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Table of contents :
Dedication
Foreword
Preface
Acknowledgements
Contents
1 Urbanization in India
2 Slums in India
3 Case Study: Kalaburagi
4 Slum Identification and Validation
5 Slum Modeling for Growth Prediction
6 Slum Housing Demand Assessment and Analysis
7 Slum Development Programs—An Overview
8 Slum-Spatial Decision Support System
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The Urban Book Series

Sulochana Shekhar

Slum Development in India A Study of Slums in Kalaburagi

The Urban Book Series Editorial Board Fatemeh Farnaz Arefian, University of Newcastle, Singapore, Singapore; Silk Cities & Bartlett Development Planning Unit, UCL, 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 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 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 Jeremy W. R. Whitehand, Earth & Environmental Sciences, University of Birmingham, Birmingham, UK Claudia Yamu, Department of Spatial Planning and Environment, University of Groningen, Groningen, Groningen, The Netherlands

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. Now Indexed by Scopus!

More information about this series at http://www.springer.com/series/14773

Sulochana Shekhar

Slum Development in India A Study of Slums in Kalaburagi

Sulochana Shekhar Department of Geography, School of Earth Sciences Central University of Tamil Nadu Thiruvarur, Tamil Nadu, India

ISSN 2365-757X ISSN 2365-7588 (electronic) The Urban Book Series ISBN 978-3-030-72291-3 ISBN 978-3-030-72292-0 (eBook) https://doi.org/10.1007/978-3-030-72292-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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

Dedicated to, My Family

Foreword

I’ve known Dr. Sulochana Shekhar for a very long time now and she, in fact, began her journey into the Geospatial Sciences at the Indian Institute of Remote Sensing when I was heading the Geoinformatics Division. Even though she had started her career as a pure geographer, she was quick to realise the potential of Remote Sensing and Geographical Information System. Since then, it’s with great pride that I’ve seen her grow into one of the leading urban geographers of the nation and apply GIS in a very efficient and pragmatic manner and mentored her whenever she’s been in need of expert support. She is now well known for taking up new initiatives and challenges. The latest of these endeavours is her excellent book, “Slum development in India—A study of slums in Kalaburagi”. Arriving at a time when the Government of India is promoting the use of space technology and geospatial tools for urban infrastructure developmental schemes such as the PMAY, Smart City project and many programs that are underway. A lot of research work has been done in India on slums, utilizing a range of methodologies to identify slums from satellite data. They’ve focused their attention on creating rule sets that are transferable and scalable and have achieved these goals to varying extents. What Dr. Sulochana brings to the field is her use of the same data to create a decision support system which moves her research from observation to action. What follows is an accessible introduction to her work and methodologies which should surely be of aid to anyone who seeks to work towards improving the state of slums and is willing to harness the latest that science and technology has to offer to do so. The book quite nicely brings out all the aspects of slums including the present condition of slum populations, how they can benefit from the use of space technology and geospatial tools for sustainable development and this is supplemented well by the case study demonstration of Kalaburagi. I am sure that this book will be a very good reference material for urban planners, developers, urban departments at the national and state level, urban local bodies, researchers and also will be of use in education and training programs.

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Foreword

I wish the publication all success and hope it will find its way to libraries of academic institutions and bookshelves as a good reference material. Shillong, Meghalaya October 2020

P. L. N. Raju Director North Eastern Space Applications Centre

Preface

The first seed for the book that you’re now holding in your hands was planted during my Ph.D. years. My thesis was about monitoring the urban sprawl in the Indian city of Pune and collecting data for it required me to visit a lot of places in and around the city. Quite a few of these locations were slums and that’s how I first came to see them and the living conditions prevalent in them. While pursuing my M.Sc. in Geoinformation Science and Systems, I chose “Applying GIS and RS for Modelling the Growth of Slums in Pune City” as the topic for my dissertation. Needless to say, this meant working closely with the slums of Pune which further enhanced my understanding of their situation and created the desire to work towards improving their situation. I visited ITC, University of Twente, The Netherlands, for my Postdoctoral Fellowship where I worked on slum extraction from Very High-Resolution Satellite Data. I felt empowered by this knowledge and was eager to translate it into action. Upon returning to India, I moved to Kalaburagi to work as an Associate Professor at the Central University of Karnataka. When HUDCO (Housing and Urban Development Corporation) invited applications from academicians for its first-ever research project into understanding why the numerous slum development programs that had been implemented in India had never been very successful, I submitted my proposal. My proposal was accepted and I undertook my project, “Application of Geoinformatics in Housing the Urban Poor—A Study on Slums of Kalaburagi”. My research revolved around the identification of slums from spatial data and offering recommendations for ideal locations for affordable housing that would deter future slum growth and formation. The insights gleaned from this work were received well by my sponsors but I felt like it hadn’t managed to reach as wide an audience as it needed to in order to effect change. This is when I decided to bring out the results of my work along with the methodology and techniques that made it possible, in the form of a book. A book that I hope will be of aid to planners, policymakers, and administrators by providing them with a basic methodology for identifying slums from remote sensing data, building models to understand future slum growth, and developing a SDSS for better implementation of slum development projects. ix

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Preface

This book can serve as a handy reference for all those who want to work on slums and also act as a handbook of best practices. I sincerely hope this book can meet these goals or at the very least be of some use in understanding slums in the twenty-first century and enable you to work towards their betterment. Thiruvarur, India

Sulochana Shekhar

Acknowledgements

This book owes its existence to the efforts, support, and sacrifices of numerous people and it would be remiss of me to not express my sincerest gratitude to each and every one of them. To begin with, I would like to thank HUDCO for the opportunity to work on a project of this scale and ambition and to the experts from HUDCO for their support throughout the process. This book presents the results and insights gleaned from the project and is thus a direct successor to it. I would also like to thank Dr. Anil Gandhi and Dr. Deepak Kumar along with all my other students who participated in the project work and sacrificed their own comfort for my convenience. The faculty and staff of the School of Earth Sciences, Central University of Karnataka played a crucial role in carrying out the project work, lending their support, and also lending the required equipment and materials. The feedback offered by Prof. Mahavir and the other members of my project presentation panel were of great help in refining and improving the quality of my work and I am grateful for the same. The staff of the School of Earth Sciences, Central University of Tamil Nadu, especially my assistant R. Maheshwari was of great help in laying all the finishing touches upon the book at a time when I was nearly overwhelmed by all the fine work that goes into the final submission of the manuscript. I would also like to place on record my gratitude and heartfelt wishes to the residents of Brahmpur, Vijay Nagar, and Bhorabhai Nagar slums for their cooperation and participation in this process. They took time out of their busy schedules and life for me and I can’t put into words how much that means to me. I hope this book and my work can make some small contribution in improving their lives and the lives of others like them across the globe. Last but not least are my family. My greatest support and a pillar of strength to the entire family, my husband Raajashekhar is the one who motivated me throughout the process of conceptualizing and realizing this book. My children, Vasu and Vaibu were understanding about the time I’d have to sacrifice to dedicate to this book and worked their own schedules around it to make sure I had time for the love and affection that powered me through any self-doubt or tiredness that could have kept this book from ever being completed. xi

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Acknowledgements

If I’ve failed to mention to anyone who belongs here, then I add my apologies and my gratitude to them. My ultimate thanks go to the almighty, whose blessings lie behind all my endeavors.

Contents

1 Urbanization in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Urban Growth (1901–2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Distribution of the Urban Population in India (2011 Census) . . . . . . 1.4 Urban Problems in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Sustainable Cities and Indian Urbanization . . . . . . . . . . . . . . . . . . . . . 1.6 Relevance for This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Organization of the Chapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 3 7 12 14 17 18 19

2 Slums in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Slum Census in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Slum Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Mapping of Slums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Slum Mapping in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Spatial Information and Slums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Remotely Sensed Slums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 22 23 27 30 31 33 35 42

3 Case Study: Kalaburagi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Introduction to Kalaburagi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Slum Situation in Kalaburagi City . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 47 50 65 65

4 Slum Identification and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Understanding Slum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Overview of Ontology and Approaches to Building It . . . . . . . . . . . . 4.2.1 Existing Approaches to Building Ontologies . . . . . . . . . . . . . 4.3 Slum Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 67 69 70 71 xiii

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Contents

4.3.1 Building Slum Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Slum Identification Through Ontology Approach . . . . . . . . . . . . . . . . 4.4.1 Slum Map Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Validation using Enumeration Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Applicability in the Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 77 78 79 80 89

5 Slum Modeling for Growth Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.2 Slum Models: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.3 Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.3.1 Conceptualizing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.3.2 Building the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.3.3 Model Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.4 Future Growth of Slums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 6 Slum Housing Demand Assessment and Analysis . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Estimating Kalaburagi City Population . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Projecting Slum Population of Kalaburagi City . . . . . . . . . . . . . . . . . 6.4 Framework for Estimating the Housing Demand of Urban Poor . . . 6.4.1 Demand Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Analytical Framework for Housing Demands: A Base for Estimation and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Sample Estimation of Housing Demands for Vijayanagar Area . . . . 6.7 Suitable Sites for Rehabilitation Program . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 116 116 118 124 125 125 128 129 132

7 Slum Development Programs—An Overview . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Slum Policies in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Review of Slum Developmental Programs . . . . . . . . . . . . . . . . . . . . . 7.4 Slum Development Programs at Kalaburagi . . . . . . . . . . . . . . . . . . . . 7.5 Suggestions for Intervention in Policies . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

135 136 136 139 141 144 157

8 Slum-Spatial Decision Support System . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Review on Spatial Decision Support System . . . . . . . . . . . . . . . . . . . . 8.3 Decision-Making Process in Slum Development . . . . . . . . . . . . . . . . 8.4 Spatial Decision Support System (SDSS) Concepts . . . . . . . . . . . . . . 8.4.1 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Designing SDSS for Slum Development . . . . . . . . . . . . . . . . . . . . . . . 8.6 Building SDSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Details of Slum-SDSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7.1 Database Management System . . . . . . . . . . . . . . . . . . . . . . . . .

159 160 161 162 162 163 165 166 167 169

Contents

8.7.2 Model Base Management System (MBMS) . . . . . . . . . . . . . . 8.8 Selection of Slum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8.1 Selection of Households and Housing Structure . . . . . . . . . . 8.9 Closing Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

169 171 175 177 177

Abbreviations

AKM APMC BSUP CA CBD CBO DBMS EB EGM EO ER ESRI EWS GDP GIS GLCM GOI GPS HUDCO IEEE IHSDP JNNURM LIG MBMS MCE MHUPA MLA MP NDVI NGO NSDP NSSO

Asha Kirana Mahithi Agricultural Product Market committee Basic Services to Urban Poor Cellular Automata Central Business District Community Based Organization Database Management System Enumeration Block Expert Group Meeting Earth Observation Entity Relationship Model Environmental System Research Institute Economically weaker section Gross Domestic Product Geographic Information Science/System Grey Level Co-occurrence Matrix Government of India Global Positioning System Housing and Urban Development Cooperation Institute of Electrical and Electronics Engineers Integrated Housing and Slum Development Programs Jawaharlal Nehru National Urban Renewal Mission Low income group Model Building Management System Multi Criteria Evaluation Ministry of Housing and Urban Poverty Alleviation Member of Legislative Assembly Member of Parliament Normalized Difference Vegetation Index Non-Governmental Organization National Slum Development Programme National Sample Survey Office xvii

xviii

NUHHP OBIA OOA ORGI PMAY QGIS RAY SDF SDGs SDSS SRA SVM UBSP UFS ULBs UNDP UN-WUP UTs VAMBAY VHR

Abbreviations

National Urban Housing and Habitat Policy Object Based Image Analysis Object Oriented Analysis Office of the Registrar General and Census Commissioner of India Prime Minister Awas Yojana Quantum GIS Rajiv Awas Yojana Slum Dwellers Federation Sustainable Development Goals Spatial Decision Support System Slum Rehabilitation Authority Support Vector Machine Urban Basic Services for the Poor Urban Frame Survey Urban Local Bodies United Nations Development Programme United Nations World Urban Prospects Union territories Valmiki Ambedkar Awas Yojana Very High Resolution

Chapter 1

Urbanization in India

Today’s towns are tomorrow’s cities. Today’s cities are the future of mankind. —Unknown.

Abstract In terms of population size, the urban agglomerations of India are the second-largest in the world, ranking behind only China. The Indian urban population has steadily increased since independence with an urban growth rate (31.8%) that is higher than the average population growth rate (17.64%). Industrialization, planned development and globalization are the major factors that induced urban development in India. The urban population undergoes the natural increase expected of any population and this increase is further augmented by in-migration. The lopsided growth of the urban population has resulted in a “top-heavy” urban system where Class-I cities and million-plus cities, which are crowded to begin with, are getting even more crowded instead of this population increase being distributed across other cities and towns. The level of urbanization is uneven among the states and UTs with even intrastate variations. The census data are considered a reliable source of information yet recent studies show that the urban population is underestimated in India. Given the persistent growth of the population in urban areas, slums are one of the major problems of urban India. The housing problem will further deteriorate the slums unless intensive measures are taken to upgrade the living conditions of the slum dwellers. The first chapter insists on a detailed study on slums and their problems so that they will be included in city planning and urges for sustainable measures to prevent the future formation of slums. Keywords Urbanization · Urban growth rate · Megacity · Million-plus city · Sustainable cities · Slums

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_1

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1 Urbanization in India

1.1 Introduction Urbanization in India has its origins in the ancient Indus Valley civilization. It has undergone a long journey and carries the footprints of the historical, medieval, colonial, and modern periods. Each period gave birth to many cities and helped in their flourishment even as others disappeared entirely. Each city had its own identity, such as the administrative capitals of Indraprastha and Pataliputra; educational towns of Nalanda and Takshashila; religious centers such as Varanasi, Madurai, and Gaya; port cities such as Mumbai, Kolkata, and Chennai, etc., during this journey. Some cities developed as military garrison towns and some arose as transport towns. Major religious centers have now turned into pilgrimage towns. Many of the major cities of India continue to wear these historical identities even today. When you enter one of these major cities today such as Delhi, Pune, Jaipur, or Mysuru, you will find majestic ancient forts and palaces at their very heart. Meanwhile, cities like Mumbai and Chennai still hold the remnants of colonization. One can run into crowded traditional market areas of indigenous origin in the streets of Hyderabad, Kochi, or Lucknow that coexist with the ultra-modern multiplexes and huge shopping malls in the same city. One can enjoy the planned civil lines and state-of-the-art public transportation on one end and immerse themselves in the beauty and serenity of extended agricultural fields on the urban fringe that forms the other end of the city. These mixed characteristics are common in many Indian cities. Cities have different functions to perform, which vary from administration, trade and commerce, industry, education, recreation, health services, transportation, and defense. Sometimes all these tasks are performed by a single city. The impact of globalization can be seen in most cities now, turning many into global cities of the world. Cities have different origins. Some are riverine like Delhi and Ahmedabad; some are coastal like Mumbai and Chennai, while some are located in close vicinity to deserts like Jodhpur, and some others are located in high altitudes with severe snowfalls in the winter like the Himalayan hill stations. Most of these cities in India grew without prior planning and underwent organic growth. There are, however, a few planned cities such as Chandigarh, Gandhinagar, Bhubaneshwar, and recently Amaravati. As the world undergoes a rapid urbanization process, India is also slowly and steadily transforming from a primarily rural to an urban country. The rate of change while initially steady was stimulated in the latter part of the twentieth century. Independence too resulted in a significant boost with a large number of migrants due to the partition. During the 1950s and 1960s, the national plans and industrial development encouraged urban growth. This along with social factors, globalization, liberalization, and the IT revolution pushed urban growth in the 1990s and 2000s. The gradual increase of the urban population brought both positive and negative impacts on India. The economic development, increase in GDP, infrastructure development, increased purchasing power, and high standard of living were

1.1 Introduction

3

the gifts of increasing urbanization. Cities grew geographically and demographically. The horizontal expansion of cities took place when the built-up area sprawled beyond their defined boundaries. The population of the city increased naturally but most of the increase can be attributed to “in-migration”. As cities expanded, rural communities moved to cities. It also created overcrowded, congested, polluted cities with an increasing gap between the rich and the poor. Millions of people live in unhealthy conditions and without any basic infrastructure. They form the urban poor. Thus, sprawled messy development along with hidden slums became the general characteristics of most Indian cities. In the present chapter, we will see the growth of urbanization in India from 1901 to 2011, and the level of urbanization as per the 2011 census. The distribution of towns and their population among the states/UTs is also discussed here. Understanding the process of urbanization and its consequences is essential to understanding the problems of slum development in India.

1.2 Urban Growth (1901–2011) Urban growth means either an increase in the size of the urban area or an increase in the number of people living in the urban area. India had a traditionally agrarian background dominated by its rural population. Even today nearly two-thirds of the Indian population lives in villages. In terms of population size, India ranks second in the world with its large rural and urban agglomerations while China is the first. As per the census definition, if an area has more than 5,000 population, has a density of 400 persons per square kilometer, and 75% of the male working population is engaged in non-agricultural activities, it will be considered as an urban area. This definition has been followed since the 1961 census and the urban areas identified based on the above criteria are known as “census towns”. In addition to this, there are statutory towns, which are defined as an area with a municipality, corporation, cantonment board or notified town area committee, etc. When we talk about the urban population growth in the early part of the twentieth century, in between 1901 and 1911, there was only an increase of 0.36% and only one city had a population larger than one million (Kolkata with 1.52 million people) along with 24 cities that had a population larger than 0.1 million. Between 1930 and 1940, the colonial impact, industrial developments, and the aftermath of the independence in the form of the partition brought a large volume of migrants. Between 1950 and 1960, planned development aided the growth of the urban population and the urban population began to rise. The growth rate was 41.4% in 1951, and there were 5 million-plus cities, along with 76 cities which had a population of more than 0.1 million. After 1951, urban growth saw some fluctuation and showed little increase in 1981 with a growth rate of 44.51%. From 1981, the urban growth rate began to slowdown and the overall population growth rate also began to show a declining trend (Fig. 1.1)

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1 Urbanization in India

Fig. 1.1 Growth of urban population in India (1901 to 2011). Source A-4 Towns and Urban Agglomerations Classified by Population Size Class In 2011 With Variation Since 1901, https://cen susindia.gov.in/2011census/PCA/A4.html

due to population control measures. Despite the declining growth rate, the urban population had increased by more than five times from 1951 to 2011. As per the census of India, urban areas are now classified into six classes based on the size of the population. Class-I cities have more than 0.1 million population. That is followed by Class-II cities with 50,000 to 99,999 population, Class-III with 20,000 to 49,999 population, Class-IV with 10,000 to 19,999 population, Class-V with 5000 to 9999 population and Class-VI which has less than 5,000 population. In general, an urban area with more than 0.1 million people is a “city” and an urban area that has less than 1,00,000 population is known as a “town” (Ramachandran 2007). The following Fig. 1.2 shows the urban growth from 1901 among these six classes of cities and towns. Accordingly, the number of class-I cities has seen a gradual increase from 76 to 393 between 1951 and 2001. Class-II towns followed a similar trend and increased from 43 in 1901 to 474 in 2011. The number of towns classified as Class-III and Class-IV showed a steady increase from 1901 to 2011. Class-III increased from 130 in 1901 to 1373 in 2011 and Class-IV increased from 391 in 1901 to 1683 towns in 2011. After independence, because of the development of secondary and tertiary economic activities, Class-I cities and other towns up to Class-IV showed a continuous increase. Class-V meanwhile showed a different trend, initially seeing an increase but also undergoing a slight decrease in between 1961 and 1991. At the same time, Class-VI towns showed a downward trend from 1951 onwards, dropping from 569 towns in 1951 to 424 towns in 2011. Despite this growth and notable changes in the number of towns in the different classes, the matter did not attract the attention of urban planners. They were not concerned about the biased urban development that was “top-heavy” (Shaban et al. 2020). This means that Class-I cities are getting overcrowded with an ever-increasing population when compared to towns of other classes (Fig. 1.3).

1.2 Urban Growth (1901–2011)

5

Fig. 1.2 Growth of cities/towns under various classes from 1901. Source A-4 Towns and Urban Agglomerations Classified by Population Size Class In 2011 With Variation Since 1901, https://cen susindia.gov.in/2011census/PCA/A4.html

Fig. 1.3 Population living in class-I cities and other towns from 1901

From this figure 1.3, it is obvious that after 1951, there is a continuous increase in the urban population living in Class-I cities and a steady decline in other towns. This is mainly because of rural to urban migration and a natural increase (crude birth rate is higher than the crude death rate). Another concern of Indian urbanization is its spatial expansion. The total urban area has increased since 1961 (Table 1.1). Even as Class-I cities, metropolitan cities, and megacities are struggling with “top-heavy” urbanization, the second-order and medium cities are also facing the pressure of population growth and unplanned spatial expansion (Shaban et al. 2020).

6 Table 1.1 Increasing urban area

1 Urbanization in India Year

Area in km2

1961

38509.28

1971

43422.24

1981

52390.63

1991

63832.10

2001

78199.42

2011

102252.00

Source A-4 Towns and Urban Agglomerations Classified by Population Size Class in 2011 with variation since 1901

At the global level, most cities expanded their land cover by a factor of 16 in the twentieth century. With the present growth rate, it is estimated that within 43 years, the world’s urban population will double but that the built-up land will double in 19 years and it will triple in the cities of developing countries (Angel et al. 2011). This increase in built-up area was made possible by the conversion of vast amounts of open lands, cultivable land, and seasonal water bodies such as lakes and ponds. Wetlands and river banks have also been transformed for urban land use. This has increased environmental degradation and resulted in the urban heat island problem in many cities, particularly in million-plus cities. The million-plus cities are urban agglomerations having a population greater than one million that includes two or more physically contiguous towns (at least one statutory town) together, with or without outgrowths of such towns (Census 2011). The following table (Table 1.2) gives details of the growth and development of million-plus cities in India from 1901 to 2011. These 53 million-plus cities are distributed across 16 states and one UT in India. Out of these sixteen states, Kerala, Chhattisgarh, and Jammu, and Kashmir along with the union territory of Chandigarh are the latest additions. Kerala has suddenly emerged between 2001 and 2011 as an urbanized state with seven million-plus cities. The role of the site and situation can be seen in the growth and development of million-plus cities. When we overlay the locations of these million-plus cities, (Fig. 1.4) one can understand the impact of physical factors (site) on the distribution of these cities. The concentration of many cities shows favorable economic, social, and political conditions (situation) for their development. The coastal plains and north Indian plains have a greater number of million-plus cities than the northern and north-eastern states that are located in hilly terrain. Himachal Pradesh, Uttarakhand, Haryana, Odisha, and the north-eastern states are yet to even enter the list. NASA scientists use nighttime satellite images to study changes in the degree of urbanization, migration, electrical services expansion, etc. We can see a notable change in the lights in the northern part of India in the given images (Fig. 1.5) of 2012 and 2019.

