existing or potential overlaps, exchanges, and research foci, as well as identifying research gaps in developing novel solutions to current and future challenges.

11.2 Agriculture and Big Data Recent research showed that data-intensive research plays a key role in tackling the heterogeneity of interdisciplinary approaches and data in agro-environmental research (Lokers et al. 2016). Lokers et al. outlined two distinct stages of disciplinespecific developments: (1) the development of models for knowledge derivation from available data in the period of circa 1985 to 2005; (2) the development of modelling frameworks to facilitate decision making based on information technology for rapid analysis of large amounts of data in the period of circa 2000 to 2012. According to Lokers et al. this enabled lower level technical and semantic interoperability, while reaching higher levels requires further efforts to enable interdisciplinary and more

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integrated approaches towards “more complex data-intensive processing and analysis across disciplines as required for supporting evidence-based decision making” (Lokers et al. 2016). Other recent research points towards challenges in the integration and analysis of data for the purpose of configuring useful decision support tools (Weersink et al. 2018; Dakshayini and Balaji Prabhu 2020), and analysis of the uptake of related methods and tools in agriculture for improvement of design and delivery of decision support (Rose et al. 2016). More specific developments include the broad range of data-based approaches, methods and technologies that facilitate Precision Agriculture and Smart Agriculture (SA). Precision Agriculture advances accuracy of management and operations, while SA uses smart technologies, Big Data (Wolfert et al. 2017), big data analytics and artificial intelligence (AI) to inform action on data collected in the field (CEMA 2017). SA has been defined as precision agriculture technologies aided by Big Data and AI to inform autonomous farm decisions for saving resources and increasing the quality of produce (El-Gayar and Ofiro 2020). It has been suggested that SA solves the problem of generalization, while providing autonomy for farm decisions enhanced by context, situation, and location awareness (Wolfert et al. 2014). Recently, research on climate-smart agriculture (CSA) emerged, as well as research on the use of IoT (Lakhwani et al. 2019), Artificial Intelligence (AI), sensor and robotic technologies in agriculture. CSA aims at the transformation of agricultural systems to ensure food security in the context of climate change (Lipper et al. 2014). This is often linked with sustainable intensification (SI), which aims for increased food production on existing agricultural land, while lowering environmental impact (Garnett et al. 2013). Sustainable agriculture (Senanayake 1991) has been linked with the notion of smart farming (Bongiovanni and Lowenberg-Deboer 2004). Kamilaris et al. (2017) elaborated that smart farming can be understood as an ecosystem-based approach to agriculture. Furthermore, Big Data also plays an increasing role in agri-food-supply chains (AFSCs) (Rejeb et al. 2021). These approaches and methods are applied in different combinations to both large scale and small-scale agriculture. CSA, for instance, is applied in small scale farming (Abegunde et al. 2019; Mizik 2021), and some research focuses on applying smart agriculture principles and technologies to small scale green-house farming (Rubanga et al. 2019).

11.3 Urban Agriculture and Big Data Urban agriculture (UA) comprises of different types of food cultivation and animal farming in urban and peri-urban areas that ranges from small plots or household level to commercialized agriculture (Tornaghi 2014). Proksch stated that UA challenges the involved experts and stakeholders “to develop new ways to approach sustainability, community integration, and visions for urban futures” (Proksch 2017). A key characteristic of UA is that it is locally varied and diverse based on specific socioeconomic and socio-ecological conditions (Clinton et al. 2018). This diversity does

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not only derive from differences in urban contexts and local climate, but also from the needs and resources of local stakeholders, and existing and non-existing linkages between urban governance, urban planning, and locally specific types of UA. In the context of UA some research efforts focus on analysing the extent and impact of urban agriculture globally, regionally, and locally. Clinton et al., for instance, undertook global geospatial ecosystem services estimate of urban agriculture, based on a quantitative framework for “assessing global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data-driven estimates of urban morphology and vacant land” (Clinton et al. 2018). To obtain such global scale estimations, the study combined data from the Food and Agriculture Organization of the United Nations and Google Earth Engine. The comparison of the country-specific findings showed considerable variation in UA-based ecosystem services and food yield (Clinton et al. 2018), which derives from the differences between location-specific socio-economic and socio-ecological systems and conditions. Various research efforts exist that focus on developing decision support for design and operation of sustainable urban farming systems. Some of these efforts focus on stakeholder decision support concerning quantitative assessment and optimization of investment and operation of urban farming systems (Li et al. 2020). Contractor et al. pursued the development of a prototype decision support and planning tool that enables feasibility assessment of rooftops for urban farming, locally specific rooftop farm design and crop selection, as well as crop yield prediction (Contractor et al. 2020). Other recent works focus on developing approaches to performance-based design and employ parametric modelling and simulation tools linked with decision support. One current focus is UA in and on buildings including rooftop gardens and greenhouses, and indoor farms (Specht et al. 2014) that feature varying degrees of control and technology support. In this context Benis et al. target the development of simulation-based decision support for implementing Building-Integrated Agriculture (BIA) in urban contexts with the aim to maximize crop yields while minimizing water and energy consumption (Benis et al. 2017). BIA frequently involves knowledge and technology adaptation and transfer from controlled environment agriculture (CEA) facilities to UA (Despommier 2011; Ting et al. 2016; Shamshiri et al. 2018). In this context data-related technologies frequently play a key role. In the context of lesser controlled exterior UA systems Podder et al. presented research on IoT based systems for monitoring and management of UA related microclimatic data, including humidity, temperature, and soil moisture, to govern irrigation measures (Podder et al. 2021). This shows that research on formal and informal UA systems exists, including solutions that are technology-driven to varying extent.