1.3 Distribution of the Urban Population in India (2011 Census)

7

Table 1.2 Origin and growth of million plus cities in India from 1901 Year

No of cities Million-plus cities (Cities/UAs having more than 1 million population)

1901

1

Kolkata

1911

2

Kolkata, Mumbai

1921

2

Kolkata, Mumbai

1931

2

Kolkata, Mumbai

1941

2

Kolkata, Mumbai

1951

5

Kolkata, Mumbai, Delhi, Chennai, Hyderabad

1961

7

Kolkata, Mumbai, Delhi, Chennai, Hyderabad, Bengaluru, Ahmedabad

1971

9

Kolkata, Mumbai, Delhi, Chennai, Hyderabad, Bengaluru, Ahmedabad, Pune, Kanpur

1981 12

Kolkata, Mumbai, Delhi, Chennai, Hyderabad, Bengaluru, Ahmedabad, Pune, Kanpur, Jaipur, Lucknow, Nagpur

1991 23

Kolkata, Mumbai, Delhi, Chennai, Hyderabad, Bengaluru, Ahmedabad, Pune, Kanpur, Jaipur, Lucknow, Nagpur, Madurai, Coimbatore, Kochi, Surat, Vadodara, Indore, Bhopal, Patna, Ludhiana, Varanasi, Greater Vishakapatnam

2001 35

Kolkata, Mumbai, Delhi, Chennai, Hyderabad, Bengaluru, Ahmedabad, Pune, Kanpur, Jaipur, Lucknow, Nagpur, Madurai, Coimbatore, Kochi, Surat, Vadodara, Indore, Bhopal, Patna, Ludhiana, Varanasi, Greater Vishakapatnam, Agra, Nashik, Rajkot, Vijayawada, Meerut, Faridabad, Jamshedpur, Jabalpur, Asansol, Allahabad, Dhanbad, Amritsar

2011 53

Kolkata, Mumbai, Delhi, Chennai, Hyderabad, Bengaluru, Ahmedabad, Pune, Kanpur, Jaipur, Lucknow, Nagpur, Madurai, Coimbatore, Kochi, Surat, Vadodara, Indore, Bhopal, Patna, Ludhiana, Varanasi, Greater Vishakapatnam, Agra, Ghaziabad, Nashik, Rajkot, Vijayawada, Meerut, Faridabad, Jamshedpur, Jabalpur, Asansol, Allahabad, Dhanbad, Amritsar, Aurangabad, Kozhikode, Kannur, Thiruvananthapuram, Malappuram, Thrissur, Srinagar, Vasai-Virar, Jodhpur, Ranchi, Raipur, Kollam, Gwalior, Bilai, Chandigarh, Trichi, Kota

Source A-4 Towns and Urban Agglomerations Classified by Population Size Class in 2011 with variation since 1901, https://censusindia.gov.in/2011census/PCA/A4.html

1.3 Distribution of the Urban Population in India (2011 Census) As per the 2011 census, the percentage of the population living in cities and towns accounts for 31.16% of the nation’s total population. This percentage, however, varies (Fig. 1.6) greatly between the states and union territories of India, ranging from over 90% in Delhi and Chandigarh to as little as 10.03% in Himachal Pradesh (Kumar 2015). Goa with 62.17% ranks first on the list of urbanized states followed by Mizoram with 52.1%. Tamil Nadu and Kerala are next and are rather close to one another with 48.4% and 47.7%, respectively. They are followed by Maharashtra at 45.2% with

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1 Urbanization in India

Fig. 1.4 Distribution of million plus cities in India

Gujarat slightly farther behind with 42.6%. Territories with the lowest percentage of urban population are Bihar, Assam, Odisha, and Meghalaya with Himachal Pradesh having the lowest urban population at 10.03%. Except for Andaman and Nicobar, all other UTs have more than 40% of their population living in urban areas. Figure 1.6 shows the distribution of towns with proportionate circles. The total number of towns includes all classes of cities and towns as per the 2011 census. The distribution of the number of towns also differs from state to state. A total of 7933 towns were distributed unevenly. Tamil Nadu stands first with 1097 towns and Meghalaya has the least with just 22 towns. The highly urbanized state of Goa has

1.3 Distribution of the Urban Population in India (2011 Census)

9

Fig. 1.5 Night satellite image showing the level of urbanization in India from 2012 to 2019. Source https://www.nightearth.com/[email protected],80.546934,5.016752486837465z&data=$bWV sMGQx&lang=en

70 towns. The least urbanized state of Himachal Pradesh has 59 towns. This shows that the degree of urbanization does not depend on the number of towns and is rather based on the size of the urban population. With an urban population of only 31.16%, India can be considered one of the least urbanized countries in the world. However, India’s vast population means that the number of people living in its urban area as per the 2011 census was 377 million which is more than the current population of the United States of America (USA). The total population of the USA as per their census on 07th Nov 2020 is 330 million. From published web sources, the Indian urban population is estimated to be around 471 million in 2019 (www.macrotrends.net 2020). This puts into perspective the urban population in India. Figure 1.7 shows the share of the urban population in percentage of the total urban population of India. Maharashtra and Uttar Pradesh were the major contributors and share more than 10% (13.48% and 11.8% respectively) of the total urban population. Tamil Nadu, Karnataka, Gujarat, and West Bengal together share more than 5% of the Indian urban population. The north-eastern and northern hilly states account for less than 1% of the total urban population of India. Another notable feature of Indian urbanization is the growing number of megacities (cities having more than 10 million population). As per the 2011 census, there were only three (Mumbai, Delhi, and Kolkata) megacities. As per the “World Cities Report”, 2016, issued by the UN’s Department of Economic and Social Affairs, Delhi will be the second-largest city in the world after Tokyo by 2030, as another 9.6 million population will be added to Delhi by then. According to the report, there are now 5 megacities (namely Delhi, Kolkata, Mumbai, Bengaluru, and Chennai) with two more that will be added in 2030, Hyderabad and Ahmedabad (Table 1.3). Though the census of India is the authorized source for information about the urban population of India, there have recently been concerns about its reliability.

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Fig. 1.6 Distribution of urban population in India

As per the World Bank report, 2774 settlements demonstrated urban characteristics between 2001 and 2011, whereas only 147 were officially accepted as urban (World Bank 2013). In another study, Balk, D et al. (2019) argued that the 2011 census figure of the urban percentage of 31.16% is too low and that the urban population has been underestimated. It has been suggested that the census surveys introduced several biases due to numerous regional inter-census adjustments and the United Nations System of classification of urban areas will be a better option (Eric Denis and Kamala Marius-Gnanou 2011). In summary, there are two reasons for the increasing urbanization in India. The first reason is the increase in population due to the reclassification of rural areas into

1.3 Distribution of the Urban Population in India (2011 Census)

11

Fig. 1.7 Share of urban population from each state as per 2011 census

urban areas. For example, in the state of Kerala, Malappuram UA’s total population in the 2001 census was 3,11,558 and in 2011, it rose to 16,99,060. This sudden increase by a factor of five was due to the reclassification of rural areas by the Office of the Registrar General and Census Commissioner, India. This reclassification was based on the shift in occupational structure. The second reason is what is considered “demographic change” as rural migrants shift to urban areas and thus increase the urban population.

12 Table 1.3 Megacities in India

1 Urbanization in India Name of the megacity

Population in millions (05th October 2016)

New Delhi

26.5

Mumbai

21.4

Kolkata

15

Bengaluru

10.5

Chennai

10.2

Hyderabad

12.8a

Ahmedabad

10.5a

a Expected

population by 2030 Source World Cities Report (2016)

1.4 Urban Problems in India The top ten cities of India occupy only 0.1% of the total area. These cities are overcrowded, congested, and most vulnerable to disasters. Cities and towns are hubs of economies, reserves, technology, centers for revolution, economic growth, and tertiary jobs. They are also the reification of hope for millions of immigrants from rural hinterlands. There was a major population shift from rural to urban in the last part of the twentieth century (Bhagat 2018). There are a growing number of migrants who, in search of employment and a better quality of life move to cities but end up joining India’s homeless population. In 2001, nearly 12.37 million and in 2011, around 14.37 million male migrants migrated in search of employment opportunities (https://cen susindia.gov.in/2011census/migration.html). Living conditions in areas inhabited by the poor, especially in slums and squatter settlements are a matter of concern. In short, the whole environment is filled with the dangers of disease and a higher mortality rate. Besides health hazards, the problems of illiteracy and youth unemployment are more pronounced in these areas. These areas also have an increased likelihood of crimes. One of the major problems of developing and underdeveloped economies is providing basic shelter to this urban poor. They have often failed to provide land or shelter to the teeming urban poor populations in accordance with their city plans (Puri 2005). Food, clothing, and housing are essential requirements for humans. Housing is considered a basic component in the inclusive socio-economic development of a country, and for the satisfaction of the social and cultural ambitions of the people. The problem of housing shortage is a great challenge and an extreme threat to the political and social order since, with each consecutive planning period, the gap between demand and supply has only been broadening (www.mhupa.gov.in). By 2051, according to the demographic experts, India’s urban population will account for 50% of its total population, and as the megacities and metropolitan cities absorb the rural workforce, there will be a further worsening of housing shortage in the urban areas (Fig. 1.8).

1.4 Urban Problems in India

13

Fig. 1.8 Urban housing shortage. Source Urban Housing Shortage report of the Technical Group (12)-2012–17

According to this report, 0.53 million dwelling units would be needed to provide shelter to households that are lacking a house, 2.27 million units to take care of the problem of obsolescence, and another 14.99 million to ensure that households do not have the problem of overcrowding (www.deccanherald.com). As per the report of the Technical Group, the shortage of housing is mostly at the lower economic strata of the population. According to the report, the estimated housing shortage works out to be 10.49 million for EWS households, which is 56.18% of the total housing shortage in urban India. Similarly, for Low Income Group (LIG) households, the shortage works out to be 7.36 million, which constitutes 40% of the total shortage (Table 1.4). Therefore, the problem stems from housing shortage in urban areas and particularly the lack of houses for its poorest residents. Land is a fixed asset and a costly affair in urban areas. Hence, the supply of land for affordable housing is the actual and acute problem in urban areas. Coupled with this, the rocketing construction costs and regulatory issues raised by the local and state governments also increase the housing shortage. The LIGs (low-income groups) are not eligible for home loans Table 1.4 Housing shortage in various economic groups

Category

Distribution of housing shortage among different economic categories as on 2012 No (in Millions)

In percentage

EWS

10.55

56.18

LIG

7.41

39.44

MIG and above

0.82

4.38

18.78

100.00

Total

Source Report of the Technical Group on Urban Housing Shortage (TG-12) (2012–17)

14

1 Urbanization in India

or any financial aids and this too intensifies the problem of affordable housing and increases the housing demand. The only way to stop slum formation or to recover the living conditions of the poor urbanites is to provide affordable housing to them. The housing stock can be created in different forms based on the location of employment opportunities, access to the basic infrastructure, and the land value. In the central part of the city, where the land value is high and owned by private people/firms, the housing stock can be built with a public-private partnership program (NUHHP 2007). If the land is owned by the urban local body, or the state, then through “in situ” development, the existing structures can be renewed and augmented with basic infrastructure such as drinking water, drainage, sanitation and approach road, etc. This assures that the urban poor have a decent living. When we plan for “affordable housing to all”, (MoHUPA 2009) we should also take into consideration the ongoing urbanization process and inflow of rural poor along with the natural increase.

1.5 Sustainable Cities and Indian Urbanization The two major problems of this century across the globe are increasing urbanization and urbanization of poverty. Increasing urbanization is irreversible and inescapable. As per the future projections for India stated above, by 2050, the urban population will rival or outpace the rural population. There is no doubt that urban areas contribute to economic growth. According to UNEP, more than 70% of the Gross Domestic Product (GDP) comes from cities. At the same time, they are the largest producers of all types of waste materials and consumers of non-renewable resources. Overutilization of existing resources is one of the biggest challenges and deterrents to sustainable development. In addition to that, this economic opportunity continuously attracts rural migrants and accelerates slum growth. Many Indian cities are struggling to manage the growth of slums. They are suffering from a lack of adequate infrastructure and basic services. Therefore, balancing economic development and sustainable development is the biggest urban challenge today. Sustainable development will not be achieved without considering the development of urban areas. Sustainable cities are the means of achieving sustainable development as a whole. A sustainable city means “Creating career and business opportunities, safe and affordable housing, and building resilient societies and economies. It involves investment in public transport, creating green public spaces, and improving urban planning and management in participatory and inclusive ways”. This definition was given by UNDP and it is part of the integrated Sustainable Development Goals, popularly known as SDGs. There are 17 goals, and “Sustainable Cities and Communities” (Goal 11) is one of them. The SDGs are adopted by United Nations member states with the aim to end poverty, protect the environment, and ensure peace and prosperity to all by 2030. Sustainable Development Goals, chiefly Goal No. 1 (No Poverty), Goal No. 2 (Zero Hunger), Goal No. 3 (Good Health and Wellbeing), Goal No. 6 (Clean Water

1.5 Sustainable Cities and Indian Urbanization

15

and Sanitation), Goal No. 7 (Affordable and Clean Energy), Goal No. 8 (Decent Work and Economic Growth), Goal No. 9 (Industry, Innovation, and Economic Growth), Goal No. 10 (Reduced Inequality) are related to Goal No. 11 (Sustainable Cities and Communities) either directly or indirectly. The actions taken to achieve any one of these goals will have a huge impact on achieving sustainable cities and communities. India is one of the active global partners of this endeavor and has framed its national development agenda along the lines of sustainable development goals. The Indian Government has taken holistic efforts to achieve the SDGs by introducing new policy programs that reflect the SDGs. The “Swachh Bharat Mission” was launched with the ambitious goal of universal sanitation coverage. The “Namami Gange Mission” seeks to clean the river Ganga while the “National Clean Air Programme” aims to prevent and control air pollution. The “National Resource Efficiency Policy” drafted in 2019, seeks to improve the efficiency of resources drawn from nature for various industries with an emphasis on reusing and recycling. India’s Economic Survey 2018–19 positioned India at the 11th position globally with respect to climate financing and observed that it accounts for 33% of the Certified Climate Bonds in emerging markets. Realizing the ongoing trend of urbanization at the global and national level, India has given due priority to SDG 11. Since it understood the links between SDG 11 and other goals, it has developed integrated policies in the direction to achieve the goal. The national mission “AMRUT (Atal Mission for Rejuvenation and Urban Transformation)” aims to provide basic services and amenities, water supply, sewerage, and urban transport in cities to improve quality of life, especially of the poor and the vulnerable. MRTS and Metro Project provide accessible and sustainable transport systems for all, especially for women, children, persons with disabilities, and older persons. The government of India’s initiative, “Swachh Bharat Mission” in the urban areas aims for 100% door-to-door collection of waste and sustainable waste management. The mission “PMAY-U (Pradhan Mantri Awas Yojana-Urban)” seeks to address housing requirements of the urban poor, including the slum dwellers. The flagship program, “Smart Cities” seeks to achieve the vision of improving ease of living, particularly for the poor, women, elderly, and differentlyabled people. The strategy components of area-based development in the mission are “retrofitting”, “city renewal”, and “city extension” as well as applying smart solutions covering larger parts of the city. “Kala Sanskriti Vikas Yojana” and “National Heritage City Development and Augmentation Yojana” (HRIDAY) are initiatives (SDG 11.4) to protect the world heritage sites. To reduce the number of deaths and the number of people affected (SD G11.5), “Infrastructure of Disaster Management”, “National Cyclone Risk Mitigation Project” (NCRMP), and other disaster management schemes have been effectively implemented. To match with the global target (11.1) of ensuring access for all to adequate, safe, and affordable housing and basic services and upgradation of slums, the Indian government set ambitious targets such as “Affordable Housing for All” and “SlumFree India” by 2030. To monitor the progress of achieving these goals, the National Institution for Transforming India, also called NITI Aayog, has developed the “SDG India Index”. As part of the SDG Index to assess the progress of states and UTs

16

1 Urbanization in India

Fig. 1.9 SDG index-sustainable cities. Source https://sdgindiaindex.niti.gov.in/#/ranking

in achieving the targets under SDG 11, indicators such as houses completed under “PMAY-U” as a percentage of net demand assessment for houses (100% target), and percentage of urban households living in slums (0% Target) were chosen. Similarly, to assess the progress of the states and UTs in achieving the SDG goal 11.6, which states “By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management”, the percentage of wards with 100% door to door waste collection, and percentage of waste processed were selected as indicators (SDG Index 2018). As per the SDG Index report (2018, 2019), when the SDG India Index score is equal to 100, then the state/UT is recognized as an “Achiever”. If the SDG index score is less than 100 but greater than or equal to 65, then the state/UT is recognized as a “Front Runner”. “Performers” are the states/UTs who have a score less than 65 but greater than or equal to 50. The state/UT with an SDG India Index score less than 50 is known as an “Aspirant” (Fig. 1.9). When reviewing index scores on SDG 11, the state of Goa and the UT of Andaman & Nicobar were leading the list of “sustainable cities”. With a score of 71, Goa was the only “Front Runner” during 2018. Andaman & Nicobar Islands (score 64), Sikkim (56), Chhattisgarh (54), Gujarat (52), and Jharkhand (52) were “Performers”, and the rest of the states and UTs were only “Aspirants” (NITI Aayog, SDG India Index 2018). This list underwent some changes in 2019, as the states of Himachal Pradesh, Gujarat, and Sikkim joined Goa in the list of “Front Runners”. Chandigarh became the first UT to join this list. Delhi moved up the “Performer” tier along with 11 other states and 2 UTs. However, 13 states and 3 UTs still had an SDG index score lower than 50 and were classified as “Aspirants”. The index also revealed that 31.01% of houses completed under PMAY(U) and 5.41% of urban households are still located in slums. All of this shows that we have to further strengthen our efforts to achieve sustainable urban development.

1.6 Relevance for This Study

17

1.6 Relevance for This Study With this background, the study aimed to contribute some viable methodologies to attain sustainable urban development through improving the quality of life of slum dwellers to a more decent state and for better implementation of slum policies to achieve sustainable development goals. The current global slum population exceeds one billion (World Social Report 2020) and as the population itself continues to grow in the years to come, this subset of it too is going to increase greatly in number. In order to resolve the difficulties that slum dwellers face and to provide them with improved infrastructure and services, there is a need for the policies that affect their lives to be made more effective and efficient. This can only be achieved by better information collection regarding slum areas and monitoring of their inhabitants (Mahabir and Crooks 2018). Recognizing the severity of the problems and the responsibility that it has to both the Indian and the world population, the Indian Government has launched many developmental programs for improving the quality of life of slum dwellers. Though these programs were well structured and planned for the betterment of slums and slum dwellers, the implementation of these programs was often restricted by the scarcity of data, not only pertaining to the living conditions in Indian slums, but even the scale and spread of its population (Report of Slum Committee 2010; www. mhupa.gov.in). Official maps representing the locations of slum areas usually only took into account the slums that have been “notified and recognized” by the local and state governments and exclude the areas which have been “identified” as a slum. Therefore, the differences in the different methods of acquiring data along with the political and operational decisions of whether or not these slum areas are taken into account in the official statistics create inconsistencies (Ranguelova et al. 2019). Using traditional methods such as household surveys to collect information on slums suffers mainly because of a lack of spatial information and preconception. Updating this information requires a relatively large number of human resources. Therefore, there is a need for a reliable source of spatial information that can be updated regularly. At this juncture, geospatial technology which includes remote sensing, GIS, and GPS can help in the collection, mapping, and analysis of data on slums. Using spatial technology to obtain information on slums, however, does have certain limitations. The approaches that have so far been in use for the mapping of underprivileged areas (i.e., household survey method, remote sensing-based, and participatory approach) are limited by difficulties in transferring the approach and data to other locations and scaling it up. They also do not take into account the views of different stakeholders. Mapping slum areas using earth observation is a mostly top-down process that barely gives any attention to the ground reality and doesn’t bother interacting with the people residing in urban areas and those with a stake in the plans for development (Kuffer et al. 2020). This study intends to help the urban poor in general and slum dwellers in particular by creating a reliable database about their location using geospatial technology that combines experts’ domain knowledge with local community participation. Thus,

18

1 Urbanization in India

the present study seeks to help them improve their living conditions and also help planners and administrators for sustainable planning by estimating the future demand and possible areas of future slum growth.

1.7 Organization of the Chapters The book is organized into seven chapters. The first chapter discussed the urban development of India, from 1901 to 2011. It described Indian urbanization through census data and also briefly touched upon the distribution of megacities, millionplus cities, and other classified towns. It brought out the uneven distribution of the urban population in India. Major problems of urban India and sustainable cities were discussed in order to provide a better understanding of the Indian urban scenario. The purpose of this background was to make the need for inclusive sustainable development clear. It also discussed the national housing shortage, affordable housing, and justified the need for such a detailed study on slums which is required to develop a necessary understanding of slums for proper slum development as part of sustainable cities and communities. Chapter 2 discusses the Indian slum scenario in a nutshell. It gives a brief about the efforts taken by the Indian Government in counting the slum population through regular census operations. Along with that, it elucidates the studies on slums of India, the methodologies that have been adopted in the studies, and the outcomes of those studies. This chapter also elucidates the characteristics of slums of India through Google Earth images and field photographs and forms the base for building slum ontologies. Chapter 3 is devoted to the introduction of the case study area. It gives details regarding the existing slum situation with the help of field photographs. The chapter also talks about the objectives and methodology adopted with regards to mapping, modeling, and managing the data for the selected study area. The methodology adopted in the present case can be successfully applied to any slum in India. Chapter 4 demonstrates how the slum ontology was developed from the knowledge acquired from the stakeholders, with the help of domain expert’s inputs, and using published sources and how it helped to identify slums from very high-resolution data. It also discusses the slum boundaries that were created using PGIS (Participatory GIS approach). This was the first map created for the slums of Kalaburagi city. Another important thing covered in this chapter is the validation of the slum map. This was done with the help of an urban frame survey map (UFS). Building a Cellular Automata (CA) model is always challenging and the successful completion of this challenge is covered in Chap. 5. The output of the CA model has given reliable results regarding the current slum distribution. According to the model output, the area that was considered as most attractive for the occurrence of slums accounted for 95% of the existing slums. The remaining 5% of slums were found in moderately attractive zones. The model also gave a hint regarding the areas that have a higher likelihood of slum formation in the future. Since the proposed land use

1.7 Organization of the Chapters

19

plan for 2021 has been finalized by the ULB (Urban Local Body), areas suitable for developing affordable housing stock were also suggested for preventing slums. Chapter 6 deals with the projecting of the slum population and estimation of the housing demand. The estimation of housing demand depends on various factors. Here, the need-based approach of sticking to the concept of willingness to stay instead of a willingness to pay has been applied. The housing demand factors were taken from RAY program modules rather than common socio-economic factors. This model was tested to provide a reasonable estimation for one slum area of Kalaburagi city (Vijay Nagar). The estimation of the city’s population was attempted using the data collected from Kalaburagi City Corporation but the output had a lot of anomalies. Chapter 7 deals with existing slum policies and programs. The Government policies are explained briefly with their salient features. The chapter talks about the successes and drawbacks of earlier programs through various case studies and published research articles on slum projects and slum policies. This chapter also talks about the rehabilitation programs already implemented in Kalaburagi city. Chapter 8 is devoted to the Spatial Decision Support System (SDSS). The slum SDSS can greatly improve the implementation of slum development programs and most importantly it allows community participation at every level. It makes the residents of slums one of the decision-makers and gives them ownership. In this chapter, the design and running of the SDSS model are demonstrated with one case study—“Borabai Nagar slum”. The model helps stakeholders to visualize the postdevelopment/implementation scenario. The best model which meets the requirements of slum dwellers will be selected as an optimal slum development program for implementation.

References Angel S, Parent J, Civco DL, Blei AM (2011) Making room for a planet of cities. Policy focus report. Lincoln Institute of Land Policy. ISBN 978-1-55844-212-2. Policy Focus Report/Code PF027 Balk D, Montgomery M, Engin H, Lin N, Major E, Jones B (2019) Urbanization in India: population and urban classification grids for 2011. Data, 4(1):35. MDPI AG. https://doi.org/10.3390/data40 10035 Bhagat R (2018) Urbanisation in India: trend, pattern and policy issues. Working paper series. International Institute for Population Sciences. https://doi.org/10.13140/rg.2.2.27168.69124.b Census (2011) https://censusindia.gov.in/2011census/population_enumeration.html Denis E, Marius-Gnanou K (2011) Toward a better appraisal of urbanization in India. A fresh look at the landscape of morphological agglomerates, Systems, Modeling, Geostatistics, Cyber Geo. Eur J Geogr. https://doi.org/10.4000/cybergeo.24798 Kuffer M et al (2020) The role of earth observation in an integrated deprived area mapping “System” for low-to-middle income countries. Remote Sens 12:982. https://doi.org/10.3390/rs12060982. www.mdpi.com/journal/remotesensing Kumar J (2015) Metropolises in Indian urban system: 1901–2011. Eur J Geogr 6(3):41–51. ©Association of European Geographers Mahabir R, Crooks A (2018) Mapping and monitoring of slums in 5 steps. GIM International. https:// www.gim-international.com/content/article/mapping-and-monitoring-of-slums-in-5-steps

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MoHUPA (2009) Fortieth report of standing committee on urban development presented to Lok Sabha on 2.7.2009. Ministry of Housing and Urban Poverty Alleviation. Urban Housing 2009. www.mhupa.gov.in NUHHP (2007) National Urban Housing & Habitat Policy, 2007- Affordable Housing to all. http:// mohua.gov.in/upload/uploadfiles/files/2housing.pdf Puri OP (2005) Housing for the urban poor by Shri O.P. Puri Assistant General Manager, NHB (This article won the third prize for best article in the Essay Competition to celebrate World Habitat Day on October 3, 2005 jointly with Shri Lalit Kumar, HUDCO). https://www.nhb.org.in/Public ations/articles_mar06.pdf Ramachandran R (2007) Urbanisation and urban system in India. Oxford Press, New Delhi Ranguelova E, Weel B, Roy D, Kuffer M, Pfeffer K, Lees M (2019) Image-based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India. Eur J Remote Sens 52(1):40–61. https://doi.org/10.1080/22797254.2018.1535838 Report of Slum Committee, 2010 Report of Slum Committee (2010) Report of the committee on slum statistics/census. Ministry of Housing and Urban Poverty Alleviation. Government of India SDG India Index (2018) Sustainable development goals India index. A baseline report. NITI Aayog SDG Index report (2019) SDG India Index & Dashboard, 2019-20, Nov 2019. www.niti.gov.in Shaban A, Kourtit K, Nijkamp P (2020) India’s urban system: sustainability and imbalanced growth of cities. Sustain J 12:2941. https://doi.org/10.3390/su12072941. www.mdpi.com/journal/sustai nability World Bank (2013) Urbanization beyond Municipal Boundaries: Nurturing Metropolitan Economies and Connecting Peri-Urban Areas in India. Directions in Development. Washington, DC: World Bank. https://doi.org/10.1596/978-0-8213-9840-1 World Cities Report (2016) Urbanization and development—emerging futures. UN-Habitat, HS Number HS/038/16E. ISBN 978–92-1-132708-3 World Social Report (2020) Slums: home to more than 1 billion people https://www.un.org/dev elopment/desa/dspd/wp-content/uploads/sites/22/2020/02/World-Social-Report-2020-Chapter4.pdf www.macrotrends.net (2020) India Urban Population 1960-2021. https://www.macrotrends.net/cou ntries/IND/india/urban-population

Chapter 2

Slums in India

Urbanization in developing countries is going to be a distinguishing phenomenon of the 21st century. As India urbanizes, the issues of urban poverty and slums will assume critical dimensions. Excerpts from “State of slums in India—A statistical compendium”, 2013.