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11.4 Linking Food Production, Ecosystem Restoration and Construction Lokers et al. pointed out that societal challenges such as food security, ecosystem restoration and climate change “require more and more complex approaches in terms of combining cross-sectoral and cross-disciplinary knowledge, information and data” (Lokers et al. 2016). This can be extended by way of co-relating different challenges such as, for instance, food security, ecosystem restoration and necessary construction, and that pursue a synthesized approach to tackling these challenges. The following elaborates a related research gap and approach. Current UA focused research does not adequately include deriving novel UA systems from targeted research into adapting traditional rural agricultural systems that successfully combine food production, ecosystem restoration, and construction. Such research needs to include a systemic analysis of existing green construction (GC) types. Some types of GC facilitate intensive agriculture, while requiring high external input and maintenance. These tend towards fully closed systems of food production that are frequently decoupled from the natural environment. Other types of GC enable extensive horticulture with either considerable or little external input. These types often provide ecosystem functions to a lesser extent. In contrast, historical agricultural constructions often enable extensive yet sustainable cultivation in challenging environments, thereby providing solutions that work with local climatic and ecological conditions, and available materials and renewable resources, and commonly do not depend on external input, such as electrical energy. Such systems range in scale from provisions for single plants, to extensive farming at the territorial scale. Recent research focuses on these types of systems in search for ecological prototypes that constitute next generation GC, which incorporate context-specific design, construction and practices that can simultaneously enable integrated landuse intensification and land restoration to reconcile seemingly divergent needs and goals. Ecological prototypes are integrated and adaptive systems of design, construction, and practices, that link architecture, agriculture, landscape, and ecology, and that seek to support ecosystems and the delivery of ecosystem services, especially in environmentally degraded peri-urban and urban contexts (Sunguro˘glu Hensel 2020, 2021, 2022). The related discovery, recovery and adaptation of land knowledge are key aspects of ecological prototypes research. Land knowledge recovery is not a trivial task as such systems are frequently complex, insufficiently documented and not always easily accessible. Furthermore, the solutions found in history are responses to particular environmental circumstances and needs of their time and therefore are difficult to assess from today’s perspective, especially when historical data is sparse. This type of study demands interdisciplinary and transdisciplinary expertise involving a range of knowledge fields and disciplines, as well as local practitioners such as farmers. From a design perspective, adaptation concerns the modification of constructions and practices as part of a resilience strategy to maintain and improve productivity in a changing environmental, land use and technological context. In addition, adaptation