Abstract Urban areas are engines of economic growth, centers of production, and support the lives of millions. These, however, do not paint a complete picture of the urban phenomenon. Urban areas are also home to millions of people who are neglected and helpless. The invisible population of urban areas was counted for the first time in India, in 2011 and included in the urban population. The present chapter describes the efforts taken by the government to count the slum population and the distribution of the slum population at the national level. It also elucidates the definitions and types of slums. Though the 2011 census was done at the national level, it did not account for the entire slum population as it neglected census towns. It was the census of 2001 that initiated slum population counting but it was restricted to towns that had a population of 50,000 and above as counted by the 1991 census. Therefore, both these censuses did not bring out a clear picture of the slum population in India. Despite these gaps, the census data did serve the purpose of drawing the poor slum situation into the limelight. Mere counting, however, will not help in improving the living conditions of the slum population. Spatial information and insight into their living conditions are needed for proper and inclusive planning. To showcase the need for spatial information and the use of geospatial technology in extracting spatial information and mapping slums, sample scenes (satellite images) of various slum areas have been taken from Google Earth and explained in this chapter. Keywords Urban · Slum · Census · Notified slum · Recognized slum · Identified slum · Kalaburagi · Earth observation data · Spatial information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_2

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2 Slums in India

2.1 Introduction Almost 55% of the earth’s population lives in urban areas. We expect that the urban population will increase from 4.2 billion (2018) to 6.5 billion by 2050. Out of 4.2 billion urban dwellers, 828 million people are estimated to live in slums, and the number is continuously rising (UNDP 2016). The United Nations’ World Social Report stated that in 2016, one in every four urban residents was residing in a slum, which puts the estimated number of slum dwellers over 1 billion. (United Nations 2019). According to the UN report, between 2000 and 2014, the slum population rose from 807 to 883 million. The greatest part of this population is distributed across the developing world as follows: 332 million slum residents in Eastern and South-Eastern Asian countries, 197 million slum dwellers in Central and Southern Asian countries, and the sub-Saharan Africa slum population accounting for the remaining 189 million (United Nations 2018). The concern is that the majority of the future urban growth between now and 2050 is expected in sub-Saharan and Asian countries (UN-WUP 2018). Rapid urbanization in the last two decades has meant a corresponding growth in slum settlements. According to the World Bank (2017) report on the urban population, it has now increased and accounts for 34% of the world population. Indian urbanization is considered hidden/messy because of unorganized, sprawled development and a huge number of slums. In India, nearly one in every six urban residents lives in a slum (www.censusindia.gov.in). Due to the efforts taken at the local and global levels, there were improvements worldwide in the living quality of slum residents. However, the number of slum residents is currently estimated to rise by around 6 million annually according to UNHABITAT (2010). The Indian Government, as part of the Millennium Development Goals, through various slum development programs and policies, improved the living conditions of a large number of slum residents significantly between 2000 and 2015. Despite these efforts, the total number of slum dwellers keeps on increasing and along with million-plus cities, the second-order cities have also started feeling the heat. It is time for us to think about inclusive planning and give priority to the proper implementation of slum development programs rather than planning that just exists on paper. The Millenium Development Goals were succeeded by the Sustainable Development Goals in 2016. Sustainable Development Goal 11 talks about inclusive, safe, resilient, and sustainable cities. To accomplish that goal, it is mandatory to include slums in urban planning and comprehensive information on slums is indispensable for policymaking to provide a better living environment to them. Traditional, field-based surveys may not be able to give a clear picture of slums, their actual location, size, and extension in an up-to-date manner and it is also a herculean task. Geospatial technologies can be of aid in providing access to the right information at the right time. Using remote sensing and GIS technologies helps planners to get the exact location, size, spatial extension, and physical characteristics in a realistic way and will enable them to plan for the entire city holistically which in turn will help achieve sustainable urban development.

2.2 Slum Census in India

23

2.2 Slum Census in India The Indian Population Census Organization is one of the largest administrative networks in the world. In 1948, the census act was included in the Statute Book. The primary objective of organizing the census operation is to cover the entire area, including everyone without omission or overlapping. The household is the basic unit of operation which is generally understood as, “a group of persons commonly living together and partaking of food from the same kitchen” (www.censusindia.gov.in). The first census started during the British time in the 1870s, and the first of the decennial censuses took place in 1881. Until 1961, it was the provisional administration structure that handled the census operations. The “Office of Registrar General and Census Commissioner of India” was founded in 1961 as a permanent government department (censusindia.gov.in). Despite previous efforts, the 2001 census was the first attempt to present the slum population by their actual count. The method of collecting all the characteristics of slum households through a primary field survey was a first of its kind at the global level. As per the 2001 census, out of 4041 statutory towns, 2613 towns reported a slum population with a total population of 42.6 million. This constituted 15 percent of the country’s population and 22.6 percent of the urban population of the 31 states/union territories that were reporting slums (www.census india.gov.in). Though counting of the slum population was only considered for the statutory towns having a population of 50,000 as per the 1991 census, the census of 2001, also included non-statutory towns having a large slum population. It added the census towns having 50,000 population in the NCR, Delhi (11 Nos), Uttar Pradesh (01 No), and included the towns which qualify for 50,000 population after adding the population of outgrowth areas (as per 1991 census). These include towns in Bihar (01 No), Madhya Pradesh (02 Nos), Gujarat (02 Nos), and Maharashtra (01 No). In some cases, the entire urban agglomeration was included to meet the 50,000 population criteria for slum mapping. An example of this is the Shillong urban agglomeration (Census of India, Explanatory note, www.mospi.gov.in). There were changes in the distribution of slums between 2001 and 2011 (Fig. 2.1). Andhra Pradesh (including Telangana) and Chhattisgarh continued to have a high slum population percentage while Kerala retained its low percentage of slum population. Karnataka, Jharkhand, Uttarakhand, West Bengal, and Assam remained stable too and did not see any changes in their slum percentage. The rest of the states though underwent changes. Irrespective of the change in the slum percentage, the absolute number of slum dwellers had increased from 2001 and 2011 due to natural increase and migration. Under Sect. 3 of the “Slum Area Improvement and Clearance Act (1956)” of the Government of India, slums have been defined as “mainly those residential areas where dwellings are in any respect unfit for human habitation by reasons of dilapidation, overcrowding, faulty arrangements and designs of such buildings, narrowness or faulty arrangement of streets, lack of ventilation, light, sanitation facilities

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2 Slums in India

Fig. 2.1 Slum Population distribution in India. Source Primary Census Abstract for Slum, 2011 Office of the Registrar General & Census Commissioner, India

or any combination of these factors which are detrimental to safety, health, and morals” (https://www.censusindia.gov.in). As per the primary census abstract for slum, published in the year 2013, there are three types of slums, namely, “Notified, Recognized, and Identified”. (i)

(ii)

(iii)

All notified areas in a town or city notified as “Slum” by State, Union Territories Administration or Local Government under any Act including a “Slum Act” may be considered as notified slums All areas recognized as “Slum” by State, Union Territories Administration or Local Government, Housing and Slum Boards, which may have not been formally notified as slum under any act may be considered as recognized slums A compact area of at least 300 population or about 60–70 households of poorly built congested tenements, in an unhygienic environment usually with inadequate infrastructure and lacking in proper sanitary and drinking water facilities. Such areas should be identified personally by the Charge Officer and also inspected by an officer nominated by the Directorate of Census Operations. This fact must be duly recorded in the charge register. Such areas may be considered as identified slums (https://www.censusindia.gov.in) (Table 2.1).

In addition to the slum census, there are also a few sample surveys that provide details about slums. At the national level, “National Sample Survey Office - M/o Statistics and Programme Implementation (M/OSPI), Government of India (GOI)” conducted sample surveys on slums. The details are given below (Table 2.2).

2.2 Slum Census in India

25

Table 2.1 Number of statutory and slum reported towns with type wise slum population Name of state/Union territory

Towns Statutory towns

Slum reported towns

Type wise slum population Total population

Notified slums

Recognized slums

Identified slums

India

4041

2,613

6,54,94,604

2,25,35,133

2,01,31,336

2,28,28,135

Jammu & Kashmir

86

40

6,62,062

1,62,909

1,36,649

3,62,504

Himachal Pradesh

56

22

61,312

60,201

0

1,111 4,79,517

Punjab

143

73

14,60,518

7,87,696

1,93,305

Chandigarh

1

1

95,135

95,135

0

0

Uttarakhand

74

31

4,87,741

1,85,832

52,278

2,49,631

Haryana

80

75

16,62,305

14,912

0

16,47,393

Delhi

3

22

17,85,390

7,38,915

0

10,46,475

Rajasthan

185

107

20,68,000

0

0

20,68,000

Uttar Pradesh

648

293

62,39,965

5,62,548

46,78,326

9,99,091

Bihar

139

88

12,37,682

0

0

12,37,682

Sikkim

8

7

31,378

31,378

0

0

Arunachal Pradesh

26

5

15,562

0

0

15,562

Nagaland

19

11

82,324

0

48,249

34,075

Manipur

28

0

0

0

0

0

Mizoram

23

1

78,561

0

78,561

0

Tripura

16

15

1,39,780

0

1,24,036

15,744

Meghalaya

10

6

57,418

34,699

8,006

14,713

Assam

88

31

1,97,266

9,163

70,979

1,17,124

West Bengal

129

122

64,18,594

48,918

37,03,852

26,65,824

Jharkhand

40

31

3,72,999

64,399

59,432

2,49,168

Odisha

107

76

15,60,303

0

8,12,737

7,47,566

Chhattisgarh

168

94

18,98, 931

7,13,654

7,64,851

4,20,426

Madhya Pradesh

364

303

56,88,993

19,00,942

25,30,637

12,57,414

Gujarat

195

103

16,80,095

0

0

16,80,095

Daman & Diu 2

0

0

0

0

0

Dadra & Nagar Haveli

1

0

0

0

0

0

Maharashtra

256

189

1,18,48,423

37,09,309

34,85,783

46,53,331

Andhra Pradesh

125

125

1,01,86,934

83,38,154

8,77,172

9,71,608 (continued)

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Table 2.1 (continued) Name of state/Union territory

Towns Statutory towns

Slum reported towns

Type wise slum population Total population

Notified slums

Recognized slums

Identified slums

Karnataka

220

206

32,91,434

22,71,990

4,45,899

5,73,545

Goa

14

3

26,247

6,107

0

20,140

Lakshadweep

0

0

0

0

0

0

Kerala

59

19

2,02,048

1,86,835

8,215

6,998

Tamil Nadu

721

507

57,98,459

25,41,345

19,78,441

12,78,673

Puducherry

6

6

1,44,573

70,092

73,928

553

Andaman & Nicobar Is

1

1

14,172

0

0

14,172

Source https://www.censusindia.gov.in/2011-Documents/Slum-26-09-13.pdf Table 2.2 Details of NSSO surveys on slums NSSO

Year

Area covered

31st round

July 1976–June1977

As per 1971 census, ‘Class I’ The economic condition of towns (one lakh or more slum dwellers population) and two ‘Class II’ towns (Shillong and Pondicherry)

49th round January–June 1999

Rural and urban areas

Aspects covered

Particulars of slum ownership, area type, structure, living facilities, and their proximity to roads, schools and hospitals

58th round July–December 2002 Only slums found in the randomly selected urban blocks were surveyed

Availability and not the adequacy of facilities available in the slums

65th round July 2008–June 2009

Availability and not the adequacy of facilities available in the slums. The aim was to collect information on the present condition of the slums

Urban Slums

69th round July–December 2012 Randomly selected urban slums based on sampling procedure

Information on the present condition of the slums and that on recent changes, if any, in the condition of facilities available therein

Source National Sample Survey Office—M/o Statistics and Programme Implementation, Government of India)

2.2 Slum Census in India

27

These surveys only covered notified and non-notified slums of selected urban areas and did not include any identified slums. Despite this limitation, the reports prepared by NSSO have been useful in policymaking and planning.

2.3 Slum Distribution The 2011 census data, disseminated by the Registrar General and Census Commissioner’s office, shows that 13.7 million households (17.4% of urban households) lived in slums. This information, however, could not be used for comparative analysis. Even though the 2011 census used the same definitions for slums as the 2001 census, it covered all 4041 municipal towns regardless of their population size. As per the 2011 census, India has a total of 7935 towns and an urban population of 377 million living in them. Of these towns, 3894 towns are census towns based on the census definition of an urban area (an area that has a population of more than 5000 and 75% or greater of the male working population is engaged in non-agricultural occupations). The remaining 4,041 towns are administered by urban local bodies such as municipalities and are called statutory towns. Slum enumeration was carried out only in these towns. Table 2.3 gives the details of all three types of slums as per the census of 2011 in all statutory towns and slum reported towns. Unfortunately, counting the slum population was not done for census towns. Another concerning issue in the slum census (2011) is that 63.9% of slums are yet to be notified. In general, there is a misconception that slums are only found in million-plus cities or megacities. But in reality, the number of slums is greater in second-order cities like class 1 and class 2 cities (Table 2.4). While metropolitan cities with a large slum population are planning for the development of slums under slum development programs, the second rank cities have either not taken this issue very seriously or are not covered under the major projects. It would be far more sensible to prevent the problem from arising instead of scrambling to solve it in the future. Table 2.3 Type wise slum households distribution Sl.No

Category of slums

Number of Slum blocks(in thousands)

percentage

Households(in lakhs)

Percentage

1

Notified slums

37,072

34.3

49.65

36.1

2

Recognized slums

30,846

28.5

37.96

27.6

3

Identified slums

40,309

37.2

49.88

36.3

Total

108,227

100

137.49

100

Source Census of India 2011: Tables on Housing Stock, Amenities and Assets in Slums

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2 Slums in India

Table 2.4 Slum households distribution at the city level Sl.No

Indicator

Number of slum households in lakhs

% of slum households

1

Total Slum Households

137.5

100

2

Slums in Million plus Cities

52.4

38.1

3

Slums in other cities

85.1

61.9

Source Census of India 2011: Tables on Housing Stock, Amenities and Assets in Slums

In India, the slum population has undergone a twofold increase in the last twenty years. The slum population was expected to rise to 93 million by 2011 or amount to 7.75 percent of the total population, a figure that is nearly twice of Britain’s population. Later, it was projected to reach 104 million in 2017. Currently, slum dwellers make up only 24% of the urban population but even then, urban India struggles to accommodate poor migrants. According to the world bank report, the percentage of slum population has shown a decline (Fig. 2.2) from 1990 (54.9%) to 2015 (24%). Yet the actual size of the population (110 million in 2019) that resides in slums or in slum like conditions is quite high (Fig. 2.3). As per the 2001 census, (Fig. 2.3) Maharashtra accounted for 22.87% of the country’s slum population with 11.98 million slum dwellers. That was followed by 6.3 million slum dwellers in Andhra Pradesh, 5.8 million slum dwellers in Uttar Pradesh, 4.7 million slum dwellers in West Bengal, and 4.2 million in Tamil Nadu. As per 2001 census data, seven states had more than 20% of their people living in slums (Table 2.5). Others including Odisha, Punjab, Uttar Pradesh, Delhi, Tamil Nadu, Jammu & Kashmir, Puducherry, Andaman & Nicobar Islands, Chandigarh, Karnataka, Rajasthan, and Gujarat had slum populations ranging from 10–20% while Kerala had the lowest percentage (0.9%) of its population in slums. Assam and Goa had a slum population of 2.6% and 2.7%, respectively.

Fig. 2.2 Percentage of slum population living in urban India. Source World Bank, Trading Economics.com (2020)

2.3 Slum Distribution

29

Fig. 2.3 Distribution of slum population (2001 Census). Source Office of Registrar General and Census Commissioner, India

Table 2.5 Distribution of slum population (2001)

Name of the state

% of Slum population

Andhra Pradesh

30.1

Maharashtra

29.1

Haryana

27.5

Chhattisgarh

26.2

Meghalaya

24.1

Madhya Pradesh

23.7

West Bengal

20.8

Source Office of Registrar General and Census Commissioner, India

According to the 2011 census, Andhra Pradesh continued to lead the table, with 35.7% of its urban population living in slums. This was followed by Chhattisgarh (31.9%) and Madhya Pradesh (28.3%). As usual, Kerala had the lowest percentage at 1.5%. Even so, compared to the 2001 census, the slum population had increased in all these states (Fig. 2.4). Though the Government of India had taken all efforts to count the slum population through census operations, the committee constituted by M/O Housing and Urban Poverty Alleviation (MHUPA) noted conceptual flaws in its approach. According to its Chairman Sri. Pranab Sen, due to the real estate boom and speculation on land value, no one can anymore expect a large area for slum settlements. The rural migrants prefer to settle down wherever they find some space for themselves. Therefore, even a small group of slum households, around 20 in number should be considered as a slum for providing minimum basic services rather than considering 60–70 households as was done in the census procedures. The Sen Committee also recommended not to use walls and roofs made up of reinforced concrete as a criterion. The Census of

30

2 Slums in India Delhi Rajasthan Chhattisgarh Karnataka Uttar Pradesh Madhya Pradesh West Bengal Tamil Nadu Andhra Pradesh Maharashtra 0

2

4

6

8

10

12

Slum Population in millions

Fig. 2.4 Distribution of slum population (2011 Census). Source Office of Registrar General and Census Commissioner, India

2011 did not even include houses using GI or metal sheets, stones, and slate for wall and roof materials. These lapses in counting the slum population and mapping their location certainly will have a large impact on sustainable urban planning.

2.4 Mapping of Slums The UN-Habitat approach on slums underwent a peer review assessment in 2008. This was organized by the World Bank and UN-Habitat. The main focus of this assessment was to evaluate the slum definition used scientifically. Of the many recommendations to come out of this, perhaps the most important was the recommendation to take into account spatial contiguousness in measuring slums by using high-resolution remote sensing and GIS (Habitat 2008). Slum formation takes place in many ways. Sometimes due to the degradation of formal housing, the housing conditions gradually devolve into slums. Sometimes the poor rural migrants settle at open or vacant land with minimal or no infrastructure which leads to the formation of slums. Each form has its typical characteristics. The slum development sequence has three stages namely, “infancy, consolidation, and maturity”. One should understand these developmental stages before trying to map the slum settlements using Very High-Resolution images, since the slum characteristics of each stage and type vary from the rest and will have different image characteristics (EGM 2008, Habitat 2008). In the last decade, Very High Resolution (VHR) data became a popular tool for mapping informal settlements among geospatial technologists (Mason and Fraser 1998; Sliuzas et al. 2008; Hofmann 2001, 2014; Angeles et al. 2009; Sliuzas 2008). In the initial stage, pixel-based classification was the most commonly used technique for classifying slum areas (Jain et al. 2005, 2007; Weeks et al. 2007). Later, advances in image processing and classification techniques helped in improved classification

2.4 Mapping of Slums

31

and distinction of informal settlements from the formal ones (Ebert et al. 2009; Sliuzas et al. 2008). There are various approaches to slum mapping. A brief summary of these approaches is provided here. The “Census-based” approach is a method that uses census data for poverty mapping and uses social, economic, and infrastructure information available at the enumeration block level. (Baud et al. 2008; Kohli et al. 2012; Swadesh Kumar et al. 2014). This approach not only includes a regular survey at the national level, but also a sample survey carried out by recognized institutions such as the “National Sample Survey Organization” for the selected sample areas at the national level. For most countries, the census data collected at regular intervals serves as a good option for informal settlement mapping. Traditional field-based survey techniques, remote sensing-based slum extraction, and surveying using GPS instruments are other common approaches to studying slums. Each method has its advantages and disadvantages. The field survey method is time-consuming and labor-intensive when attempted at the city level. It is also expensive and sometimes biased. Remote sensing-based slum extraction needs expertise and often also requires sophisticated software and people skilled in its use. The GPS field survey needs both trained manpower and software. When the community is involved in mapping their settlement, it is known as the “participatory approach”. It requires the cooperation of the slum dwellers (Joshi et al. 2002). Karachi’s slum-infrastructures were mapped using this approach (Hasan 2006). Slums in the Kenyan city of Kisumu were mapped with all spatial and nonspatial attributes (Karanja 2010). This helped in creating GIS layers as an input to slum planning. Earth observation data only recently became an integral part of slum mapping. The synoptic coverage, temporal frequency, and high spatial resolution make satellitebased remote sensing the superior option for mapping and monitoring the spatial characteristics of slums and how their patterns evolve over time (Jain et al. 2005, 2007).

2.5 Slum Mapping in India In India, except for some union territories, the majority of the states are burdened with slum settlements. Some of these state governments have detailed plans and policies for slum development. Despite this, only a few major cities even in these state governments actually have slum maps. In many cases, these slum maps are little more than land-use maps that show the locations of slums as dots. More often than not, these maps are also outdated. For sustainable planning, we need more than a dot map for we need the size, extension, and exact location of slums in the city and their neighborhood. This is what will allow planners to choose the right option for slum development. This will give an idea about the space available within the slum and its neighborhood, the access that it has to basic facilities, the livelihoods of its inhabitants, etc. Such a slum map can then be integrated with the

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2 Slums in India

city development plan and the master plan of the city for inclusive planning. These slum maps will provide knowledge about the vulnerability of slums and thus help in resilient planning. Mapping of slums is, therefore, essential and using geospatial technology for mapping becomes the need of the hour since continuous monitoring and updating of slums are imperative for sustainable urban planning. There are very few slum studies based in India (Sur et al. 2004). Most of them are based on primary field surveys and only very few used or combined technology with field validation. In the paper, “Identification/Mapping of Slum Environment using IKONOS Satellite Data: A Case Study of Dehradun, India”, the following factors were taken into consideration for the identification of slums: compact tiny structures; tonal variations in the roof materials of the houses; haphazard and unorganized approach roads and kachcha roads; proximity to the wetlands such as river banks or canals, and open areas lying close to the transportation lines (Sur et al. 2004). In another presentation, “Approaches for extraction of Slum area from highresolution satellite data, Case study: Dehradun, India”, Jain et al. (2007), mentioned that visually interpreting the high-resolution satellite imagery has great potential and helps in mapping slums in detail. In the article, “Locating the Livelihood Problems for the Promotion of Sustainable Life in Slums of Dehradun, Uttrakhand, India using Ikonos High-Resolution Satellite Image”, Swadesh Kumar et al. (2014) used satellite imagery to locate the slums of Dehradun city. An area with 20–25 households without concrete roofs, with no basic facilities such as potable water and sanitation, was used as the working definition for a slum, and these were then identified. Chandramouli’s (2003) paper on the slums of Chennai was purely based on 2001 census details. Baud et al. (2008) used GIS along with census of India data to map multiple deprivations at the administrative ward level in Delhi, India. Ranguelova et al. (2019), studied the slums of Bangalore and Kalyan using earth observation data from Google Earth. They adopted image processing techniques and SVM classification. They were interested in exploring this method for detecting slums, the applicability of it in images of different resolutions, and the potential for generalization over different locations. Kumar (2014) used census data to study the slum distribution in major metropolitan cities of India. Mahabir et al. (2016) in their study on, “Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, analyzed the location of slums in Pune as a product of socio-cultural (neighborhood) and economic factors (cost of commutation, accessibility to public goods) and they confirmed that slum dwellers choose vulnerable sites due to their poverty. Kit et al. (2011), used image characteristics to identify slums of Hyderabad which have dense and compact settlements that often represent slums of urban India. A study on the slums of Pune using Quick-Bird satellite data and object-based image analysis (OBIA) resulted in high accuracy classification of slums (Shekhar 2012). Image characteristics and texture-based (GLCM) measures were used in this study for detecting the slums. “Participatory mapping for city-wide slum upgrading in India”, was a project work undertaken by a Non-Governmental Organization, SPARC, Mumbai, India

2.5 Slum Mapping in India

33

using Google Earth images, open GIS software (QGIS), and GPS for slum mapping. They made use of the participatory approach with help from the community for mapping slums in Cuttack, Odisha (2012). Roy et al. (2018) analyzed Bangalore slums using a socio-economic survey. Montana et al. (2016) used satellite data and population surveys to distinguish slums from non-slums in Uttar Pradesh, India. Emily Rains et al. (2018) studied three major cities of India—Bangalore, Jaipur, and Patna, and discussed the omission of slum neighborhood details in official data publications which will lead to the failure of slum development policy and planning activities since it leads to defective conclusions about the depth, breadth, and persistence of poverty.