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may involve utilising ideas gleaned from the past to better understand contemporary challenges as well as solving new problems through learning and history-inspired innovation. Preservation, recovery, adaptation and discovery of land knowledge relies to a considerable extent on data. Furthermore, modelling and analytical approaches must balance simplification with the complexity that characterises rural and urban agriculture. Traditional approaches evolved and accumulated over time through trial-anderror, and traditions passed down through generations, generating, amassing and exploiting traditional ecological knowledge (TEK). Recovering this land knowledge is not only useful for adaptive management of productive landscape, but also for adaptation and transfer of such knowledge for use in different context, such as cities. Consequentially, what is required is knowledge recovery across a broad range of cases. Such systems can provide valuable insights for transitioning from resource-intensive to sustainable farming and land use. To make this line of argumentation more tangible it is useful to examine some examples of traditional agricultural systems that offer interesting cases for study and recovery and adaptation of land knowledge, for instance for use in UA. One of these systems are terraced landscapes. Terracing is one of the most widespread anthropogenic modifications of terrestrial landscapes for agricultural use in different climate zones. Terraces transform sloped terrain into stepped horizontal land for cultivation thereby expending high-quality croplands, and provide mitigation of flood risks, prevention of landslides, conservation of soil, water management, as well as restoration of degraded habitats. Recent studies showed that extensive research is necessary to better understand the role of terraces in improving ecosystem services and preventing land-degradation, especially since terracing methods and terraces vary greatly between different contexts thereby resulting in significant variability in the provision of ecosystem services (Wei et al. 2016). In order to establish an approach to recovery of related land knowledge an ongoing research effort focuses on highaltitude terraced vineyards in Lamole, Tuscany (Hensel et al. 2018). Terraced vineyards involves construction that combines the modification of terrain and building of dry-stone walls, and specific related agricultural and management practices of plant manipulation. Terracing in conjunction with dry-stone walls provides flat terrain for planting crops, mitigation of landslides and soil erosion, effective water management and advantageous modulation of microclimate. Due to land abandonment this type of viticulture diminished during the second half of the twentieth century accelerated by industrialization of agriculture. In Lamole and elsewhere the system of terracing, dry stone walls, planting wine along height lines, the pruning of separated and genetically individual plants was almost entirely replaced by planting rows of cloned vine plants uphill on sloped hillsides rather than terraces. However, the search for ways of improving the quality of wine led to a re-emerging interest in traditional methods of terracing. In consequence, an increasing number of farmers have embraced the reconstruction of terraced vineyards. Still, it is necessary to adapt the historical system of terraced vineyards to new technical requirements, for instance, changing the distance between rows of vine plants to permit the use of a small tractor for the purpose of tilling and harvesting, as was the case in Lamole. Also, traditional

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methods included other plants that were grown between the vine plants to improve on the quality of soil and to add food sources for human and animals (i.e., through incorporating legumes), thus promoting biodiversity in terraced vineyards. Furthermore, it is necessary to understand the impact of adjacent natural and managed forest on the microclimate of the mosaic of small, terraced vineyards. Currently, the research includes numerous surveys and data acquisition on the territorial scale, the scale of individual vineyards and the scale of individual vineyard features, as well as correlation and analysis of the data obtained from surveys and simulations (Tyc et al. 2021). Based on this, further research efforts are under way that focus on developing a data-driven and performance-oriented computational design framework for such terraced vineyards, as well as targeted decision support. Another example that is currently under study are fruit walls and walled gardens. Fruit walls are commonly linked with specific plant manipulation methods. One key example is the espalier, a horticultural practice that consists of pruning fruit bearing trees and other woody plants and training them on wooden trellis for vertical growth along a fruit wall. Such fruit walls are frequently a part of walled gardens, with historical examples of extensive use of fruit walls and walled gardens over large areas. One prominent example are the walls for vine cultivation in Thomery, near Paris in France, with its unique system of training vine upon trellises (Du Breuil 1876). This method of cultivation served to overcome disadvantageous orientation and poor soil conditions. Full South orientation for maximum solar gain was not possible as the plants required protection from the damp southerly winds. In the 1920s there existed ca. 350 km of walls these walls in Thomery, which were generally 3 m tall and up to 100 m long, running parallel at ca. 9 m. Vines were only grown on the side of the wall that had solar exposure. The Thomery method and quality of wine was described in detail by various sources (Phin 1862). A second example of large-scale applications of walled gardens in an urban area are the peach orchards of Montreuil. In 1907 these orchards covered an area of 300 hectares. While these walls might first have emerged to demarcate plots, it soon transpired that the micro-climatic impact of the walls on growing peaches was advantageous and soon the walls were oriented in such way as to utilize south exposure, creating a micro-climate close to the walls that was up to 10 degrees centigrade higher than the ambient temperature of the surrounding. The large-scale application of fruit walls shows how thermal mass can be advantageous for the purpose of growing fruit, vine, etc. for the purpose of providing food to larger populations. As such, these systems could be integrated with other needs for construction. One specific type of fruit walls are the so-called Talut walls that features a small cantilevering roof to protect the espalier. For further protection, sheets of glass were mounted in front of the plants. Today, talut walls can still be found in some places, such as the vineyard Krapenberg in Radebeul, and in the garden of palaces such as Sanssouci in Potsdam and Herrenhausen in Hannover. Literature from the end of the nineteenth century shows the rich diversity of talut walls with and without additional equipment such as heating (Tatter 1879). A similar development are the so-called hot walls that emerged in Great Britain. Hot walls feature embedded heating in the form of a system chimneys and flues and even hot-water pipes that make it possible to