2.6 Spatial Information and Slums The urban environment is a complex one with different kinds of land use/land cover and often consists of small objects when compared with the spatial resolution of satellite sensors. Since the urban environment is a combination of man-made and natural objects (Fig. 2.5), and when energy interacts with objects with different wavelengths, the reflectance of various objects within the pixel (spatial resolution) also varies in different parts of the spectrum. Therefore, a “mixed pixel” is created with the presence of different objects of varied reflectance. This is mainly found in “residential land use” since it has the greatest variety of objects (tree, concrete, tar road, water, etc.) with unique spectral signatures in a single pixel (Weng and Quattrochi 2006).

Fig. 2.5 Urban residential area (Pune)

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2 Slums in India

Fig. 2.6 Liss IV (5 m) and GeoEye data (0.5 m)

For example, urban objects such as metal roads, concrete houses, lawns, trees in home gardens, public green space, bare soil, shrubs, swimming pools, footpaths, parking areas may each have a unique spectral response, and yet all need to be considered as (Fig. 2.5) part of a “residential-class” (Weng and Quattrochi 2006; Myint et al. 2011). Therefore, it is wise to choose remote sensing data with a high spatial resolution (sub-meter resolution) rather than data with higher spectral resolution. Moreover, urban features are made up of a variety of materials that are spectrally different from each other. For example, steel, plastic, rubber, glass, wood, and asbestos are all mixed in different proportions in different features. In general, moderate (5 m), high (1 or 2 m), and very high (≤0.5 m) spatial resolution (Fig. 2.6) optical remote sensing data should be good enough to offer necessary spectral contrast to identify the objects (e.g., apartments, individual buildings, multistorey buildings, and slum households) and distinguish it from the background (e.g., green space and metal roads) (Myint 2006). Slum households are very small and most of them are single floor/plus one floor or quite rarely multistorey apartments. Therefore, they do not show huge shadows in the satellite images. Because of their compact structure, they don’t display internal roads (Fig. 2.7). These settlements lack planned layouts and planned green spaces such as domestic gardens, avenues, and open spaces between houses. Their roofs are mostly made up of tin sheets, stone slabs, PVC sheets (banners), gunny bags, and other locally available materials (Picture 2.1). Hence, their reflection properties are different from normal concrete formal structures. This lends a different texture and tone to slum settlements. This is the reason they differ from formal settlements when we interpret the images visually. The following satellite images show the characteristics of a slum and how they differ from non-slum (formal settlements). The slum scenes are taken from a metropolitan city of India for better understanding. Figures 2.8 and 2.9 show the slums of Mumbai.

2.7 Remotely Sensed Slums

35

Fig. 2.7 Slum and non-slum

Picture 2.1 Slum scene at Pune

2.7 Remotely Sensed Slums As discussed previously, slums are not seen only in big cities. Even second-order cities show slum development. This section is a visual tour of slums through satellite images taken from Google Earth Pro. Figures 2.10, 2.11, 2.12, 2.13, 2.14, 2.15, 2.16, 2.17 and 2.18 show the slums identified from the very high-resolution data using image interpretation elements. Most of these slum scenes are taken from Class-II cities and towns. The images of different states of India give a common understanding of slums. When we go through these images, one can understand, how slums appear in satellite images. Their compact structure, small households, lack of any street pattern, dearth of vegetation, and with small or absent shadows. Another important understanding

36

Fig. 2.8 Slum characteristics in a high-resolution data

Fig. 2.9 Difference between slums and non-slums

2 Slums in India

2.7 Remotely Sensed Slums

37

Fig. 2.10 Slums in Tamil Nadu and Kerala

one can gain from these images is that most slums are located in vulnerable sites. The general understanding of “locations of slums” such as near railway lines, close to the river, and open lands close to airports and sometimes amidst residential areas helped to identify the slums from remotely sensed data. Along with location, the texture of slum settlements, their size, and their shape is also helpful for the visual interpretation of images. The slum houses are made up of different materials compared to formal concrete houses. Hence, they display a different texture in satellite images. For example, in the above images, slums are located on the bank of the river and are found near the water body (Fig. 2.10). Slums can also be seen along the canal and major roads in Figs. 2.11 and 2.12. Clustered tiny slum settlements can be seen in Fig. 2.13. The residential neighborhood always gives the urban poor lots of job opportunities and that’s why the residents of slums preferably settle near the middleand high-income residential areas. In Figs. 2.14, 2.15, and 2.16, we can see slums located along the railway lines and nallah. In Hazaribagh, slums are located in the middle of the residential neighborhood. In Fig. 2.17, Delhi slums are located near the railway lines, and in Gaya, we can once again see slums on the river bank. Slums are found along the major roads in Fig. 2.18.

Fig. 2.11 Slums in Karnataka and Telangana

38

Fig. 2.12 Slums in Andhra Pradesh and Odisha

Fig. 2.13 Slums in Maharashtra and Madhya Pradesh

Fig. 2.14 Slums in Chhattisgarh and Jharkhand

2 Slums in India

2.7 Remotely Sensed Slums

39

Fig. 2.15 Slums in Gujarat and Rajasthan

Fig. 2.16 Slums in Uttar Pradesh and Uttarakhand

Fig. 2.17 Slums in Delhi and Bihar

From these Figures, one can understand that irrespective of the state and the size of the city, slum settlements share some common characteristics. The locations vary, but their conditions remain the same. The field photographs taken during the field visits at Pune and Chennai give a better understanding of slum characteristics and help in identifying slums from satellite images (Pictures 2.2 and 2.3).

40

Fig. 2.18 Slums in Punjab and Haryana

Picture 2.2 Slums on the river bank at Pune (Photo Courtesy -Raajashekhar)

2 Slums in India

2.7 Remotely Sensed Slums

41

Picture 2.3 Slums along the railway line at Pune (Photo Courtesy -Raajashekhar)

Slum near the residential area- Chennai (Photo Courtesy -R V Govindaraj)

With this understanding, we shall now study a second-order city and its slums which form the case study for the present work. The next chapter introduces the study area in detail.

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2 Slums in India

References Angeles G, Lance P, Barden-O’Fallon J, Islam N, Mahbub AQM, Nazem NI (2009) The 2005 census and mapping of slums in Bangladesh: design, select results and application. Int J Health Geogr 2009(8):19 Baud I, Sridharan N, Pfeffer K (2008) Mapping urban poverty for local governance in an Indian mega-city: the case of Delhi. Urban Stud 45(7):1385–1412 Census of India, Explanatory note, Ministry of statistics and program implementation www.mospi. gov.in Chandramouli Dr C (2003) Slums in Chennai: a profile”. In: Bunch MJ, Suresh VM, Kumaran TV (eds) Proceedings of the third international conference on environment and health. Chennai, India, 15–17 Dec 2003 Ebert A, Kerle N, Stein A (2009) Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data. Nat Hazards 48:275–294. https://doi.org/10.1007/s11069-008-9264-0 EGM (2008) Expert group meeting on slum identification and mapping at: faculty ITC, University of Twente, Enschede The Netherlands, 21–23 May 2008, Organized by UN- HABITAT Habitat UN (2008) State of the World Cities 2010/2011, bridging the urban divide published by Earth scan in the UK and USA in 2008 for and on behalf of the United Nations Human Settlements Programme (UN-HABITAT) Hasan A (2006) Orangi pilot project: the expansion of work beyond orangi and the mapping of informal settlements and infrastructure. Environ Urban-Environ Urban 18:451–480. https://doi. org/10.1177/0956247806069626 Hofmann P (2001) Detecting informal settlements from ikonos image data using methods of objectoriented image analysis-an example from cape town (South Africa) Paper presented at the remote sensing of urban areas Hofmann. P (2014) Defining robustness measures for OBIA framework—a case study for detecting informal settlements in global urban monitoring and assessment through earth observation. In: Weng Q (ed). CRC Press Taylor & Francis, Boca Raton, pp 303–324 Jain S et al (2007) Use of IKONOS satellite data to identify informal settlements in Dehradun India. Int J Remote Sens 28(15):3227–3233 Jain S, Sokhi BS, Sur U (2005) Slum identification using high-resolution satellite data. GIM Int 19(9) Joshi P, Sen S, Jane H (2002) Experiences with surveying and mapping Pune and Sangli slums on a geographical information system (GIS). Environ Urban 14, 225–240 Karanja I (2010) An enumeration and mapping of informal settlements in Kisumu, Kenya, implemented by their inhabitants. Environ Urban 22(1) Kit O, Lüdeke MKB, Reckienc D (2011) Texture-based identification of urban slums in Hyderabad, Inida using remote sensing data. Appl Geogr 32(2):660–667 Kohli D, Sliuzas R, Kerle N, Stein A (2012) Local ontologies for object-based slum identification and classification. In: Proceedings of the 4th GEOBIA. Rio de Janeiro, Brazil. p 201, 7–9 May 2012 Kumar J (2014) Slums in India: a focus on metropolitan cities. Int J Dev Res Full Length Res Artic 4:388–393 Kumar S, Dutta V, Jain S, et al (2014) Locating the livelihood problems for the promotion of sustainable life in slums of Dehradun. IOSR J Environ Sci, Toxicol Food Technol (IOSR-JESTFT) 8(1):31–36. e-ISSN: 2319-2402, p- ISSN: 2319-2399 www.iosrjournals.org Mahabir R, Crooks A, Croitoru A, Agouris P (2016) The study of slums as social and physical constructs: challenges and emerging research opportunities. Reg Stud Reg Sci 3(1):399–419. https://doi.org/10.1080/21681376.2016.1229130 Mason SO, Fraser CS (1998) Image sources for informal settlement management. Photogram Rec 16(92):313–330 Ministry of Urban Development, Government of India’s report (2013)

References

43

Montana L, Lance PM, Mankoff C, Speizer IS, Guilkey D (2016) Using satellite data to delineate slum and non-slum sample domains for an urban population survey in Uttar Pradesh India. Spat Demogr 4(1):1–16. https://doi.org/10.1007/s40980-015-0007-z Myint SW (2006) Urban Mapping with Geospatial Algorithms, Urban Remote Sensing (Qihao Weng and Dale Quattrochi, editors), Taylor and Frances, pp. 109–135 Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011) Per-pixel versus object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115(5):1145–1161 Rains E, Krishna A, Wibbels E (2018) Urbanization and India’s slum continuum: evidence on the range of policy needs and scope of mobility, reference number: C-35309-INC-1 Working paper, International growth centre, London school of economics Ranguelova E, Weel B, Roy D, Kuffer M, Pfeffer K, Lees M (2019) Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, Inida, European. J Remote Sens 52(sup1):40–61. https://doi.org/10.1080/22797254.2018.1535838 Roy D, Palavalli B, Menon N, King R, Pfeffer K, Lees M, Sloot PMA ( 2018) Survey-based socioeconomic data from slums in Bangalore, India. Sci Data J 5(1):170–200, 09 Jan 2018. https:// doi.org/10.1038/sdata.2017.200 Shekhar S (2012) Detecting slums from quick bird data in Pune using an object-oriented approach. Int Arch Photogramm Remote Sens Spat Inf Sci 39, 519–524 Sliuzas R, Kerle N, Kuffer M (2008) Object-oriented mapping of urban poverty and deprivation. Paper presented at the EARSeL workshop on remote sensing for developing countries in conjunction with GISDECO 8 Sur U, Jain S, Sokhi BS (2004) Identification/mapping of slum environment using IKONOS satellite data: a case study of Dehradun, India. https://www.gisdevelopment.net/application/environment/ pp/mi04011.htm UNDP (2016) United nations development program- human development report 2016. https://hdr. undp.org/en/content/human-development-report-2016-overview UN-WUP (2018) World urbanization prospects, 2018 revision, Department of economic and social affairs population dynamics. https://population.un.org/wup/ Weeks J, Hill A, Stow D, Getis A, Fugate D (2007) Can we spot a neighbourhood from the air? defining neighbourhood structure in accra. Ghana, Geojournal Weng Q, Quattrochi DA (2006) Urban remote sensing. CRC Press, Taylor and Francis, p 448 World bank (2017) Urban population (% of total population), United nations population division. World Urbanization Prospects: 2018 Revision. https://data.worldbank.org/indicator/SP. URB.TOTL.IN.ZS https://www.censusindia.gov.in www.mhupa.gov.in https://www.census2011.co.in/slums.php www.censusindia.gov.in. Primary Census Abstract for Slum, https://www.censusindia.gov.in/2011Documents/Slum-26-09-13.pdf

Chapter 3

Case Study: Kalaburagi

Slums represent the worst of urban poverty and inequality. Kofi A. Annan Secretary-General, UN

Abstract Case studies are in-depth investigations of an event or a community. Case studies explore complicated issues in their natural environment. They also help in experimenting with new policy ideas and government schemes. The output helps us understand the loopholes or lacunas and assists in refining the policy and fine-tuning the existing schemes. They offer insight into a phenomenon or a process that cannot be attained by any other approach. Hence, to understand the characteristics of slums, problems of slum dwellers, issues of planners, we need to study at least the slums of one city in detail. This will also elucidate the success and failures of the efforts taken by the central, state, and local governments in uplifting the slum dwellers. This, in turn, will help us plan sustainable cities. With this background, it was decided to study the slums of a non-metropolitan city. Under the HUDCO (Housing and Urban Development Corporation) project, the city of Kalaburagi had been chosen as a case study. Mapping of slums experimented with new combinatory methods like slum ontology and spatial decision support systems combined with a participatory GIS. This chapter creates a base for building slum ontology, participatory slum mapping, and spatial decision support system (SDSS) for slums in the present work. The field photographs give a visual tour of the slums of Kalaburagi. The detailed study on slums helps in understanding the role of multi-stakeholders in the sustainable management of slums. Keywords Case study · Kalaburagi · Karnataka · Slums · Government schemes

3.1 Introduction Slums are an integral part of urban India. The twenty-first century is the urban century. It is the need of the hour to understand this fact and act diligently. One cannot plan for a sustainable city without adding slums into the equation. To plan, we need to know © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_3

45

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3 Case Study: Kalaburagi

their location and the prevailing conditions in the slums. For that, the mapping of slums is crucial. Mapping accompanied by other non-spatial information aids better management. To showcase the necessity of mapping, modeling their future growth, and managing them efficiently, Kalaburagi city in Karnataka has been chosen. This case study is an in-depth study of a particular city. A detailed study on its slums helped demonstrate the significance of geospatial techniques, and the importance of slum ontology, integration of various stakeholders in mapping, modeling, and managing the slums. The methodology adopted in the present case is highly suitable to Indian conditions and applicable to all kinds of Indian cities. Kalaburagi city is a second-order city and has 11% of its population living in slums as per the 2011 census. The present chapter describes the study area, objectives, and methodology adopted in the study in detail. This will create a base for building slum ontology, participatory slum mapping, and spatial decision support system (SDSS) for slums in the present work.

3.2 Introduction to Kalaburagi “KALABURAGI” (formerly known as Gulbarga) is the fourth largest city of Karnataka State and located in its northern part. It is the largest city in Hyderabad Karnataka (Now officially known as the Kalyana-Karnataka) region and the district capital of the Kalaburagi District with an urban area of 64.00 km2 (Shekhar and Aryal 2019, Shekhar 2020). Figure 3.1 shows the location of Kalaburagi at national, state, and district levels. It includes its 58 administrative units that also serve to depict the urban outgrowths located on the southeastern side of Kalaburagi city (Fig. 3.2). Kalaburagi is known for its architecture, religious places, and it is more importantly, a commercial center of the northern part of Karnataka state. It is also an important educational and tourist center (Shekhar and Aryal 2019). Though Kalaburagi is located in a backward region, it holds the potential to attract many developmental activities and is now experiencing rapid urbanization. The urban growth is mainly due to the migration of rural folk from neighboring districts and that has induced the growth of the slum population in the city. Thus, slums remain an inescapable aspect of rapid urbanization (Shekhar 2020). As per provisional reports of the 2011 Census, Kalaburagi has a population of 0.532 million and is, therefore, not a metropolitan city; its urban population including the outgrowth of the city is 0.542 million. Kalaburagi was initially governed by a municipal council and it came under the governance of the Municipal Corporation in 1982. The city’s master plan is developed by the Kalaburagi Urban Development Authority (KUDA) (Fig. 3.3).

3.3 Slum Situation in Kalaburagi City

47

Fig. 3.1 Location of Kalaburagi city

3.3 Slum Situation in Kalaburagi City As per the Ministry of Urban Development, Government of India’s report (2013), 11% of Kalaburagi’s population occupied a total slum area of 1.48 km2 (Shekhar 2020). Only 42 slums out of 60 are notified and the remaining are only recognized (non-notified) slums. Kalaburagi, by virtue of being a historical city, has been occupied by slums for more than 10 years and nearly 30% of the slum population belongs to this category. When we consider the location of slums, some (7 slums) are located in low lying areas recognized as vulnerable areas by the municipal administration. Of the remaining 53 slums, 27 are situated near nallahs and lakes which are prone to natural disasters such as flooding. The rest of the slums are present along the major road network and railway lines. Most of these informal settlements (50 slums) are clustered around the Central Business District (CBD) or residential area, market area, near the temples, and masjid, and only a few slums (10 slums) are found in the urban periphery/fringe area (www.mhupa.gov.in). When we talk about tenure, only 47% of the slums have certificates of secure tenure and have access to basic amenities. The rest of the slums (53%) do not have

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3 Case Study: Kalaburagi

Fig. 3.2 Geo-eye image of Kalaburagi city

proper tenure and this is an issue that needs attention. Whereas some “identified” settlements that possess the security of tenure enjoy the services provided by the local government, these services remain inaccessible to “unidentified” slums. Table 3.1 presents the ownership of land occupied by the slum dwellers. The Karnataka Slum Board lists 52 notified slum areas in Kalaburagi. It includes rehabilitated slum areas such as Aashraya colonies. There are 9 non-notified slums and that brings the total number of slums in Kalaburagi to 61. The state government and the urban local body have taken the initiative and implemented various slum development programs. Unfortunately, the developed slums are not removed from

3.3 Slum Situation in Kalaburagi City

49

600,000

540,000

Population

500,000

430,265

400,000

310,920

300,000

221,325

200,000

145,588 97,069

100,000 0 1961

1971

1981

1991

2001

2011

Census Year Fig. 3.3 Population growth of Kalaburagi city

Table 3.1 Ownership of land occupied by slums

Ownership of the land

Percentage of slums occupied the land (%)

City Corporation, Kalaburagi

54

State Government

18

Private agencies

28

Source http://mhupa.gov.in

the slum list. Rather the conditions of the rehabilitated slums are among the worst cases resulting in them being added as “new slums” to the existing slum list. The following table gives a glimpse of the programs implemented in the recent past (Table 3.2). Even though the counting of slum population was done during the 2001 census, it is the 2011 census that gave the complete details of slum conditions at the city level. It gives more than 80 attributes for slums including housing conditions, demographic, social, and economic conditions of the slum population. Among the slum attributes given in the 2011 census, the notable attributes which can clearly distinguish slums and non-slums are the roof of the house, housing condition, and the floor material. The following bar diagrams explain the household conditions of the slums in Kalaburagi as per the 2011 census (Figs. 3.4, 3.5 and 3.6). The slum conditions were also documented through field photographs during the slum visit (Pictures 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.20, 3.21, 3.22, 3.23, 3.24 and 3.25).

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3 Case Study: Kalaburagi

Table 3.2 Slum development programs under Government of Karnataka Year of movement

Earlier location

Current location

Under which scheme

1996

Roja, Sunder Nagar, Bapu Nagar, Station Bazar

Ambedkar Aasharaya Colony

Aasharaya Yojana

1995

Tarpail, Indira Nagar

Dariyapur Slum Board Office GDA Layout Aasharaya Colony 1

SC/ST Scheme

1995

Tarpail, Indira Nagar

Dariyapur Slum Board Office GDA Layout Aasharaya Colony 2

SC/ST Scheme

2002

People were staying in Gandhi Leprosy Aland this area in huts for a Nagar long time, but in the year 1999 due to a fire that broke out, the government-sanctioned a rehabilitation program and handed these people newly constructed houses in 2002. This above statement was given by Hanamanth Devaru, Secretary of this Colony

Government of Karnataka Aasharaya Yojana

2009

Dabarabad, and their own property

Pandit Deendayal Upadyanagar Aasharaya Colony

Karnataka Slum Development Board (JNNURM)

2002

Khaadi Bowdi, Noorani Colony, Yadulla Colony, Malagati, Hagaraga

Sonia Gandhi Aasharaya Colony

Aasharaya Yojana

1999

Dabarabad, Jaferabad

SM Krishna Aasharaya Colony

Aasharaya Yojana

Source Office of Registrar General and Census Commissioner, India

3.4 Objectives With this background, the study aimed to contribute some viable methodologies to elevate the quality of life of slum dwellers to a decent one and for better implementation of slum policies. This has been elucidated by taking a case study, Kalaburagi. The main objectives of this study were: 1.

2.

Mapping: Mapping the existing slums of Kalaburagi using ontology-based slum identification from very high-resolution data and validating the slum maps based on slum enumeration blocks of the 2011 census. Modeling: Developing a model to understand the factors which are responsible for present growth, as well as to predict the future growth of slums.

3.4 Objectives

51

Roof type of Slum Households Others

30 4,998 1,781 1,864

Roof types

G.I./Metal/ Asbestos sheets Burnt Brick

122 238 399 142 94

Hand made Tiles

Grass/ Thatch/ Bamboo/ Wood/Mud etc. -

1,000

2,000

3,000

4,000

5,000

6,000

Number of Households Fig. 3.4 Roof type of slum households as given in the 2011 census

NUMBER OF HOUSEHOLDS

Usage of Slum House holds 7,000 6,000 5,000 4,000 3,000 2,000 1,000 Resident cum other use Residence

Good

Livable

58

67

Dilapidated 5

6,304

2,930

304

Fig. 3.5 Housing conditions and usage in slums as given in the 2011 census

3. 4. 5.

Estimating the housing demand of urban poor and suggesting a suitable site for the rehabilitation program. Managing: Suggesting better interventions for and in the form of government policies. Spatial Decision Support System (SDSS) for slums.

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3 Case Study: Kalaburagi

Number of Households

Material used for Floor in Slums 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 -

7,755

631 Mud

40

178

140

Wood/ Burnt Brick Bamboo

Stone

Cement

806

118

Mosaic/ Any other Floor Ɵles material

Types of material Fig. 3.6 Materials used for the floor in slums as given in the 2011 census

Picture 3.1 Housing condition of Borabai Nagar

The objectives were selected based on the existing scenario at the global, national, and regional levels. The slum development program (Rajeev Awas Yojana), currently known as the Prime Minister Awas Yojana program is the major source of inspiration for these objectives. Since it already has the GIS component, the best possible way of using this technology to achieve the “Affordable Housing for all” mission forms the base of this work.

3.4 Objectives

Picture 3.2 Housing condition of Brahmpur Waddar Wada

Picture 3.3 Housing condition of Vijay Nagar

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Picture 3.4 Housing condition of Tarfail (East)

Picture 3.5 Interaction with slum dwellers of Borabai Nagar

3 Case Study: Kalaburagi

3.4 Objectives

Picture 3.6 Housing condition of Gazipur

Picture 3.7 Cement Road in Lambani Tanda

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Picture 3.8 Housing condition of Ramji Nagar

Picture 3.9 Housing condition of Indira Nagar

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3.4 Objectives

Picture 3.10 Housing condition of Rajapur Village

Picture 3.11 Housing condition of Jagajeevan Ram Nagar (R.T.O Office Back Area)

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Picture 3.12 Housing condition of Bhavani Nagar

Picture 3.13 Bapu Nagar-Storm Water Drainage

3 Case Study: Kalaburagi

3.4 Objectives

Picture 3.14 Housing condition of Mangarwadi

Picture 3.15 Housing condition of Mangarwadi

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Picture 3.16 Road belongs to Mangarwadi

Picture 3.17 Housing condition of Bapu Nagar

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3.4 Objectives

Picture 3.18 Housing condition of Shamsunder Nagar

Picture 3.19 Housing condition of Arya Nagar

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Picture 3.20 Housing condition of Jai Bheem Nagar

Picture 3.21 Buffalo farming for milk in Nehru Nagar Langoti Peer Darga

3.4 Objectives

Picture 3.22 Housing condition of Nehru Nagar (Filter bed Area)

Picture 3.23 Housing condition of Syed Galli

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Picture 3.24 Housing condition of Khanapur

Picture 3.25 Housing condition of Basava Nagar

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3.5 Methodology

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Fig. 3.7 Flow chart represents the overall methodology

3.5 Methodology There are three major steps involved in this study. Those are mapping, modeling, and managing the slums. The first is achieved using ontology-based slum detection from very high-resolution (VHR) data, the second using cellular automata coupled with multi-criteria evaluation techniques to model the slums and estimating the housing demand, and the third one by building a Slum Spatial Decision Support System (Slum SDSS). Detecting slums from Very High-Resolution data (VHR) demands more sophisticated techniques, hence ontology-based image classification methods were used and the same is explained in Chap. 4. Similarly, for modeling of the slums, the state-ofthe-art techniques of cellular automata and the MCE method were applied and the detailed methodology is given in Chap. 5. The method used to estimate the housing demand is given in Chap. 6 and the last chapter explains the methods adopted in building the SDSS. The flow chart gives the overall methodology (Fig. 3.7) followed in this study.

References Shekhar S (2020) Effective management of slums—case study of Kalaburagi city, Karnataka, India. J Urban Manag 9(1):35–53. ISSN 2226-5856. https://doi.org/10.1016/j.jum.2019.09.001 Shekhar S, Aryal J (2019) Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning. Urban For Urban Green 46(September). Article 126450. https://doi.org/10.1016/j.ufug.2019.126450 www.mhupa.gov.in

Chapter 4

Slum Identification and Validation

About one in six Indian city residents lives in an urban slum with unsanitary conditions that are unfit for human habitation. —2011 Slum Census

Abstract With increasing urbanization at the global level, slums too are going to grow. Hence, a solution is urgently required. When we look at slum development, we should begin by putting them on the map. Mapping their spatial extent and locating them in the cityscape will help planners prepare for their betterment. Mapping with outdated technologies will provide us with the needed information in the required format to aid in the planning process. Therefore, it becomes necessary to go for geospatial technologies such as earth observation data and GIS. Identifying slums from satellite data needs domain expertise and training. In the present chapter, slums were identified from high-resolution satellite data with the help of cognitivebased technology such as ontology. The slum ontology was built with the help of domain experts, planners, local administrators, and slum dwellers. The ontology building process has been explained in different phases. Through the participatory GIS approach, using slum ontology, the slums of Kalaburagi city were mapped and the city’s first slum map was prepared. The Urban Frame Survey (UFS) maps were used for validating the slum map. Thus, the slums of Kalaburagi city were mapped using a slum ontology built exclusively for that city. It can also be extended to mapping slums of any Indian city since the slum ontology is based on the Indian scenario. The same has been explained with sample images of the top 10 cities of India. Keywords Urban · Slum · Ontology · Kalaburagi · Participatory GIS · UFS maps

4.1 Understanding Slum The origin of the term, “Slum” dates back to the first part of the nineteenth century (Cowie 1996). During the industrial revolution, the poorly paid laborers used to live in the dark streets of the city, and the “unhealthy living conditions” that prevailed in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_4

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the industrial cities of Great Britain, led to the origin of the term “slum”. The local authorities of such industrial cities were empowered to demolish and replace these unhealthy settlements and the British legislation associated such conditions with the term “slum” (Garside 1988). Later due to the colonial impact this term is now widely used in colonized countries including India (Huchzermeyer 2008). Thus, slums were the outcome of lopsided industrialization. Ever since 1870, slums have become an integral part of the urban landscape. Cities offer new opportunities that attract rural folk. However, these poor migrants cannot afford proper housing, for it remains a luxury way beyond their income levels. They then end up settling themselves in slum areas as they have no other alternatives. Due to the lack of even minimum infrastructure and overcrowding, these areas have become an unhealthy environment. These areas are invisible to the local authorities for inclusion in city planning and for any development despite national policies and development schemes. In the process of building ontologies, knowledge gathered about slums revealed that definitions of it varied from country to country. To have a common understanding, a definition at the global level was considered. The “UN-HABITAT” (2006) defines a slum as A slum household is one that lacks one or more of the four criteria, namely: 1.

Durable housing of a permanent nature,

2.

Sufficient living space, which means no more than three people sharing the same room,

3.

Easy access to safe water in sufficient amounts at an affordable price, and

4.

Access to adequate sanitation in the form of a private or public toilet shared by a reasonable number of households.

From the above definition, it is clear that the UN description is mostly concentrated on the state of a single slum household rather than a slum area. As the Indian scenario has a group of households living in a highly congested environment, it is difficult to follow the UN definition approach and there was thus a need to look for areabased definitions. Nevertheless, the definition ignited thinking and discussion of the characteristics of slum areas. The following characteristics of slum areas are common throughout India—overcrowding, dilapidated buildings, no secure tenure, lack of safe potable water, lack of hygiene or sanitation facilities, lack of proper ventilation, poor lighting conditions, poor roads, insufficient living space, absence of drainage system (sewage and stormwater), and an unhealthy environment. These slums are mostly found near vulnerable/hazardous locations. Based on these observations, the slum areas were marked on the city map as point features. These efforts are seen only in major cities and million-plus cities in India. When we talk about achieving the Sustainable Development Goals (SDGs) in 2030, by reducing poverty and building sustainable cities, it is obligatory to include slum dwellers who contribute to the city’s economic development into city planning and it is imperative to put them on the map for their improvement. At this juncture, knowing the location of slums in a city and their environmental conditions becomes essential for inclusive city planning. It demands the involvement

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of urban local bodies, NGOs, and slum dwellers along with state of art technology such as geoinformatics. Therefore, identification of a slum from remotely sensed data forms the base for spatial data which is imperative for city planning. Identification of slums from remote sensing data requires some basic knowledge about slums and their characteristics such as what they will look like in a satellite image, where one can see slums in a city image (satellite) and how they can differ from non-slum areas, etc. This is where the ontology comes in, a cognitive and scientific approach to identifying slums.

4.2 Overview of Ontology and Approaches to Building It The word ontology was initially borrowed from philosophy and is now used in several other disciplines (Roussey et al. 2011). Guarino (1995) differentiated ontologies into “philosophical ontology”, and “knowledge engineering ontologies” (Fonseca 2007). Ontology was associated with a priori perception, knowledge, or language in the philosophical tradition. It is also classified into reality-based and human conceptualizations of reality (Smith and Mark 1998). In knowledge engineering, formal ontology copes with experience: it is used for “an explicit specification of a conceptualization” (Gruber 1993) or a “shared understanding of some domain of interest” (Uschold and Grüninger 1996). So “what is an ontology? Does it improve our understanding of geographical space? Or is ontology a modern, flowery phrase for former concepts, like formal data models, formal specifications, or semantic networks?”, It all hinges on how the users use the term (Winter 2001). Since the application of ontology to spatial problems became possible (Agarwal 2005), ontology became a significant research theme in GI Science (Fonseka et al. 2002). Ontologies, in general, are built to enable knowledge about a particular domain by defining its units, its sub-units, its meanings, and the associations between all those (Fonseca and Egenhofer 1999). Ontology is, therefore, a simple technique to make obvious and enhance the understanding of a particular domain. The research and developments in the field of spatial ontology have created a specific lexicon and a theoretical structure, to respond to the specific demands of the spatial domain. Smith and Mark (1998) proposed the creation of spatial ontologies with the objective of getting a better understanding of the geographic world. Gruber’s definition is that “an ontology specifies the concepts, relationships, and other distinctions that are relevant for modeling a domain, and the specification is in the form of definitions of representational vocabulary (classes, relations, and so forth), which provide meanings for the vocabulary and formal constraints on its coherent use”. According to Gruber (1992), ontology is a “body of formally represented knowledge [that] is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them” (Genesereth and Nilsson 1987; Gruber 1995). Domain-based ontologies are mostly related to the classification of the domain and arranging them

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in orders at a successive level based on their relations. However, ontologies can be more than a taxonomic hierarchy (Gruber 1993). Domain-ontologies can be represented by; (a) domains, which describe the vocabulary used in a specific field of knowledge; (b) tasks, which describe the vocabulary used in a specific activity of the field; and (c) representations, which explain the concepts of formal entities. There are domain ontologies in which a user’s group conceptualizes and visualizes a specific phenomenon and the integration of many domain ontologies results in the formation of core reference ontology. The critical character of ontologies is the imparting of knowledge, which develops the common systems. That helps to create an amalgamation of various aspects on the same substance of interest, through a persistent methodology. In simple words, ontologies have great potential to describe the semantics of a domain in both a human-understandable and computer-processable form (Arara and Benslimane 2004). Ontologies can be expressed through standard associations (Novello et al. n.d.— as mentioned in the thesis of Montenegro 2010) to qualify the relations between entities, namely: taxonomy (is a; type of), partonomy (part of), mereology (“part of all” theory), chronology (precedents between concepts) and topology (theory of limit and border).

4.2.1 Existing Approaches to Building Ontologies There is no one right approach and no sole right result for building ontologies. “Developing an ontology is an iterative process. Usually one starts with a rough first pass at an ontology, and then revises and refines the evolving ontology in order to fill in the details” (Noy et al. 2000; Noy and Mcguinness 2001). “As a result, one will almost certainly need to revise the initial ontology. This process of iterative design will likely continue throughout the entire lifecycle of the ontology” (Sachs et al. 2006). Before one starts building an ontology, we have to be clear about the purpose of building the ontology, its type, and whether it is specific or generic, and at the same time understanding the fact that ontologies are representations of the real world, and thus its content must be a true representation of it. In designing the ontology used in this project, there were three approaches (Gandon 2002 as stated in Jacques Teller et al. 2005) which were considered: • Bottom-up approach proposed by Van Der Vet and Mars (1998): starting from the most specific concepts and building the conceptual hierarchy by generalization. This approach is prone to provide tailored and specific ontologies with fine granularity concepts. • Top-down approach recommended by Sowa (1995): starting from the most generic concept and building the structure by specialization. This approach

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is prone to the reuse of ontologies and inclusion of high-level philosophical considerations, which can be very interesting for maintaining consistency. • Middle-out approach of Uschold and Grüninger (1996): identifying core concepts in each domain. Once identified, these can then be generalized and later specialized to complete the ontology. This approach is prone to encourage the emergence of thematic fields and to enhance the modularity and stability of the result. In addition to these three methods, some methods use existing resources such as the existing vocabularies approach (Aussenac-Gilles and Soergel 2005) and existing database conceptual models. The prevailing textual dictionary or glossary can act as a starting point in the first method, and it is similar to the “middle-out approach” in collecting optimal concepts and relations. The second method is a typical database model similar to the “Entity-Relationship (ER) model”. The ER models of various databases are amalgamated into a unique database as the conversion of relationships into ontologies is possible by sharing and exchanging the information in the database. This can be brought under the “bottom-up approach” since it is taking into consideration every entity in detail in order to build the ontology.

4.3 Slum Ontology In describing slums, many components are involved such as geographic, social, and economical ones. The best method to explicate these aspects is to elaborate it by creating a “knowledge model”, also called an ontology (Gruber 2005), that permits to establish an extended knowledge base, connecting all its characteristics. Having analyzed the various methodologies, and arrived at the understanding that “urban planning” is a unique discipline with many aspects, we have chosen the “slum domain” to develop “slum ontology” to detect slums from remotely sensed data with a “very high spatial resolution (VHR)” (Shekhar 2020). In the present work, the “enterprise ontology” built by Uschold and King (1995), Uschold and Grüninger (1996), as stated in Fernández López (2002) and “specification, conceptualisation and implementation process of methodology” developed on IEEE standards methodologies were coupled with “generic slum ontology” from Kohli et al. (2011) and then further modified (Shekhar 2020) to suit the Indian scenario and suit the actual stakeholders involved to result in the “slum ontology” that was used to detect the slums.

4.3.1 Building Slum Ontology • “Specification”: states “why the ontology is being created and what its intended uses are and who are its end-users”. In the present study, the ontology has been developed to detect slums from VHR satellite data by the actual stakeholders. The local administrations involved, such

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as the Kalaburagi City Corporation, Slum Clearance Board of Karnataka, town planners, NGOs, ward corporators, slum dwellers, and the common people will benefit from this ontology. • “Identification of the key concepts and relationships in the domain of interest”: After having understood what our domain of interest is and for whom we are building the ontology, to begin with, we then need to collect some of the important terminologies about the domain. It includes definitions, attributes they might have, or conceptual relationships between them. The expertise can be shared by the professionals in that particular field, exploring the domain, metadata of knowledge-database, etc. The knowledge gained has to be organized properly. Basic concepts have to be refined by adding entities and the relationship between them, etc. (Shekhar 2020). To build slum ontology for various stakeholders, the expertise of 50 professionals was used, and also the concepts used in the recent literature were used to strengthen the ontology. This step in the process of building an ontology is what is called as “ontology capture” (Falquet et al. 2011). Sample areas of slums taken from GeoEye (VHR data) satellite data of Kalaburagi city were used for the primary survey. The professionals were requested to delineate slum areas from non-slums and the thought process involved in the identification of slums was captured by a validated questionnaire. The experience and the knowledge of domain experts were then converted into a broad knowledge base and helped in building our “slum ontology”. Figures 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 represent the organized knowledge base (Shekhar 2020). After creating a structured knowledge base using the experts’ inputs and from other relevant literature, the following procedures were adopted in the building of the ontology: Fig. 4.1 Regional differences in the name

4.3 Slum Ontology

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Fig. 4.2 First thoughts about a slum

Fig. 4.3 Location of slum

“Conceptualization” helps to organize the knowledge into different themes and subthemes that reflect the slum household characteristics and help to distinguish slum households from other households in the remote sensing data with high spatial resolution. The characteristics of slum households were validated using Google Earth Images representing various cities and towns. Slums of Kalaburagi and other Indian cities were visited and ground truth verification was done. The findings have been summed up in Table 4.1. “Formalization” means the transformation of a theoretical model into a more practical computational model. This process can also be called “coding” which converts the attained knowledge into a “formal language”. This in turn helps in the identification of slums from remotely sensed images. After gaining knowledge,

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Fig. 4.4 Type of land

Fig. 4.5 Facilities in slum

the next step is the listing of variables which can differentiate the characteristics of a slum from a non-slum in earth observation data (Shekhar 2020). “Implementation” changes these formal representations into field models by implementing the model at ground level. This process involves the amalgamation of different established ontologies. “Evaluation and documentation” is the final stage of ontology building. It evaluates the implemented field models for their applicability in general. It is similar

4.3 Slum Ontology

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Fig. 4.6 Socio-economic aspects

Fig. 4.7 Concepts from published sources

to “maintenance of methodology” (after Fernández López et al. 1999) by refining and apprising. After developing the slum ontology, it was implemented on the GeoEye image of Kalaburagi and also tested on other cities using Google Earth Images. The socio-economic characteristics which were not directly inferred from the remote sensing data were collected during field visits and documented through field photographs (Shekhar 2020) (Picture 4.1).

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Fig. 4.8 Image subsets used for survey Table 4.1 Slum characteristics-A Slum features Features

Based on the primary survey

Ground survey

Building characteristics

Variable size Often lacks high-rise structure Varies in brightness value in comparison to non-slums

Very small to moderate size Roofs: Slates/stones, concrete, tin sheets Single story structure Dilapidated condition Lack of sanitation and hygiene

Building density

High density Often lacks vegetation and shadows

Crowded together with very narrow lanes

Layout

Irregular, haphazard street pattern

No layout, Kachcha internal roads

Location information

Close to railway line, canals, Along the railway line, major river, nallah, major roads, roads and nallahs, outskirts of Airport boundary, coastal line airport area, coastal area

Neighborhood characteristics In between big apartments/high-rise structure Close to market area, bus stand Source Shekhar (2020)

Close to high- and middle-income class residential area, adjacent to informal employment opportunities

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Picture 4.1 Field photos—slum characteristics

4.4 Slum Identification Through Ontology Approach Efforts were made to identify slums from high-resolution data on the basis of slum ontology, which has been built by using inputs from domain experts (Table 4.2). When it was tested with real stakeholders, the quality of the results showed the effectiveness of the proposed method. As a first step, four subsets (Fig. 4.8) of geo-referenced GeoEye images of Kalaburagi city were shown to the stakeholders and they were asked to spot the familiar landmarks of Kalaburagi city such as major roads, railway station, bus stand, and university. While helping them to do so, we helped them get acquainted with the satellite image. Then, they were asked to identify the buildings, trees, roads, water bodies (built-up and non-built-up) and slowly recognize the pattern between planned and unplanned areas. The next step was making them understand the slum characteristics (based on slum ontology) and how they would appear in a satellite image (Fig. 4.9). Finally, they were requested to detect the slums, and they could detect slum areas from GeoEye satellite image of Gulbarga city with reasonable accuracy (75%). Out of twelve known sites of slums, they could identify nine slum pockets of Kalaburagi city.

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Fig. 4.9 Identifiable slum characteristics from VHR data

It has been proven that knowledge-based approach will be effective in the identification of slums, particularly by non-geospatial experts, i.e., the actual stakeholders who will be using this spatial information for slum improvement and renewal programs. The government of India’s efforts to create a slum free India needs community participation at all levels in the slum development programs. Does the slum dweller actually know, where he/she is living? And by identifying how far away they are from necessary infrastructure facilities will they now take a more informed decision to improve the current situation on their own. Hence, it is the base for spatial decision supporting and successful implementation of planning programs. It is crucial to uplift their standard of living, and for this, a legal dwelling unit is the first step in the right direction. This, in turn, will bring about a marked improvement in their health and hygiene (http://www.sra.gov.in/).

4.4.1 Slum Map Preparation Based on the slum ontology, slums were identified (both visual and object-based analysis) and a slum map was prepared (Fig. 4.10). The same was validated with the UFS (Urban Frame survey) map. The UFS maps were purchased from the National Sample Survey Organisation, Hubli. These maps were used by the enumerators at the time of collection of census data, and are thus maps of the actual enumeration blocks.

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Table 4.2 Slum characteristics-B Detecting slums from very high resolution data Indicators from conceptual model

Image interpretation elements For real stakeholders (visual interpretation)

For geospatial technology experts (image analysis software)

Building characteristics

Shape and size of the image objects

Shape and size of building footprints (after segmentation in OOA) Brightness value, spectral mean, standard deviation value of various bands Texture measures of various bands (GLCM values)

Building density

High density of image objects

High density—patch density, area Compact measurement, lacunarity (spatial metrics and spatial statistics values)

Layout

Irregular Less vegetation Small/Nil shadows

Geometry, shape index, symmetry Green index, NDVI, customized index Shadow measurement—in relation with the image objects

Location information

Association Close to railway lines, major roads, river, nallah, coast

Creating buffers Distance to other image objects, relation with image objects

Neighborhood characteristics

Close to bus stands, market areas, high residential areas, labor intensive Industrial areas

Distance to other image objects, relation with image objects

4.5 Validation using Enumeration Blocks 14 UFS maps were purchased from NSSO, and they were compared with Google Earth maps. Slum enumeration block maps are prepared based on the field observation. This will include not only notified and non-notified slums but also identified slums based on the census definition. Hence, validating with the enumeration block map is very important to assess the reliability of the map prepared by this method. The following snapshots show the matching of UFS maps with Google Earth display for initial understanding and later on the same was compared with the slum map (Figs. 4.11 and 4.12).

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Fig. 4.10 Slum map

4.6 Applicability in the Indian Context Based on personal experiences and the ground reality, this is an earnest attempt to solve the major problems of the stakeholders involved in slum development. The stakeholders are those who are involved in planning, policymaking, and implementing the projects at the ground level. GIS professionals, administrators at local bodies, corporators, and the slum residents have also been included in the list of stakeholders. The cognitive model in the form of a “slum ontology” helps facilitate better understanding and knowledge transfer between various stakeholders. The major obstacle in slum development is the lack of spatial data and a good map of slum areas will help not only the planners, but also the administrators to plan, monitor, and successfully manage the slum developmental programs.

4.6 Applicability in the Indian Context

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a

b

Fig. 4.11 a Matching UFS map with Google Earth image. Source Urban Frame Survey maps. b Google Earth image matching with UFS map. Source Google Earth

For example, the location of slums in major Indian cities was identified on images downloaded from Google Earth and other image sources have proven that the slum ontology built upon slum characteristics (Figs. 4.13, 4.14, 4.15, 4.16, 4.17, 4.18, 4.19, 4.20, 4.21, 4.22, 4.23, 4.24, 4.25, and 4.26) will come in handy to identify the slums spatially. The spatial information on slums, their location, and extent will

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Fig. 4.12 Validation with slum map

Fig. 4.13 Bengaluru: slum area with a high density of households

form the base for slum upgradation/slum development and further help in building sustainable cities. Figures 4.13, 4.14, 4.15, 4.16, 4.17, 4.18, 4.19, 4.20, 4.21, 4.22, 4.23, 4.24, 4.25, and 4.26 Location of slums in Million-plus cities. From the above, it is obvious that VHR data is essential for the identification of slums since it brings out the slum characteristics very clearly such as dense, compact settlements with very narrow roads (or no roads), tiny households made up of mixed materials, located in vulnerable sites, etc. Technically the tone and texture of images also help us in the detection of slums. It has been proven that “slum ontology” based identification of slums can give a better result and by improving it further, the map accuracy will be enhanced. The bureaucrats, the officers of urban local bodies, slum development board, urban planners, and last but not least the slum dwellers are the major stakeholders and

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Fig. 4.14 Kolkatta: slum area with a high density of households around a pond

Fig. 4.15 Hyderabad: slums found near the airport

they should know this methodology and be able to identify the slums based on slum ontology. Once the slums are mapped, they will certainly be “in” the planning and true inclusive planning will happen for a sustainable future.