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provide required temperatures for native and non-native species (Hall 1989). Together with the strategy of leaning sheets of glass against fruit walls, to increase their thermal effect, the heated walls marked a departure towards energy-based solutions and full enclosure that led to the development of the modern glass greenhouse. Extensive surveys are in planning for selected cases of fruit walls and walled gardens to collect data to better understand the specific ranges of their microclimatic and other performance characteristics. Clearly, these different case studies are context specific. Research is needed to establish to which extent these can be generalized and adapted to different and changing conditions, needs and challenges, and contexts. For this reason, it is vital to specify these possible ranges and to configure targeted decision support for adaptation of such traditional agricultural systems for use in urban agriculture (Sunguro˘glu Hensel 2020). Knowledge-based decision support systems (KDSS) can play a key role in supporting adaptation and utilization of land knowledge. In this context multi-criteria and multi-scale decision methods entail identification of feasible key solutions and possible design pathways for their adaptation to given problems. The KDSS can combine data mining and machine learning, database, ontology and optimization approaches in an agent-based modelling environment. In that way, it can, for example, serve to collect and analyse data to identify solutions and can be computerized or operated by humans. At any rate, DSS can deliver a vital resource for tackling the involved complexity and large datasets derived from surveys, simulations and analyses (Sprague 1980). As shown above agricultural decision support systems exist, especially in the areas of precision and automated farming. However, decision support tailored for adaptation, both in terms of supporting the continuous adjustment of a system in response to change, as well as the transfer of knowledge to new contexts, is sparse. Decision support is especially important in cases where decisions are based on traditional and local knowledge, or a systematic framework is needed to make land knowledge accessible for adaptability to changing environments and different objectives and contexts. In this context, transdisciplinary research and a substantial number of interviews with experts will need to be conducted in parallel to multi-modal data acquisition and analysis to capture aspects of practical knowledge that cannot be obtained through other means.

11.5 Conclusion Big Data, data-related methods and decision support in rural and urban agriculture, as well as in agro-environmental research, are developing fast and have begun to play a key role in tackling current challenges to food production. Further efforts are required to map these developments, and to compare and cross-inform advances in agro-environmental research and rural and urban agriculture. This includes understanding agriculture as a socio-ecological system, thereby placing strong emphasis on sustainability. This needs to be underpinned by advancing the understanding of the connection between the different sustainable development goals of the United

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Nations (SDGs), including zero hunger (SDG2), good health and well-being (SDG3), clean water and sanitation (SDG6), sustainable cities and communities (SDG11), responsible consumption and production (SDG12), climate action (SDG13), and life on land (SDG15), as well as linkages to other SDGs via the respective sub-goals. This can be addressed through inclusive approaches to compound sustainability problems that bear upon and can benefit from novel approaches to UA. In this context it is useful to consider the linkages of UA to urban microclimate, urban ecology, urban forestry, and broadly to human health and well-being. Further inter- and transdisciplinary research efforts need to focus on the complex interactions and dynamics that can promote or inhibit the development of UA through new types of land use and novel green construction and architectural typologies. As briefly shown above, Big Data and data-related methods already play a central role in such endeavors and especially when accompanied by targeted decision support, especially for the puspose of linking food production, ecosystem restoration and construction. For this reason, it will be useful to recover or discover and adapt land knowledge and transfer knowledge between rural and urban agriculture (Taylor 2017).

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Defne Sunguroglu Hensel (AA Dipl RIBA II AA EmTech Ph.D.) is an architect, partner in the practice OCEAN Architecture|Environment and OCEAN net, and founding and steering member and coordinating manager of LamoLab Research Centre. She is Associate Professor of urban ecology and landscape architecture at the Architecture Internationalization Demonstration School, Southeast University of Nanjing, China. Post-doctoral researcher at Technical University Munich in the context of the H2020 FET Open project ECOLOPES in work-package WP 4—Data Acquisition and Information Modelling. Her work focuses on topics spanning ecological architecture and urbanization, green construction, data-driven design, knowledge-based decision support systems, urban agriculture. Previously university lecturer and post-doctoral researcher at Vienna University of Technology in the Special Research Area Advanced Computational Design in the context of the Centre for Geometry and Computational Design.

Correction to: From Amsterdam to New Amsterdam to Amsterdam: How Urban Mobility Shapes Cities Tom Benson , Fabio Duarte , and Carlo Ratti

Correction to: Chapter 7 in: A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_7 The original version of the chapter has been updated for the reference: Benson T, Duarte F (2022) Snseable Cities. Nationellt möte 2020 Tillämpad stadsbyggnad Kris och transformation. Corrected version is: Benson T, Duarte F (2020) Senseable Cities. Nationellt möte 2020 Tillämpad stadsbyggnad Kris och transformation.

The updated version of this chapter can be found at https://doi.org/10.1007/978-3-031-03803-7_7

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Chokhachian et al. (eds.), Informed Urban Environments, The Urban Book Series, https://doi.org/10.1007/978-3-031-03803-7_12

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