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Fig. 4.16 Mumbai: slums found near the airport

Fig. 4.17 Delhi: slums found near the Rly station

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4.6 Applicability in the Indian Context

Fig. 4.18 Kolkata: slums found near the Rly station

Fig. 4.19 Pune: slums found on the hill slope and canal

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86 Fig. 4.20 Jaipur: slums found on the hill slope

Fig. 4.21 Surat: slums found near ‘Nallas’

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4.6 Applicability in the Indian Context Fig. 4.22 Pune: slums found near ‘Nallas’

Fig. 4.23 Ahmedabad: slums found near the river

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88 Fig. 4.24 Chennai: slums found near the river

Fig. 4.25 Chennai: slums located close to the sea

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Fig. 4.26 Mumbai: Slum located close to the sea

References Agarwal P (2005) Ontological considerations in GIScience. Int J Geogr Inf Sci 19(5):501–536 Arara A, Benslimane D (2004) Towards formal ontologies requirements with multiple perspectives. In: Christiansen H, Hacid MS, Andreasen T, Larsen HL (eds) Flexible query answering systems. FQAS 2004. Lecture notes in computer science, vol 3055. Springer, Berlin, Heidelberg. https:// doi.org/10.1007/978-3-540-25957-2_13 Aussenac-Gilles N, Soergel D (2005) Text analysis for ontology and terminology engineering. Appl Ontol 1(1):35–46 Cowie LW (1996) The Wordsworth dictionary of British Social History: an illustrated guide to the Social Tapestry of Britain. Wordsworth Editions, Hertfordshire Falquet G, Métral C, Teller J, Tweed C (2011) Ontologies in urban development projects, 2011/7/29. Springer Science & Business Media Fernández López M (2002) Overview of methodologies for building ontologies. J Knowl Eng Rev 17(2):129–156 Fernández López M, Gómez-Pérez A, Sierra JP, Sierra AP (1999) Building a chemical ontology using methontology and the ontology design environment. IEEE Intell Syst 14(1):37–46. https:// doi.org/10.1109/5254.747904 Fonseca F (2007) The double role of ontologies in information science research. J Am Soc Inf Sci Technol 58(6). First published: 26 February 2007. https://doi.org/10.1002/asi.20565 Fonseca F, Egenhofer M (1999) Ontology-driven geographic information systems. In: Medeiros CB (ed) Proceedings, 7th ACM symposium on advances in geographic information systems, held in Kansas City, Mo., November 1999, pp 14–19 Fonseca FT, Egenhofer MJ, Davis CA, Borges KAV (2000) Ontologies and knowledge sharing in urban GIS. Comput Environ Urban Syst 24(3):251–272 Gandon (2002) as stated in Teller J et al (2005). Gandon F (2002) Distributed artificial intelligence and knowledge management: ontologies and multi-agent systems for a corporate semantic web. Doctoral school of sciences and technologies of information and communication, University of Nice Sophia Antipolis

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Garside PL (1988) ‘Unhealthy areas’: town planning, eugenics and the slums, 1890–1945. Plan Perspect 3(1):24–46. https://doi.org/10.1080/02665438808725650 Genesereth MR, Nilsson NJ (1987) Logical foundations of artificial intelligence, vol xviii. Morgan Kaufmann Publishers, Los Altos, Calif., 405 pp. J Symb Logic 55(3):1304–1307. https://doi.org/ 10.2307/2274491 Gruber (1992) Ontolingua: a mechanism to support portable ontologies. Stanford University, Knowledge Systems Laboratory Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220 (revised) Gruber (2005) Folksonomy of ontology: a mash-up of apples and oranges. In: First on-line conference on metadata and semantics research MTSR, vol 6, 2005/11 Gruber TR et al (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Human Comput Stud 43(5):907–928 Guarino (1995) Formal ontology, conceptual analysis and knowledge representation. Int J Hum Comput Stud 625–640 Huchzermeyer M (2008) Slum upgrading in Nairobi within the housing and basic services market: a housing rights concern. J Asian Afr Stud 43(1):19–39. https://doi.org/10.1177/002190960708 5586 Kohli D et al (2011) An ontology of slums for image-based classification. Comput Environ Urban Syst Montenegro N (2010) Building a pre-design ontology: towards a model for urban programs. Novello TC et al. An approach for systematic interface design from knowledge models Noy N, Mcguinness D (2001) Ontology development 101: a guide to creating your first ontology. Knowledge Systems Laboratory, p 32 Noy NF, Fergerson RW, Musen MA (2000) The knowledge model of Protege-2000: combining interoperability and flexibility. Lect Notes Comput Sci 17–32 Roussey C, Pinet F, Kang M-A, Corcho O (2011) An introduction to ontologies and ontology engineering. https://doi.org/10.1007/978-0-85729-724-2_2 Sachs E, Contact C, Modified L (2006) Getting started with Protégé-frames. Standford University Shekhar S (2020) Effective management of slums—case study of Kalaburagi city, Karnataka, India. J Urban Manag 9(1):35–53. https://doi.org/10.1016/j.jum.2019.09.001. ISSN 2226-5856 Smith B, Mark D (1998) Ontology and geographic kinds. In: Poiker TK, Chrisman N (eds) Proceedings of the eighth international symposium on spatial data handling, held in Vancouver, British Columbia, Canada. International Geographical Union, Burnaby, British Columbia, pp 308–320 Sowa J (1995) Top-level ontological categories. Int J Human-Comput Stud 43(5/6):669–685 UN-HABITAT (2006) State of the world cities, 2006/7. https://mirror.unhabitat.org/documents/ media_centre/sowcr2006/SOWCR%205.pdf Uschold M, Grüninger M (1996) Ontologies: principles methods and applications. Knowl Shar Rev 2 Uschold M, King M (1995) Towards a methodology for building ontologies. In: Workshop on Basic ontological issues in knowledge sharing Van Der Vet PE, Mars NJI (1998) Bottom-up construction of ontologies. IEEE Trans Knowl Data Eng 10(4):513–526. https://doi.org/10.1109/69.706054 Winter S (2001) Ontology: buzzword or paradigm shift in GI science? Int J Geogr Inf Sci 15(7):587– 590. https://doi.org/10.1080/13658810110061207

Chapter 5

Slum Modeling for Growth Prediction

Making cities safe and sustainable means ensuring access to safe and affordable housing, and upgrading slum settlements. SDG Goal 11: Sustainable cities and communities-UNDP

Abstract Modeling helps in the understanding of complex spatial phenomenon in a simple manner. All spatial problems are complicated and need a thorough understanding of their causes and consequences. Slum formation is one such problem that includes not only economic aspects but also social, cultural, and most importantly, behavioral aspects. Modeling all such factors requires the use of a sophisticated model which can imitate human thinking and accurately portray the ground reality. A high-end model demands valid input data for the expected output to have reasonably high accuracy. In the present chapter, the Cellular Automata (CA) model has been used to model the conditions that favor slum formation in the study area, Kalaburagi city. The model was built in an ArcGIS environment with various thematic layers generated using satellite data and spatial analysis. The core part of the model is transitional rules that reflect the actual situations that lead to slum formation. These generated results were close to the ground situation. Accordingly, the most favorable sites for slum formation as identified by the model accounted for 97% of the actual slums and moderate areas were identified as probable sites for future slum formation. Since prevention is better than rehabilitation, the open land available in the moderate zone was suggested for affordable housing. Meanwhile, the Master Plan Map of Kalaburagi city prepared by the Kalaburagi Urban Development Authority for 2021 was used to identify the areas planned for future residential expansion. The identified and economically viable sites were compared with the sites favoring slum formation as per the CA model and recommended for affordable housing. Keywords Urban · Slum · Cellular automata model · Ontology · Kalaburagi · MCE

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_5

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5.1 Introduction As natural increase and rural to urban migration are augmenting the size of the slum population in urban areas, it is time to think about amicable solutions to address this issue. To enjoy the benefits of urbanization and its positive impacts, integrated planning is the need of the hour. Only inclusive planning can help achieve sustainable cities (Report of Slum Committee 2010; www.mhupa.gov.in). Change is the only permanent thing in the world. When cities grow, they undergo a lot of changes. Some are positive and some are negative. There is a positive growth in population size and economic activities and an increase in GDP, per capita income, and infrastructure. Unfortunately, there is also an increase in the slum population. It is because cities are unable to provide affordable housing for the poor migrants and fail to extend their basic infrastructure to them. It creates a divide in society and divides the city physically into slum areas and non-slum areas. Initially, it is just a fault line but later this adds social tension and finally, it might even shake the governance of the city. To avoid such an unwanted situation, we have to bridge the gap through proper planning. Good planning needs reliable data. To uplift the existing poor conditions of slums, reliable and up-to-date spatial information on slums is essential. We need to build models to explore the spatial data thoroughly and also for visualizing the results. These models help us to understand the hidden issues and view the system from a new perspective. Hence, models play an imperative role in urban planning, spatial decision-making, and policy implementation. The cellular automata model is a renowned one in the urban field especially with regards to integrating inputs from various sources and performing multiple criteria evaluations to bring out a reliable model.

5.2 Slum Models: A Review Over the last few decades, Cellular Automata (CA) models became quite popular and are presently the preferred modeling technique for modeling urban growth by many urban researchers. Its popularity is due to its capability “to model and visualize complex spatially distributed processes” (Takeyama and Couclelis 1997). The transition rules that apply to a predefined area in a cellular automata model may be quite simple but these can still result in a system of high complexity (Firebaugh 1998). Herold et al. (2002) established the advantages of combining earth observation technology and spatial metrics, in their study on “The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses”. In the past few years, the blending together of multi-criteria evaluation with GIS has been gaining in popularity in the fields of natural as well as social sciences research (Makropoulos and Butler 2005; Malczewski and Rinner 2005; Boroushaki and Malczewski 2008). CA has been applied in the current project, along with MCE in the ArcGIS environment in order to model the probable growth of slums.

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The availability of earth observation data with very high spatial and spectral resolution provides new opportunities to perform detailed land-use/land cover mapping and offers an increasingly luxuriant area for research with studies focusing on slum mapping (Mason and Fraser 1998; Sliuzas et al. 2008; Hofmann 2001; Sur et al. 2004; Shekhar 2012). The geospatial technique also significantly contributes to this process and has brought a surge of changes in urban research. (Batty and Xie 1994a, b; Clarke et al. 2002; Batty 2003; Shekhar 2004, 2006). For modeling the growth of urban areas and stimulating the growth for predicting future scenarios, the amalgamation of CA and GIS has proven very useful (Ward et al. 2000; Singh 2003; Batty and Longley 1994; Wegener 1994; Yeh and Xie 2001). The visual interpretation approach is extremely useful when the interpreter has the local knowledge of the slum area and knows the existing slum conditions (Sliuzas 2004; Baud et al. 2010; Jain et al. 2007) though it has deficiencies and loses control on quality over time and between image interpreters (Sliuzas 2008). Recently, ontological frameworks are being used to define slums and also being used as the basis for image-based classification and modeling (Hofmann 2008, Hofmann et al. 2008; Kohli et al. 2011; Khelifa and Mimoun 2012; Sietchiping 2005).

5.3 Model Building Cellular Automata (CA) comprises five elements, namely, “cell space, cell state, time step, transition rule, and neighborhood”. In CA, the condition (existing land use) of the cell and the conditions of its surrounding cells at the initial period (say T1) regulate the condition of a cell in the next period (say T2). At the initial stage (T1), if the cell has an inclination for development and it is encouraged by its surrounding cells, then transformation will happen in T2. If we visualize an urban area as a cell matrix, divided into cells arranged in rows and columns, one will notice that no cell will undergo uniform development. The transformation from one type of land use to another type depends on various parameters. These parameters can be either geographical, social, economic, or organizational (government policy) or combinations of any or all of these (Liu and Phinn 2001; Shekhar 2020). This data can be modeled to understand the fittingness of any land use say, “slum” for each cell, using Multi-Criteria Evaluation (MCE). Modeling a dynamic growth process is a basic requirement to understand the complexity involved in the process. It is an “abstraction of reality” that brings out the multifaceted associations in a lucid manner. The domains of geography and planning are known for their research in building urban models to understand its dynamic and complex nature (Batty and Xie 1994a, b). More recently, with the development of Earth observation techniques, many geospatial scientists have examined applications of satellite data for modeling urban development.

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5.3.1 Conceptualizing the Model “Cellular Automata” (CA) are popular urban models that have close associations with complexity theory and have been applied in the exploration of a varied range of urban activities. In general, CA models tend to examine how urban systems operate, but using a controlled environment within computer software (Lecture 7, www.casa. ucl.ac.uk). The five elements of CA are described below: “Cell Space”: The cell space represents the geographical area of an individual cell. Hypothetically, the cells can be in any regular shape. Since CA models use raster data (one of the two data formats in GIS, the other being vector) format as an input for modeling, we tend to use a “regular grid” format which mimics the raster data model of GIS. “Cell States”: This refers to the existing conditions of the cell. For example, the land use of a cell. “Timesteps”: Cellular automata model evolves through an order of events that happens in the given time. At every step, the cells will undergo changes depending on the transition rules. “Transition Rules”: Transition rules play a pivotal role and all the changes that take place in a cell depend on these rules. This rule determines the relationship between the cell and its surrounding cells (Singh 2003; Shekhar 2006). “Neighborhood”: It means the area (cells) surrounding a particular cell space. It can be represented in two ways. If the cell has only two surrounding cells (neighbors), then it is a “one-dimensional cellular automata”. If it has more than two neighbors, then it is a “two-dimensional cellular automata model” and they are related in two different forms. The first of these is the Von Neumann model where a cell is surrounded by four cells as neighbors while the second is the Moore model where each cell is surrounded by eight neighbors. Figure 5.1 represents the neighborhood types in a two-dimensional CA model (Singh 2003).

Fig. 5.1 Neighbors type

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According to the concept of Cellular Automata, geographical space is an amalgamation of small spaces, which have a particular geometry and are known as “cells”. As a geographical unit, each cell space has many attributes that include land-use, social, and economic characteristics. As they represent the urban system, these cells are dynamic and change due to the impact of surrounding cells over the course of time. Thus, it is understood that urban area is represented as the integration of many sub-units and undergoes changes due to some impacts eventually. CA model permits the conversion of the status of the cell into any state as per the transition rules. It can be non-probabilistic as well as non-deterministic. However, the non-deterministic rules in a CA model will be appropriate when it mimics systems that are related to human activities (Liu and Phinn 2001, 2003). The situations that lead to the transformation may vary from cell to cell and depend on various conditions. For example, if development has to happen to a cell and it is inclined to the same, change might still not happen because it did not receive needed support from its surrounding cells (say neighborhood). The process of change can also be slowed down if the cell is in an unfavorable location (steep side of the valley). At the same time, if the cell is located in a favorable location (near to the junction of transportation networks), that will expedite the development. These favorable situations can be either geographical, economic, or organizational or a combination of any or all of these (Liu and Phinn 2001; Singh 2003; Shekhar 2006). In applying cellular models to slum development, transition rules must imitate important factors that impact slum growth, and these can vary from geographical to economic factors. In the present case, factors were decided through an understanding of relevant literature, as well as consultation with the domain experts. The factors that were taken for this model were classified based on their relative suitability as “most, moderately, or less suitable” instead of the crisp division of “suitable and not suitable” (Shekhar 2012, 2013). In the current model, cell space represents a subset of the geographical area, and in our case, it stands for the city of Kalaburagi. The land use represents the status of a cell. The transition rules are built to allow the factors to respond to the various growth scenarios of a cell. The land use of a cell (Lu) at x, y (position of the cell) in period “T2” is the function of the land use of the cell at x, y in period ‘T1’ (Shekhar 2020) LuT2xy = f (Lu T1xy ) In this study, the base map for neighborhood analysis was prepared with two landuse types—open space and slums. The transformation of one state to another state will take place only among these two in a linear direction, and the conversion of any cell is possible only to slum. With the help of MCE, all those available cells for slum development will be given a score (Table 5.2) as per the weightage assigned to the variables. Finally, MCE scores (Sc) will be combined with the neighborhood influence (N) and thus, the final score (final score = Sc + N) determines the probability of the cell for slum development.

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When building the transition rules, the land use of the cell is considered as “Z”, with Zs for “slum” and Zo for “open space”. (i) (ii)

In time T1, if the land use is Zs then in time T2, it will remain as “Zs “only if the cell is surrounded by all “Zs” cells in its neighborhood. In time T1, if the land use of the cell is Zo, then in time T2, it will change into Zs if the cell is surrounded (a majority) by Zs cells in its neighborhood.

These rules were applied in a 3 × 3 matrix in the Moore model with the “majority” option, and this process is what generated the neighborhood map (N) (Shekhar 2020).

5.3.2 Building the Model After the development of ontology as described in the previous section for slum identification, GeoEye data was classified into required classes for modeling such as open area (excludes agricultural land with crops and hill slopes) and slums. As per the slum ontology, basic inputs for the CA model (Table 5.1) were identified such as being close to a road, railway line, lake, informality (Figs. 5.2, 5.3 and 5.4) and criteria maps with scores were generated using ArcGIS model builder (Shekhar 2020). To understand the location, density, and nature of buildings at the environment, settlement, and object level, extensive field visits were made and ground conditions were documented through field photographs (Pictures 5.1, 5.2, 5.3, 5.4, 5.5 and 5.6). Pictures 5.1, 5.2, 5.3, 5.4, 5.5 and 5.6 shows the slum conditions at Kalaburagi. The pictures taken during the field visits show that the roof and wall materials vary from stones, tin sheets to polythene sheets and concrete roofs. The connecting roads also vary from tiled to paved and unpaved roads. This model structure built in the ArcGIS environment using a model builder (Fig. 5.4) is shown. The layers created are shown in Figs. 5.5, 5.6, 5.7, 5.8, 5.9, 5.10, 5.11 and 5.12 with their score ranging from 1 to 4 (Table 5.2) based on their influence to attract slum settlement and finally, the weighted overlay operation gives us the final score of their attraction. The final scores (Fig. 5.13) of weighted overlay resulted in the labeling of zones as “most attractive”, “moderately attractive”, and “least attractive” zones for slum development. Figure 5.14 represents the result of neighborhood analysis map with two major land-use classes, namely open/green space and slum area. The open green space includes all types of green cover (grass to tree cover) and the open space includes barren land as well as agricultural land without crops.

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Table 5.1 Formalization of concepts (after Kohli et al. 2011 with local adaptation) Level

Indicators

Observation

Image interpretation elements

Environs

Location

Adjacent to main roads, railway lines; open land

Pattern, association

Neighborhood characteristics

Close to employment opportunities such as Market area, industrial areas, middle/high socio-economic status neighborhoods

Pattern

Shape

Irregular, linear

Pattern

Density

Highly dense compared to Texture planned Association Presence/absence of vegetation

Building

Variable Range of values—10–40 m2 Roofs-slates/stones, concrete, tin sheets Range-variable Regular but clumped together

Shape (geometry) Size Texture Color (in case of MSS data) Orientation Shadow—association Height

Access network

Irregular, haphazard Paved/unpaved access streets Range—variable

Shape Type Width

Settlement level

Object level

Fig. 5.2 False color composite GeoEye image shows slum distribution close to highways and the railway station

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Fig. 5.3 False color composite GeoEye and True color image clip taken from Google Earth shows highly dense and comparatively less vegetation in the slum area

Pictures 5.1 and 5.2 Wall and roof materials of slums of Kalaburagi

Pictures 5.3 and 5.4 Lack of approach road and proper drainage

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Pictures 5.5 and 5.6 Wall and roof materials of slums of Kalaburagi

Fig. 5.4 CA model

5.3.3 Model Output The final score derived from the weighted overlay was then added to the neighborhood analysis map (Fig. 5.14) to get the model output map (Fig. 5.15). To verify the model output and identify possible areas of future slum development, the existing slum map was overlaid on the model output map (Fig. 5.16). It revealed that in Kalaburagi city, 97% (1.5 km2 ) of slum areas are present in the most attractive zone for slum

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Fig. 5.5 Distance from road

development, and remaining 3% of slum areas are present in the moderately attractive zone for slum development (Shekhar 2020). In Kalaburagi, slum expansion is due to a lack of affordability and poverty of rural migrants. This expansion happens on the vacant land, unsecured, and unsuitable government land, along the roads, railway lines, and near the lake. It has been observed that a large number of slums exist in the central part of the city (under the most attractive zone category), and there are chances for further slum expansion along the linear features such as roads and railway lines in peripheries. This is because more than 8.7 km2 of open land in the form of agricultural land without crops and barren land is present in the moderately attractive zone, and only 1.5 km2 of open land is present in most suitable areas for slum growth. These areas are however under

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Fig. 5.6 Distance from railway

planned green cover. Hence, open moderate areas will attract more slum growth than those open areas present in the most attractive category (Shekhar 2020).

5.4 Future Growth of Slums Dislocation of a large workforce from the agricultural areas to urban areas and their engagement in informal sectors have led to serious problems. The urban areas are not fully capable of offering job opportunities, necessary infrastructure, and secured tenure to the poor migrants. The issues escalated when the migrant laborers selected sites for their settlement based on the job opportunities it offered and that lead to the growth of slum areas. The quality of life thus suffers due to the continuous influx

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Fig. 5.7 Distance from water bodies

of migrants and consequently widens the gap between the demand and supply of essential services and other infrastructure in urban areas (Shekhar 2020). The CA model was based on the physical location of slums. That has taken into consideration the spatial expansion of slums in certain conditions into modeling. The model does not include the population size or the number of households that would have to be included in the future growth of slum. The model assumes that there are certain areas generally attractive to poor migrants for settlement. Those areas if identified will aid in planning while also preventing the formation of slums in the future. The model mainly identifies areas that are likely to attract migrant settlements and thus have a high chance of becoming a slum in the future. These areas are linked with the availability of employment opportunities and availability of vacant land.

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Fig. 5.8 Distance from nodal pints

The output of the model has taken into consideration an open area (green area under proposed land use) which remains open even after the implementation of the proposed land-use plan and does not just look at the current land-use scenario. The current vacant land (residential use in proposed land use) depending on its closeness to employment opportunities has been considered for affordable housing. The affordable housing should be within at least 1,500 m from the existing ring road. This will ensure connectivity and affordability to travel to work and other movements. The houses should be distributed in such a way that it is accessible in all directions from the city center. The housing layout plan should have adequate space for further expansion and green areas (park). Schools and hospitals should be in approachable distance from the new stock of houses (Shekhar 2020).

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Fig. 5.9 Reclassification of distance from road

The land values (both residential and commercial) should be taken into consideration before deciding the location of affordable housing. The following Fig. 5.17 shows the possible sites for affordable housing to prevent future slum formation. The details of possible sites for affordable housing are given below. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

The site between Pallapur and Agricultural Research Center. The area surrounding the Azmir colony, Heeranagar. The area next to the central warehousing corporation. The site between Mahanteswara layout and Udnoor road (toward) Minajagi. The left side of Laxminarayan colony, BL Nagar, Satyam layout. The left side of the Police Training Center and the road to Khanadal. The right side of the Police Training Center. The right side of the University toward Sedam roadside. The area close to Dhariyapur and Krishna Nagar. Near Polytechnic college.

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Fig. 5.10 Reclassification of distance from the railway line

11. 12. 13. 14. 15.

Close to Rajapur, Pavana Ganaga Colony, GDA layout. Close to Azadpur. Close to the Arfadh colony, Aman Nagar and Sonia Gandhi colony toward Kotnoor. The area between Vidya Nagar and Malagatti. Toward the right side of Karnataka Reserve Police battalion.

The above sites are earmarked for residential purpose in the proposed land-use plan. There is no separate allocation of residential use for EWS (Economically Weaker Section). Hence, the proposed sites for affordable housing may be used for other residential activities. It ultimately depends on the decision of the urban local bodies (Shekhar 2020).

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Fig. 5.11 Reclassification of distance from water bodies

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Fig. 5.12 Reclassification of distance from nodal points Table 5.2 Ontology-based factors with their scores (After Shekhar 2020)

Selected factors Suitability (to attract slum) scores Most—3 (m)

Moderate—2 (m)

Least—1 (m)

Distance from lake

Less than 1000

1000–3000

Above 3000

Distance from road

Less than 1000

1000–3000

Above 3000

Distance from rly line Distance from nodal points

Less than 1000 Less than 1000

1000–3000 1000–3000

Above 3000 Above 3000

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Fig. 5.13 Weighted overlay result

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5.4 Future Growth of Slums

Fig. 5.14 Neighborhood analysis map

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Fig. 5.15 CA model output

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5.4 Future Growth of Slums

Fig. 5.16 Model result verification

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Fig. 5.17 Affordable housing sites

References Batty M (2003) Spatial and locational modelling in human geography. In: Rogers A, Viles HA (eds) The students’ companion to geography, 2nd edn. Oxford: Blackwell Publishing, pp 157–160 Batty M, Longley P (1994) Fractal cities: a geometry of form and function. Academic Press, London Batty M, Xie Y (1994a) Modelling inside GIS. Part 1: model structures, exploratory spatial data analysis and aggregation. Int J Geogr Inf Syst 8(3):291–307 Batty M, Xie Y (1994b) Modelling inside GIS. Part 2: selecting and calibrating urban models using ARC/INFO. Int J Geogr Inf Syst 8(1):451–470 Baud I, Kuffer M, Pfeffer K, Sliuzas R, Karuppannan S (2010) Understanding heterogeneity in metropolitan India: the added value of remote sensing data for analyzing sub- standard residential areas. Int J Appl Earth Obs Geoinf 12(5):359–374

References

113

Boroushaki Soheil, Malczewski Jacek (2008) Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Comput Geosci 34:399–410. https://doi.org/10.1016/j.cageo.2007.04.003 Clarke KC, Parks BE, Crane MP (eds) (2002) Geographic information systems and environmental modeling. Prentice Hall, Upper Saddle River Firebaugh MW (1998) Artificial Intelligence—a knowledge based approach, Boyd Frazer Publishing Company, Bosten Herold M, Scepan J, Clarke KC (2002) The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ Plan A Econ Space 34(8):1443–1458. https:// doi.org/10.1068/a3496 Hofmann P (2001) Detecting informal settlements from IKONOS image data using methods of object-oriented image analysis—an example from Cape Town (South Africa). Paper presented at the remote sensing of urban areas Hofmann P (2008) Detecting informal settlements using methods of object-based image analysis. In: Expert meeting on slum mapping ITC, Netherlands, 21–23 May 2008 Hofmann P, Strobl J, Blaschke T, Kux, H (2008) Detecting informal settlements from quick bird data in Rio De Janeiro using an object based approach. Object Based Image Anal 531–553 Jain S et al (2007) Use of IKONOS satellite data to identify informal settlements in Dehradun, India. Int J Remote Sens 28(15):3227–3233 Khelifa D, Mimoun M (2012) Object-based image analysis and data mining for building ontology of informal urban settlements. In: Proceedings of SPIE 8537, image and signal processing for remote sensing XVIII, p 85371I Kohli D et al (2011) An ontology of slums for image-based classification. Comput Environ Urban Syst 36(2):154–163. https://doi.org/10.1016/j.compenvurbsys.2011.11.001 Liu Y, Phinn SR (2001) Developing a cellular automaton model of urban growth incorporating fuzzy set approaches. Paper available on line Liu Yan, Phinn Stuart (2003) Modeling urban development with cellular automata incorporating fuzzy-set approaches. Comput Environ Urban Syst 27:637–658. https://doi.org/10.1016/S01989715(02)00069-8 Makropoulos CK, Butler D (2005) A multi-objective evolutionary programming approach to the ‘object location’ spatial analysis and optimisation problem within the urban water management domain. Civ Eng Environ Syst 22(2):85–108 Malczewski Jacek, Rinner Claus (2005) Exploring multicriteria decision strategies in GIS with linguistic quantifiers: a case study of residential quality evaluation. J Geogr Syst 7:249–268. https://doi.org/10.1007/s10109-005-0159-2 Mason SO, Fraser CS (1998) Image sources for informal settlement management. Photogram Rec 16(92):313–330 Report of Slum Committee (2010) Report of the committee on slum statistics/census, Ministry of Housing & Urban Poverty Alleviation, National Buildings Organisation. http://nbo.gov.in/pdf/ REPORT_OF_SLUM_COMMITTEE.pdf Shekhar (2004) Urban sprawl assessment—entropy approach. GIS Dev 8(5):43–48 Shekhar S (2006) Modelling urban development with fuzzy logic and cellular automata. Asian J Geoinform 6(1):3–10 Shekhar S (2012) Detecting slums from quick bird data in pune using an object oriented approach. Int Arch Photogram Remote Sens Spatial Inf Sci XXXIX-B8 Shekhar S (2013) Slum modelling by using ontology and geoinformatics: case study of Gulbarga. Int J Geoinf 2013(9):53–60 Shekhar S (2020) Effective management of slums—case study of Kalaburagi city, Karnataka, India. J Urban Manag 9(1):35–53. ISSN 2226-5856. https://doi.org/10.1016/j.jum.2019.09.001 Sietchiping R (2005) Third urban research symposium on land development, urban policy and poverty reduction, 4–6 Apr 2005, Brasilia, Df, Brazil Singh AK (2003) Modeling land use land cover changes using cellular automata in a geo-spatial environment. MSc thesis, ITC, Netherlands

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Sliuzas RV (2004) Managing informal settlements: a study using geo-information in Dar es Salaam, Tanzania. Utrecht University, ITC Publication Series No. 112 Sliuzas RV (2008) Improving the performance of urban planning and management with remote sensing systems. In: Jurgens C (ed) Remote Sensing: new challenges of high resolution—EARSeL workshop. Bochum, Germany Sliuzas R, Mboup G, Sherbinin A (2008) Expert group meeting on slum identification and mapping. ITC, Enschede, The Netherlands Sur U, Jain S, Sokhi BS (2004) Identification/mapping of slum environment using IKONOS satellite data: a case study of Dehradun, India. http://www.gisdevelopment.net/application/environment/ pp/mi04011.htm Takayama M, Couclelis H (1997) Map dynamics: integrating cellular automata and gis through geo-algebra. Int J Geogr Inf Sci 11. As cited in Liu Y, Phinn SR (2001) Ward DP, Murray AT, Phinn SR (2000) An optimized cellular automata approach for sustainable urban development in rapidly urbanizing regions. Comput Environ Urban Syst 24:539–558 Wegener M (1994) Operational urban models. State of the art. J Am Plan Assoc 60(1):17–29 www.casa.ucl.ac.uk. Cellular automata modelling: principles of cell space simulation. http://www. casa.ucl.ac.uk/rits/rits-lecture-7.pdf www.mhupa.gov.in. Report of the committee on slum statistics/census Yeh AG, Xie L (2001) A constrained CA model for simulation and planning of sustainable urban forms by using GIS. Environ Plan B 28:733–753

Chapter 6

Slum Housing Demand Assessment and Analysis

Action is needed now to provide low-income families and vulnerable populations with affordable housing with security of tenure and easy access to water, sanitation, transport, and other basic services. António Guterres, UN Secretary General, 05 October 2020

Abstract Offering affordable housing is the best possible way of solving the problem of future slum formation or slum expansion/extension. So, how many houses need to be constructed to stop the formation of new slums? This is the first question that would arise in the minds of urban planners and no one can accurately answer this question. Whatever the answer, the most important thing is that we do not give up on searching for an answer. We may not be able to estimate the actual inflow of migrants but we can project the present slum population and assess the requirements of existing slums and their housing demands. The present chapter tries to project the slum population of Kalaburagi city but due to the dearth of slum population data, it did not yield good results. Despite the anomalies, we could arrive at a projection with an expected growth rate of 1.29%. Along with this, estimation of housing demand was carried out using the principle, “Willingness to stay instead of a willingness to pay”. Since the demand for housing depends on many factors; for this present study, only six basic parameters were selected for estimating the housing demand. One slum area, Borabai Nagar, was taken as a sample to demonstrate the methodology and the result is encouraging. This chapter also brought out another issue in developing affordable housing since the houses constructed under various housing schemes for slum dwellers in Kalaburagi city were evaluated for their success. It was observed that the houses meant for improving the slum situation were constructed in the city’s peripheries, far away from their employable opportunities, and thus failed to achieve their purpose. Keywords Affordable housing · Population projection · Housing demand · Kalaburagi city · Slums

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_6

115

116

6 Slum Housing Demand Assessment and Analysis

6.1 Introduction Population projection gives an idea about what the future size of the population might be. Such projection helps planners, policymakers, and administrators and nudges them to adopt scientific planning that will guide them to make arrangements to meet the requirements of the projected population. It can warn them that it’s time for economic planning to sustain growth and development based on different projection scenarios. The projection of the current population is done based on the knowledge of past trends. To project the future population, we need reliable data with authenticated methodology as the accuracy of the projected population has long-term effects. Social challenges such as an aging population, sex ratio, health issues, etc. need to be managed based on the projected scenario. The World Health Organization (WHO), the World Bank, and the United Nations regularly do population projections at the global level, and many countries use this population projection for their strategic planning. The ever-increasing slum population demands systematic planning to meet their basic requirements. If we learn how many houses more need to be built to accommodate the additional slum population in the next ten years, calculating how much more money has to be invested in bringing up the drinking water supply to meet the demand, electricity, and other basic infrastructure to slum areas in advance will help in managing urban areas successfully. The population (slum) projection will enable us to prepare ourselves to face the expected in a planned manner. But these projections should be as reliable as those made by the United Nations, the World Bank, and other international organizations who regularly announce and revise the projections at a global level as well as at the county level. The net increase or decrease (birth rate − death rate + immigration − emigration) in the population over a period is added to the base population to project the future population. The projected population for a small region like a city needs a good database to understand past trends and to project to the future. For a city like Kalaburagi, the available data for projection is census data alone. But still, there are differences of opinions in the reliability of census data as it underestimated the housing problems of the country by reducing it to counting only those slum households located in big slums (Amit et al. 2020).

6.2 Estimating Kalaburagi City Population Having problems with the reliability of data from other unauthorized sources, it was decided to use census data (available at ward level) for population projection of Kalaburagi city. The census data for the years 1991, 2001, and 2011 were used for projecting the city population for the year 2021. There were some issues in this data because of changes in the ward boundaries.

6.2 Estimating Kalaburagi City Population

117

Population in numbers

Figures 6.1 and 6.2 show the population projection at the ward levels. From the figures, one can understand that there are anomalies in the population data. The anomalies can be grouped under two categories. There is a nominal decrease (less than 1,000 population) of the population from 2001 to 2011 census in ten wards— numbers 3, 7, 16, 26, 28, 32, 34, 35, 40, and 44 and a remarkable decrease (more than 1,000 population) in ward numbers 1, 6, 9, 10, 12, 19, 27, 31, 43, 46, 47, 48, and 51. Particularly ward numbers 16 and 48 show a continuous decline in their population from 1991 to 2011 and negative growth in 2021 (Fig. 6.2). The city as a whole has a good growth rate (23.28% of decadal growth rate from 2001 to 2011) but there are some wards of the city as mentioned above which contradict the growth due to multiple factors like migration, rehabilitation, and other local circumstances, which have been elucidated below: 60000 50000 40000 30000 20000 10000 WARD2 WARD4 WARD5 WARD8 WARD11 WARD13 WARD14 WARD17 WARD18 WARD20 WARD21 WARD22 WARD23 WARD24 WARD25 WARD30 WARD33 WARD36 WARD37 WARD38 WARD39 WARD41 WARD42 WARD45 WARD49 WARD50 WARD52 WARD53 WARD54 WARD55

0

Wards without anomalies 1991

2001

2011

2021

Population in numbers

Fig. 6.1 Population projection with regular trends 60000 50000 40000 30000 20000 10000 WARD1 WARD3 WARD6 WARD7 WARD9 WARD10 WARD12 WARD15 WARD16 WARD19 WARD26 WARD27 WARD28 WARD29 WARD31 WARD32 WARD34 WARD35 WARD40 WARD43 WARD44 WARD46 WARD47 WARD48 WARD51

0

Wards with anaomalies 1991

2001

Fig. 6.2 Population projection with anomalies in trends

2011

2021

118

1.

2.

3.

4.

6 Slum Housing Demand Assessment and Analysis

There has been an abrupt transition of the population in the ward numbers 3, 7, 16, 26, 28, 32, 34, 35, 40, and 44 as well in the ward numbers 1, 6, 9, 10, 12, 19, 27, 31, 43, 46, 47, 48, and 51. This could be due to a change in the ward boundaries or to some extent due to the shifting of the slum population during the process of implementing rehabilitation programs. Another interpretation is that the implementation of several central government projects in the city has brought with it a flash increase in the informal population with workers migrating to remain in proximity of the workplace. The completion of the construction work has then resulted in migration of the population. This is justified by an increase in most of the slum pockets in the ward numbers 1, 2, 4, 5, 8, 11, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 30, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 49, 50, 52, 54, and 55. There are some interesting trends that can be noticed in ward numbers 1, 3, 6, 7, 9, 10, 12, 19, 26, 27, 28, 31, 34, 43, 46, 47, and 51 which reflects the flash increase (1991 to 2001) and decrease (2001–2011) of the population in the mentioned patch during the years 1991, 2001, and 2011. Beyond the above-reported incidence, some more noticeable events occurred in ward numbers 15, 29, 32, 35, 40, and 44 of the study area. These show different peculiar characteristics. Here, there is a decrease in population from the year 1991 to 2001 and an apparent increase in population from the year 2001 to 2011. These are most likely due to the migration of people, who left earlier to make a living and were then added back to the population after returning to their native home.

The complex situation described above can be recapitulated simply by stating that it has proven challenging to arrive at an exact projected population due to the highly unstable and volatile population in the ward areas. However, a fair attempt has been made taking into consideration these problems. The projection has been done with due care and understanding of the available data from acceptable sources. The best possible projection of population has been attempted and reported as below (Table 6.1).

6.3 Projecting Slum Population of Kalaburagi City Since the slum data is only available from the 2011 census, and that too at the city level rather than the ward level, projecting slum population is not at all feasible. Even though slum data was collected from the Karnataka Slum Board, AKM (Asha Kiran Mahithi), and other published reports, there is no consistency in their data. In addition to all this, there are a few more problems in projecting the slum population. 1.

The slum estimations made by international organizations and national governments have considerable variations (Amit Patel et al. 2020).

6.3 Projecting Slum Population of Kalaburagi City

119

Table 6.1 Ward wise population as per census data

Ward No

1991

2001

2011

2021

WARD1

6047

14866

12621

17752

WARD2 WARD3

6247 5758

9776 8475

12634 7971

15939.33 9614.333

WARD4 WARD5

6559 5885

8882 5945

14468 7592

17878.67 8181

WARD6 WARD7

6418 5288

9812 8392

6613 8047

7809.333 10001.33

WARD8

6018

7043

7249

8001

WARD9 WARD10

6001 6351

6576 6386

3097 5202

2320.667 4830.667

WARD11 WARD12

5461 6385

6734 12836

10278 6946

12308 9283.333

WARD13

6311

8558

17133

21489.33

WARD14 WARD15

5065 6151

6168 5416

14145 7982

17539.33 8347.333

WARD16 WARD17

6409 5168

5384 6615

5318 7061

4612.667 8174.333

WARD18 WARD19

6828 6716

9969 11699

13365 8039

16591 10141

WARD20

6826

7626

16355

19798

WARD21 WARD22

4941 5393

7355 5897

10589 15446

13276.33 18965

WARD23 WARD24

6348 4681

5738 6313

19993 10075

24338 12417

WARD25

4511

7519

9333

11943

WARD26 WARD27

4617 6252

6370 7784

5708 6544

6656 7152

WARD28 WARD29

6387 6346

9913 4346

9636 13918

11894.33 15775.33

WARD30 WARD31

5458 6061

9219 6716

12596 5048

16229 4928.667 (continued)

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6 Slum Housing Demand Assessment and Analysis

Table 6.1 (continued)

WARD32

5371

4722

11095

12786.67

WARD33

4964

6379

8027

9519.667

WARD34 WARD35

4798 5893

5762 5521

5006 8445

5396.667 9171.667

WARD36

5893

7346

11703

14124

WARD37 WARD38

5437 6247

7006 12889

7531 16218

8752 21755.67

WARD39 WARD40

5701 5989

5972 5414

7164 9275

7742 10178.67

WARD41 WARD42

5429 4818

6098 6868

6964 8180

7698.667 9984

WARD43

4775

7294

4870

5741.333

WARD44 WARD45

5706 4638

5366 5431

7703 12339

8255.333 15170.33

WARD46 WARD47

5558 5750

11422 7654

10393 5989

13959.33 6703.333

WARD48

6284

5958

4847

4259.333

WARD49 WARD50

5063 5413

6618 10076

6808 12175

7908 15983.33

WARD51 WARD52

5394 5379

7793 7015

4268 8695

4692.333 10345.67

WARD53 WARD54 WARD55

6224 5579 5809

11195 9233 9209

10736 16820 17334

13897 21785 22309

Source Kalaburagi Municipal Corporation (KMC), Census of India Nominal change in the population (less than 1000) from 2001 to 2011 Remarkable change in the population (above 1000) from 2001 to 2011 Change in the population from 1991 to 2001 Negative growth from 2011 to 2021 (as per projected population)

2.

3.

There aren’t clear guidelines regarding whether an area that has undergone development activities should be considered a “slum” or a “non-slum” and thus deleted from the list of slums. Addition of new slums into the listed slums is a major problem. Because of this, the number of slums under various categories (notified, non-notified,

6.3 Projecting Slum Population of Kalaburagi City

121

recognized, and identified) vary in different sources such as ULB, state, and NGOs. Therefore, there is a need for developing a methodology to update (deletion and addition) the census slum data. There are two channels that mainly contribute to the rising number of slums in urban areas. These are the incorporation of areas surrounding an urban area into the boundary of the urban area and the formation of settlements in empty areas that lie within the boundaries of the urban area. But these details are not updated regularly. This also leads to the underestimation of the slum population projection. The population projection of Kalaburagi city at the ward level gave a fair idea about changes in the slum population growth and their distribution. As we discussed in Chap. 2, the first slum census that included the statutory towns for counting the slum population was the 2011 census. With this data, it is difficult to project the slum population. Hence, the slum population data from Karnataka Slum Board was used for projecting the slum population. The projection will help us to know the size of the slum population that will be added to the city and help in planning for affordable housing stock and other necessary infrastructure developments. Unfortunately, we could not get the slum population details for the year 2001 and the only data available with us belonged to 1990. Along with this problem, the other major issue was that we had data for only 27 slum pockets whereas the total number of slums is 60. As something is better than nothing, an effort was made to project the slum population of these 27slum areas using the year 1990 as the base. These results have been collected in the following table (Table 6.2). The slum population growth rate was 27% for 21 years (from the year 1990 to 2011). From that, the annual growth rate was calculated to be 1.29%. With this rate of growth, the slum population was projected to the year 2021. This projection of the slum population is thus just an indication and cannot be counted as a base for housing demand, due to the non-availability of population details for all the slum areas of the city. Due to the unavailability of the complete slum population datasets for all the slums, we have only taken into account the datasets of slum populations that have slum population for both the years (1990 and 2011). Moreover, the number of slums also increased to 60 in 2011. That means the growth rate of 1.29% might not be true since this calculation was based on the available base year data (1990) for 27 slums distributed in 15 wards as given in Table 6.2. Therefore, this slum projection will not solve the actual purpose of planning for developmental activities but certainly gives an idea that one can begin with. The following maps (Figs. 6.3 and 6.4) show the distribution of population density and the slum population distribution at the ward level. The population density is high to moderate in the central wards whereas the wards touching the city boundary have comparatively less density. The distribution of the slum population followed a similar trend in the city center and near the railway station (ward number 40). The number is also high in northern wards where more brick kiln industries are located.

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6 Slum Housing Demand Assessment and Analysis

Table 6.2 Slum projection Ward no

Name of the Slum

Population 1990

Population 2011

Changes

% (1990–2011)

Projection (2021)

53

Tarfail (East)

2293

2905

612

26.6899

3278

49

Indira Nagar

1848

2031

183

9.9026

2292

53

Budha Nagar (North)

617

444

−173

−28.0389

501

17

Joshi Wada (Bambu Bazar

510

556

46

9.0196

627

23

Shaha Bazar (Harizan wada)

405

472

67

16.5432

533

31

Gazipur

800

637

−163

−20.3750

719

45

Municipal Labor Colony

396

982

586

147.9798

1108

41

Kunchi Korwar Galli

441

476

35

7.9365

537

52

Hanuman Nagar Part-1

507

481

−26

−5.1282

543

52

Hanuman Nagar Part-2

1441

1635

194

13.4629

1845

45

Mangarwadi

6

Sanjai Gandhi Nagar

8 40 23

Lambani Tanda

8

Sanjivanagar

12 43 23

Nehru NagarLangoti Peer Darga

53

Budha Nagar

300

447

147

49.0000

504

53

Tarfile (East)

2125

2349

224

10.5412

2651

23

Shaha Bazar Tanda

300

1953

1653

551.0000

2204

35

Ganaga Nagar

3500

864

−2636

−75.3143

975

22

Gandhi Leprosi Colony

240

308

68

28.3333

348

329

362

33

10.0304

409

1548

1880

332

21.4470

2122

Arya Nagar

750

825

75

10.0000

931

Brahmpur Waddar wada

708

779

71

10.0282

879

672

748

76

11.3095

844

1622

2034

412

25.4007

2295

Ramji Nagar

832

959

127

15.2644

1082

Bapu Nagar

2800

601

−2199

−78.5357

678

760

721

−39

−5.1316

814

(continued)

6.3 Projecting Slum Population of Kalaburagi City

123

Table 6.2 (continued) Ward no

Name of the Slum

Population 1990

23

Nehru Nagar (Filter bed area)

1750

52

Shamsunder Nagar

43

JaiBheem Nagar

Population 2011

Changes

% (1990–2011)

Projection (2021)

536

−1214

−69.3714

605

184

247

63

34.2391

279

436

480

44

10.0917

542

There is a Mean change of 27.2713% for the year 1990–2011 Average Change per year = 27.2713/21 = 1.285714 (implies that there is change @1.28571426%. For next 10 years (2021) = Population as on 2011 + % increase in population on 2021

Fig. 6.3 Distribution of population density at ward level

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6 Slum Housing Demand Assessment and Analysis

Fig. 6.4 Distribution of slum population at ward level

6.4 Framework for Estimating the Housing Demand of Urban Poor There is an urgent need to find a feasible solution as the total number of slum inhabitants has grown to over 1 billion. Current estimates suggest that by 2030, almost 3 billion people will be in need of housing that is adequate and affordable (UN, 2020) https://unstats.un.org/sdgs/report/2019/goal-11/. If urbanization is to be harnessed in a positive manner so as to lead to sustainable development, there’s a need to steer clear of the blunders of the past and aim at inclusive urban planning alongside regional planning that does not exclude the urban poor (Report of Slum Committee, 2010). The National Housing and Habitat Policy (1998) of the Government of India recommends that any housing policies or programs that are synthesized are driven by demand. In the UN-Habitat II conference, the National Report was presented

6.4 Framework for Estimating the Housing Demand of Urban Poor

125

and the National Plan of Action that was detailed in it once again stressed this. It is believed that this will fulfill the citizens’ dreams by giving them the kind of housing they want. (http://www.mumbaidp24seven.in/reference/Housing_Demand_Assessment_M odel.pdf).

6.4.1 Demand Assessment The housing patterns, both in public and private domains, must be planned after evaluating the net demand, which in other words is the “willingness to stay instead of a willingness to pay”. This is with respect to all the different kinds of housing solutions and for the different sets of residents of a city. Multiple forms of demand exist, such as need-based demand, spatial-location demand, and scenario demand, and these need to be assessed However, the general consensus is that the perfect solution is need-based demand. There are however lacunas in that type of demand assessment method, as we ignore the more prominent types of constraints like situation or location as well as scenario or condition. In the case of housing, it boils down to how many people the house can hold. This in turn is a product of the unconstrained utility maximization principle alongside demographic factors. Market demand better reflects reality by incorporating how willing people are which is an indicator of the relationship that quantity shares with the amount spent. In slum areas of upcoming cities like Kalaburagi, it is very difficult to project anything based solely on demographic as well as socio-economic parameters. It should also include other vital components like “willingness to stay instead of a willingness to pay”, which infers that situation and scenario will also play a role in estimating the housing demand. This doesn’t completely take into account the transformation of willingness to pay to actual actions as that would require taking into consideration additional parameters. These parameters, associated with finance, are socio-economic and demographic and include things such as the ability to acquire the required resources. This is still a step toward augmenting situations to the point that we can project and estimate everything on some fair evidence. (http://www.mumbaidp24seven.in/reference/Housing_Demand_Assessment_M odel.pdf).

6.5 Analytical Framework for Housing Demands: A Base for Estimation and Prediction The housing demand assessment of slum dwellers of Kalaburagi city would be of aid in analyzing likely future residents, within selected scenarios and locations. Meanwhile, assessing demand can also shed light upon what potential occupiers are looking

126

6 Slum Housing Demand Assessment and Analysis

for in housing solutions along the lines of different aspects such as its design, how accessible various services and other essential facilities are, its spatial position, and how far it is from their workplace. The chief objective is matching the quantitative demand for a location and scenario with the needs and wants of the likely inhabitants. The model in use for assessing the demand for housing takes into consideration differences in slum type, age group of people staying in slums, conversion of slum areas, land ownership, the transformation of surrounding area type, notified and non-notified slum area instead of traditional parameters like house prices and preferences of the people. The current model developed for the housing demand estimation can be expressed mathematically as follows: H.D. = f (SHt , Sa , Ho , Lo , At , Stn ) where H.D. = Housing Demand f = Function SHt = Slum Household type Sa = Age Group of people staying in slums Ho = House ownership Lo = Land Ownership At = Transformation of Neighboring Area Stn = Tenable and Untenable Slum Area. The analytical framework has been developed to estimate the base for housing demands and to estimate and predict using Weighted Least Squares (WLS), the standard method. The estimation and prediction require the information content of the surveyed sample, which is a practically challenging task to achieve in the study area (i.e., Kalaburagi). The complete chain has been analytically explained below in a tabulated sequence. Subsequently, the estimate has been applied to derive controlling equations. The above model can be restructured to the following type applying the weightage parameters to it: H.D. = f (w1 ∗ SHt , w2 ∗ Sa , w3 ∗ Ho , w4 ∗ Lo , w5 ∗ At , w6 ∗ Stn ) The above formula can be condensed to the following form as follows: H.D. = w1 ∗ SHt + w2 ∗ Sa + w3 ∗ Ho + w4 ∗ Lo + w5 ∗ At + w6 ∗ Stn where w1, w2 , w3 , w4, w5, and w5 stand, respectively, for the constraints SHt , Sa , Ho , Lo , At , Stn The above discussed mathematical model can be tabulated as follows (Table 6.3).

6.5 Analytical Framework for Housing Demands …

127

Table 6.3 Estimating of housing demand considering various parameters Weightage

Expression for housing demand (HD)

W1

W2

W3

W4

W5

W6

W1 *SHt + W2 *Sa + W3 * Sc + W4 * Lo + W5 * At + W6 * Snd

SHt

1

0

0

0

0

0

W1 * SHt

Sa

1

1

0

0

0

0

W1 * SHt + W2 * Sa

Ho

1

1

1

0

0

0

W1 * SHt + W2 * Sa + W3 * Ho

Lo

1

1

1

1

0

0

W1 * SHt + W2 * Sa + W 3 * H o + W 4 * Lo

At

1

1

1

1

1

0

W1 * SHt + W2 * Sa + W 3 * H o + W 4 * Lo + W 5 * At

Stn

1

1

1

1

1

1

W1 * SHt + W2 * Sa + W3 * Ho + W4 *Lo + W5 * At + W6 * Stn

Controlling factors (Housing demand)

The above table matrix explains that housing demand is dependent on the control weights of each factor (i.e., w1 , w2 , w3 , w4 , w5 , and w6 , respectively). The controlling weights (varies between 0 and 1) for different factors (Table 6.4) are defined as follows. The above-mentioned Table 6.4 explains the detailed version of the different parameters which play a vital role in the estimation of housing demands. The housing demands in the slum areas will be higher for the higher parameter values. In the rest of the cases, the values will show deviations and drifts in the demands depending upon the values selected among other categories. Table 6.4 Weightage for various controlling parameters

SHt = Slum household

Pucca House = 0 Katcha house = 1

Sa = Age group of people staying in slums

Fertile group = 1

Ho = House ownership

Owned = 0

Lo = Land ownership

Private = 1

Non-fertile group = 0 Rented = 1 Public = 0

At = Transformation of neighboring Transformed = 1 area Non-transformed = 0 Stn = Tenable and untenable slum area

Untenable = 1 Tenable = 0

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6 Slum Housing Demand Assessment and Analysis

6.6 Sample Estimation of Housing Demands for Vijayanagar Area To demonstrate the housing demand estimation, the Vijayanagar slum was chosen based on the field visit and the cooperation of the slum dwellers (Fig. 6.5). House Demands may be expressed as follows: H.D = W1 ∗ SHt + W2 ∗ Sa + W3 ∗ Ho + W4 ∗ Lo + W5 ∗ At + W6 ∗ Stn The simulation of the above equation was performed to estimate the actual housing demand for the said area (i.e., Vijayanagar slum area), using field data collected via surveys whose responses were filled by slum dwellers. The following assumptions were considered while estimating the housing demands of the urban poor residing in those areas. SHt = 106 (as the total number of houses are 213) with W1 0.5 due to the majority of Katcha houses Sa = 35 with W2 = 0.75 due to fertile age group Ho = 100 with W3 = 0.5 due to around 50% of houses are on rent L0 = 1 with W4 = 0, as the majority of the land, is under the corporation, so land ownership is having no prominent values At = 0.022 with W5 = 0, assuming no change in neighboring areas Stn = 1 with W6 = 0, assuming the area as a tenable area. Transforming the equations with the assumed values of weights and replacing all the values with data collected through field survey. H.D. = 0.5 ∗ 106 + 0.75 ∗ 35 + 0.5 ∗ 100 + 0 ∗ 1 + 0 ∗ 0.022 + 0 ∗ 1 = 129.25 say 130 (considering the above field values) The above results project that there is an urgent need for houses to be constructed in those areas as they might otherwise prefer to occupy the nearby vacant areas. This

Fig. 6.5 Vijayanagar slum area

6.6 Sample Estimation of Housing Demands …

129

is going to be the toughest problem in the forthcoming days for the administration as they’ll need to protect private as well as public land holdings.

6.7 Suitable Sites for Rehabilitation Program In general, rural migrants are very choosy about the location they settle down in. Most of the time, it depends on employment opportunities. This is because the money and time spent on reaching the place of work can be saved. In this regard, if the rehabilitation program can help them to settle down in the same place, for example with an in situ program, the success rate will be high. In many cases, the rehabilitation program ends up shifting their location into an entirely new area, far off from their job opportunities and even other basic facilities such as schools and medical facilities. That leads to forced shifting of slum population and sooner or later, these people will come back to their original location or move to other places where they can easily settle down. This also leads to misuse of this program with people letting out their allotted houses for rent to others or at times simply leaving it vacant. The very basic idea of improving slum conditions gets diluted and instead ends up creating new slums in the city (Figs. 6.6 and 6.7). Through buffer analysis, one can understand the following facts regarding the existing rehabilitation centers. The rehabilitation centers under various schemes such

Fig. 6.6 Slum map of Kalaburagi city with rehabilitation centers

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6 Slum Housing Demand Assessment and Analysis

Fig. 6.7 Rehabilitation centers with road network, schools, hospitals, and buffer zones

as Aashraya colonies are mostly located in the outskirts of the cities and away from the basic infrastructure such as schools for their children and health centers (Fig. 6.8). The following pictures show the present condition of some of these rehabilitation centers (Pictures 6.1 and 6.2).

Fig. 6.8 Location of three Aasharaya colonies (Pandit Deenadayal, Ambedkar, and SM Krishna Aashraya colonies)

6.7 Suitable Sites for Rehabilitation Program

131

Picture 6.1 Unoccupied houses at Mahatma Gandhi colony

Picture 6.2 Unoccupied houses at Dhariyapur rehabilitation program under HUDCO

In certain conditions, this rehabilitation program leads to the formation of slumlike conditions (slums) in a new location rather than real development. Suggestions based on field experience as well as from the published research findings (Appadurai 2012, Livengood and Kunte 2012, Patel et al. 2011, Victoria 2013, Risbud 2010, Joshi et al. 2002, Subbaraman et al. 2012, Gandhi 2012, Patel 2013, Slum Dwellers Act 2011, Burra 2005, Sawhney 2013):

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• One possible solution is to involve the slum community in choosing their new location by providing nominal options. • The houses should be allotted to the needy people so that the beneficiary will not move from that place. • Regular monitoring of the rehabilitation centers and maintenance of infrastructure facilities as well as monitoring the movement of residents/original beneficiaries. • Creating new job opportunities close to the rehabilitation centers. • Involving the community (particularly women) in the further development of these colonies and giving ownership rights to women of slum households. • Try to adopt in situ development programs for slum development instead of shifting them to new places. • Develop affordable housing stock to prevent future slums and meet the existing housing demand.

References Amit P, Phoram S, Brian E, Beauregard (2020) Measuring multiple housing deprivations in urban India using Slum Severity Index. Habitat International 101:102190 Appadurai A (2012) Why enumeration counts, environment & urbanization. Copyright © 2012 Int Instit Environ Develop (IIED) 24(2):639–641 Burra S (2005) Towards a pro-poor framework for slum upgrading in Mumbai, India. Envir Urban 17(1):67–88 Gandhi S (2012) Economics of affordable housing in Indian cities: the case of Mumbai. Environ Urbanizat ASIA 3(1):221–235. © 2012 National Institute of Urban Affairs (NIUA) Joshi P, Sen S, Hobson J (2002) Experiences with surveying and mapping Pune and Sangli slums on a geographical information system (GIS). Environ Urbanizat 14:225–240 Livengood A, Kunte K (2012) Enabling participatory planning with GIS: a case study of settlement mapping in Cuttack, India. Environ Urbanizat Int Instit Environ Develop (IIED) 24(1):77–97 Patel S (2013) Upgrade, rehouse or resettle? An assessment of the Indian government’s Basic Services for the Urban Poor (BSUP) programme. Environ Urbanizat 25:177–188 Patel B, Joshi R, Ballaney S, Nohn M (2011) Slum Planning schemes: a statutory framework for establishing secure tenure and improving living conditions in Indian Slums, Environment and Urbanization Asia 2011 2:45. http://eua.sagepub.com/content/2/1/45 Risbud N (2010) Typology of slums and land tenure in Indian cities. Presented during the National workshop on land tenure issues in slum free planning. Organized by: Centre of Urban Equity, CEPT University Ahmadabad. http://www.spa.ac.in/NRC/SPA’sPresentationSlumTypologyGr ading.pdf Sawhney U (2013) Slum population in India: extent and policy response. Int J Res Bus Soc Sci IJRBS 2(1). ISSN: 2147-4478. Available online at www.ssbfnet.com Slum Dwellers Act (2011) Draft Model Property Rights to Slum Dwellers Act, 2011 and Central Legislation for Street Vendors, Ministry of Housing and Urban Poverty Alleviation, 13th Nov 2011, Press Information Bureau, Government of India. http://pib.nic.in/newsite/erelease.aspx? relid=77137 Subbaraman R, O’brien J, Shitole T, Shitole S, Sawant K, Bloom DE, Patil-Deshmukh A (2012) Off the map: the health and social implications of being a non-notified slum in India. Environ Urbanizat 24:643–663

References

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Victoria C (2013) A sustainability evaluation of slum rehabilitation authority housing development at Nanapeth, Pune, India. Environ Urbanizat ASIA 4(1):121–134. https://doi.org/10.1177/097 5425313477567 www.mhupa.gov.in http://mhupa.gov.in/ https://pmaymis.gov.in/ https://pmaymis.gov.in/# https://pmay-urban.gov.in/assets/nw-pdf/ARHC.pdf www.pibguwahati.nic.in

Chapter 7

Slum Development Programs—An Overview

By 2030, ensure access for all to adequate, safe, and affordable housing and basic services and upgrade slums. Target 11.1: Sustainable Cities and Communities, SDG Goal, UN, 2015

Abstract The United Nations development goals insist that developing countries take steps to improve the living conditions of slum dwellers and eradicate poverty. India has taken major efforts and also achieved the target of MDGs but the slum populations keep on increasing. The present chapter discusses how slum development planning began in independent India and the schemes and policies that were introduced for the betterment of slum dwellers. The five-year plans of the Government of India aimed for urban development and had special programs for slum dwellers. As the land of a state comes under the purview of the state’s government, the central government only sponsored the schemes financially in most cases while the implementation of the scheme was deemed to be the responsibility of the state. Though various schemes and policies exist, the results were not up to expectations. Hence, a detailed study on popular slum development programs and the reasons for their success and failures was carried out based on government reports and published research articles. The study revealed that government projects followed the “topdown” approach and lacked community participation. The latest slum development program, Prime Minister’s Awas Yojana (earlier known as Rajiv Awas Yojana), has taken this into account and incorporated the multi-stakeholder approach, including slum dwellers in its processes. One of the reasons for the failure of early programs is the lack of spatial data and the use of outdated technology for mapping. In an effort to overcome these limitations, in this study, geospatial technology has been used as the main source for data input, surveying, and mapping. Keywords Slum developmental programs · Slum act · JNNURM · RAY · PMAY · Community participation · Geospatial technology

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_7

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7.1 Introduction More than half the world population resides in urban areas and this number only continues to rise. Slums are an inevitable part of increasing urbanization since urban areas fail to offer a decent life to these poor migrants. Efforts have been made at the international, national, regional, and local levels to improve their living conditions but most of them did not yield the expected results. Despite funding from international institutions, the quality of life of its residents did not improve much. In some situations, slum rehabilitation has even resulted in the formation of new slums. There are always some good examples, a few case studies, and success stories of slum development. However, the truth is that most slum policies failed to reach their targets. Initially, slums were considered as the dark side of urban development, and planning only sought to evacuate, remove, and clear them from the cities. The realization came later that they are an integral part of the informal sector of the urban economy, and it is inhuman to allow them to live in such an unhealthy environment. In India, after the slum clearance act, there were many programs for improving the living conditions of slum residents. Every program started out with a single mission objective, and over time, gained more objectives while also expanding spatially. Over the years, the methods adopted to implement such schemes were also improved and included community participation in the planning process. In this chapter, an overview of important slum policies and developmental programs is discussed, and the evaluation of such programs is also critically presented. It also gives a brief account of the slum development program at Kalaburagi, along with suggestions for the better intervention of slum policies based on field experience.

7.2 Slum Policies in India When the government began planning activities in the wake of independence, due care was taken to cater to the weaker sections of society through special-assistance programs and housing schemes. As land and housing were state subjects, the central government enabled the state by assisting financially and providing an administrative/legal framework for the improvement of slums. The union government directed the financial aid from the international funding sources like the World Bank to the state governments for implementing centrally sponsored schemes. There were several programs on poverty mitigation and job opportunities for the people who belong to the economically weaker section in general and specifically for urban slum dwellers. These programs aimed to improve necessary infrastructures such as clean drinking water, toilet facilities, drainage, and health care facilities. The strategies and priorities for upgrading slums are evolving, and new initiatives are tailored based on the experiences gained by implementing many schemes launched during previous plan periods.

7.2 Slum Policies in India

137

The Government of India announced the first act pertaining to slums in Dec 1956 as “THE SLUM AREAS ACT (IMPROVEMENT AND CLEARANCE)” during the Second Five-Year Plan. Subsequently, all state governments were asked to enact legislation for slum clearance by establishing the necessary setup. Each state took its own time to enact this legislation. For example, Andhra Pradesh in 1956, Tamil Nadu in 1957, Punjab and Haryana in 1961, Karnataka in 1973, Madhya Pradesh in 1976, with little alterations Maharashtra in 1971 and Gujarat in 1973 as “Slum Areas (Improvement, Clearance, and Redevelopment) Act”. Some state governments created separate setup involving housing boards and slum development boards to deal with the problems of slums. For example, Slum Clearance Board (Tamil Nadu, Karnataka) and Slum Rehabilitation Authority (Gujarat, Maharashtra). The Fourth Five-Year Plan gave special importance to the quality of the urban environment. In 1972, for improving the slum environment in urban areas, a program was introduced to provide at least minimal facilities. At the first stage, this was only applicable to urban areas with more than 8 lakhs population. Initially, it started with eleven cities and nine cities were included later on. Under the “Integrated Low-Cost Sanitation Scheme” (1980–81), loans and subsidies were given to improve the sanitation facilities in the slums (http://mhupa. gov.in/). In August 1996, the “National Slum Development Programme (NSDP)” was set in motion to provide physical and social infrastructure for slum upgradation under the nodal ministry, “The Ministry of Urban Employment and Poverty Alleviation”. From the financial year 2005–06, the NSDP had been withdrawn and a new slum development program began under the national urban renewal mission (M/o HUPA 2006; http://mhupa.gov.in/). In 2001, a new program, “Valmiki Ambedkar Awas Yojana (VAMBAY)” was launched with 50:50 funding support from both the central and state governments to improve hygiene by constructing “community toilets”. In 2005, “Integrated Housing and Slum Development Programme (IHSDP)” was started as part of the “Jawaharlal Nehru National Urban Renewal Mission (JNNURM)” (www.pibguwahati. nic.in) after discontinuing NSDP and VAMBAY programs. “Urban Basic Services for the Poor” (UBSP) was an exclusive urban poverty alleviation scheme launched during the seventh plan. The eighth plan understood the major problems of urban areas such as disorganized growth, housing shortage, and the formation of slums. It insisted on inclusive planning by providing essential infrastructures like potable water, sanitation, schooling, and health facilities to the slums (http://mhupa.gov.in/). During the decade of 1996–2005, the planning commission released Rs. 30,896.3 million as additional funding to the states and UTs to enhance the living environment of slums under the “National Slum Development” program. The M/o Urban Employment and Poverty Alleviation acted as a nodal agency for monitoring the progress of slum development programs. By the end of August 2006, 80% of this amount received by the states was spent for 45.8 million slum dwellers. The “JNNURM” announced by the Indian Government in 2005 aimed for sustainable and inclusive development of urban areas. As it aimed for inclusive development,

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a special program for integrated slum development known as “Integrated Housing and Slum Development Programme” (IHSDP) was brought under this mission. This special program was meant for upgrading the derelict situation of the slum population in urban units as per the 2001 census. It excluded 63 cities that were to be benefitted from the mission “Basic Services for the Urban Poor (BSUP)” which was also a part of JNNURM. The major funding (80%) for this mission came from the central government and the remaining was shared by the states, MLA/MP fund, and the urban local bodies. Few special category states received 90% funding from the central government. To get funding under IHSDP, the state and union governments start urban reform projects and the central government releases the funding to some nodal agency that had been nominated by the state/union government. The “Slum-free India” mission known as “Rajiv Awas Yojana” (RAY) was launched in 2009 with a planned duration of five years with a focus to grant secured land tenure to people living in slums. RAY directed the city governments to prepare “slum-free city plans” for developmental activities in slum areas that include rehabilitation and reconstruction. As per RAY, the states/UTs are responsible for the process of passing legislation on property rights for slum dwellers and implementation. In 2011, a paradigm shift in thoughts arrived in the form of “secured tenure” and “universalization of services” to upgrade the slum conditions. This initiative was known as the “Slum Dwellers Act (2011)”. It encouraged providing legal rights to the slum residents (Fig. 7.1). A centrally sponsored scheme was introduced in 2015 under the name of “Pradhan Mantri Awas Yojana—Housing for All (Urban)” and will be active until March

Fig. 7.1 Slum development programs in India

7.2 Slum Policies in India

139

Fig. 7.2 Verticals representing beneficiary options in PMAY

2022. This new scheme has replaced all previous schemes including RAY. The PMAY ensures “Housing for all by 2022”. It is meant not only for slum dwellers but also for groups belonging to economically weaker sections, and preference will be given to scheduled caste and tribes, transgenders, and widows (http://mhupa.gov.in/) (Fig. 7.2). The PMAY scheme has two sections, one for urban beneficiaries and one for rural beneficiaries. (https://pmaymis.gov.in/). During the COVID-19 pandemic, the Government also launched a scheme known as the “Affordable Rental Housing Complex (ARHC)” for rural migrants and the urban poor. The new scheme will help in ensuring a safe and easy living space for the poor (https://pmay-urban.gov. in/assets/nw-pdf/ARHC.pdf).

7.3 Review of Slum Developmental Programs It took almost four decades for the government to move from slum clearance programs in 1956 to slum development programs in 1996. By this time, the slum population had increased greatly and slum areas had also expanded spatially in size. The quality of urban life too continued to deteriorate. Many efforts were then taken to raise the standard of living and improve slum conditions by providing basic services.

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Unfortunately, the government schemes were not executed properly and there were issues in achieving the mission targets. The problem was that the beneficiaries (slum dwellers) were not aware of the benefits of government policies, and the executive bodies responsible for the implementation of the projects were careless. Politics also played its role to worsen the situation. Many researchers explored the salient features of earlier programs and discussed the main drawbacks of those programs. The following is a brief review of the same. The slum developmental programs need to have a detailed road map to show the “step by step process” that will assist officials for the better intervention of slum policies (Patel et al. 2011). Some of the central schemes aimed for improvement in the living conditions of slum dwellers by demanding contribution from them but as these people are financially impoverished, they are not able to share a percentage of the cost of construction which ends up making these schemes useless to them (Sawhney 2013). The census recognized three types of slums—notified, non-notified, and identified slums. Except for the first category of slums, the rest are not even eligible to get the benefits of slum development programs (Subbaraman et al. 2012). As per some of the slum development programs, the decision of whether to go ahead with in situ development or rehabilitation is up to urban local bodies. Without community participation, such developmental programs met failure and could not achieve their aims (Livengood and Kunte 2012). With field experience, Joshi et al (2002) insisted on community participation and teamwork of local governments, non-governmental organizations, and the slum community. They also emphasized the inclusion of slums in city planning. When the community and people’s organization are involved, it can bring a new standard to the development (Patel et al. 2001). Unfortunately, Non-Governmental Organizations (NGOs) and people’s organizations were not included in decision-making for the betterment of slums and slum dwellers (Livengood and Kunte 2012). A few slum development programs of the Union Government need a matching grant from state governments or urban local bodies. Sometimes, it is difficult to get such funding and even more difficult for it to reach the right person. Therefore, the involvement of Community-Based Organization (CBO) and NGO is required to deliver the goods and services to the right people (Burra (2005). In the few cases that community/CBO/NGO participation is there, it is merely for sharing information regarding the progress and they aren’t involved in decision-making (Patel 2013). While implementing slum developmental programs, the preference of the community to take up the project must be given priority and that will be helpful in the successful implementation of the projects and the advantages too will reach the beneficiaries (Buckley et al. 2007). To implement slum policy or slum developmental projects, we need reliable data on slums such as their existing conditions and details on available infrastructure, etc. (Patel 2013). One of the best ways of mapping a slum is involving the community in mapping and collecting the details about their present infrastructure (Appadurai 2012). Sadly many governmental agencies do not believe in this data collection and rely on professionals or private agencies (Patel et al. 2012).

7.3 Review of Slum Developmental Programs

141

Creating a stock of low-cost housing is the only possible way of preventing slum formation (Ferguson and Navarrete 2003). To bring out changes in their living, the women can play a crucial role (Risbud 2010), and slum development programs should consider this and involve them in making slum-free cities (d’Cruz and Mudimu (2013). After introducing geospatial technology as a mandatory tool in slum planning, the experts felt that Geographic Information System (GIS) can also lead to additional problems in slum planning. Accordingly, GIS-based decision-making can reduce the scope of intervention by the community as it is decided by GIS experts (Jackson 2008). A slum policy that supports the use of GIS in planning, without transparency, can only reduce the capacity of the community to be included in the process of spatial decision-making. Thus, many researchers who evaluated the government schemes encouraged community participation at every stage of slum development programs including the decision-making process. The participatory approach also showed a positive impact on the implementation of recent projects.

7.4 Slum Development Programs at Kalaburagi There were 18 slums from Kalaburagi city that were selected for slum development under the RAY program. Out of 18 slums, houses for Nomadics Budga Jangam (Rajapur), Devadasi Women, and stone artisans have been constructed in the outskirts

Fig. 7.3 New houses under RAY in the outskirts of the city

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Fig. 7.4 Closer look of those houses in Google Earth

of the city (Figs. 7.3 and 7.4). The project was taken up as a pilot project and more than 1,000 houses were constructed but these are yet to be occupied (as of 2016) (Pictures 7.1, 7.2, 7.3). The following Table 7.1 gives the detail of all 18 slums taken under RAY and list of beneficiaries are given in the annexure. The RAY program was successfully unveiled in February for its first phase after the pilot project (Picture 7.4).

Picture 7.1 The project site with a name board

7.4 Slum Development Programs at Kalaburagi

143

Picture 7.2 Houses constructed under this project

Picture 7.3 Closer look at the houses

The Rajapur slum area has been picked for in situ development. The progress of the project was documented during the field visit. The houses were demolished and residents were asked to shift to nearby areas in the same slum. The houses were mostly constructed by the residents as the majority of them are construction laborers. When we interacted with the residents, they felt extremely happy and shared their views. Through this program, their dream of a pukka house has come true. They

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Table 7.1 RAY program at Kalaburagi city D.P.R. Sl. no

Slum area name

Pilot DPR 01

01. Nomadics Budga Jangam (Rajapur)

Benefited population 211

02. Devadasi Women’s

258

03. Pandit Deendayal Nagar

271

04. Stone Artisans

259

05. Municipal Corporation Labors 05

DPR 01

01. Rajiv Gandhi Nagar

205

02. Shahbazar Tanda

205 156

04. Kutumb Kalayan Nagar

175

05. Rajapur Harijjan Wada

195

06. Kirti Nagar

125

07. Hanuman Nagar Tanda

135

DPR 02

01. Arya Nagar

18

90 201

03. Ganga Nagar

205

04. Siddhartha Nagar

160

05. Hirapur Harijjan Wada

485

06

6510.19 (Tender will be announced)

1196

02. Sanjeev Nagar

06. Brahmpur Waddar Wada Total

1024

03. Sanjiv Gandhi Nagar

07

5580.59 (Tender will be announced)

45

Total

Total

Estimated cost (in lakhs)

6585.83 (Tender will be announced)

86 1227 3447

18,676.61

were particularly happy that they can continue to live in the same area so that their livelihood is not affected (Pictures 7.5, 7.6, 7.7). The following Pictures 7.8, 7.9, 7.10 show the interactions with slum residents at various slums of Kalaburagi.

7.5 Suggestions for Intervention in Policies An interactive field survey was carried out with the slum residents of Kalaburagi. The issues, problems they are facing currently, and what their expectations are were discussed in detail. Based on the interactions with slum dwellers, the following suggestions for interventions were made for the slum areas of Kalaburagi (Table 7.2).

7.5 Suggestions for Intervention in Policies

Picture 7.4 Inauguration of RAY program

Picture 7.5 RAY In situ program (Rajapur Area)—foundation

145

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7 Slum Development Programs—An Overview

Picture 7.6 RAY In situ program (Rajapur Area)—uncompleted house structure

Picture 7.7 RAY In situ program (Rajapur Area)—final house

7.5 Suggestions for Intervention in Policies

Picture 7.8 Interaction with slum residents at Brahmpur slum

Picture 7.9 Interaction with slum residents at Vijayanagar slum

Picture 7.10 Interaction with slum residents at Borabai Nagar slums

147

Joshi Wada (Bambu Bazar)

Gazipur

Shaha Bazar Hrizanwada

Sanjiva Nagar

Arya Nagar

2

3

4

5

6

Construction Work Financing Kirana Store Gold Making Shop

1. Coolie Work in APMC 2. Working in Garage in Gunj Area

1. Construction Work 2. Coolie Work

1. 2. 3. 4.

1. Chappal making and Selling 2. Working in hotels

1. Bamboo Business 2. Working Kirana store 3. Working in Hotels

Construction Work Plumbing Work Maid Wadi Cement Factory

Tarfail (East)

1

1. 2. 3. 4.

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 Suggestions for slum development

Auto Cycle Bike By Walk

Auto Cycle Bike By Walk

Auto Cycle Bike By Walk

Auto Cycle Bike By Walk

Bus Auto Train Bike By Walk

1. Cycle 2. Bike 3. By Walk

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4. 5.

Means of transport

1. Gunj Area

1. All parts of the Gulbarga City where Construction is in Progress. 2. Gunj Area

1. All parts of the Gulbarga City where Construction is in Progress. 2. Super Market

1. Gazipur 2. Super Market 3. Chappal Bazar

1. Bambu Bazar 2. Super Market Area 3. Gunj Area

1. All parts of the Gulbarga City where Construction is in Progress. 2. Aiwan-E-Shahi 3. P&T Quarters

In which part of the city they work

(continued)

Ready to Rehabilitate in a different place

In Situ

In Situ

In Situ (Improvement)

In Situ (Upgradation)

In Situ (Upgradation)

Suggestion for slum development based on community feedback

148 7 Slum Development Programs—An Overview

Budha Nagar (North)

Kunchi Korwar Galli

Lambani Tanda

Mangarwadi

Municipal Labor Colony

Brahmpur Waddar Wada

7

8

9

10

11

12

Goat Farming Jaddu and Butti making Construction Work. Working as Helper in Schools

1. Stone Breaking Work 2. Construction Work. 3. Tailoring

1. Municipal Corporation Office

1. Waste Plastic Picking 2. Pig Farming 3. Recovering Finance

1. Goat Farming 2. Construction Work

1. 2. 3. 4.

1. Plumbing Working 2. Construction Work. 3. Helper Work in Government Offices

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

Auto Cycle Bike By Walk

Auto Cycle Bike By Walk

Auto Cycle Bike Bus

1. 2. 3. 4.

1. 2. 3. 4. Auto Cycle Bike Walking

Auto Cycle Bike By Walk

1. Auto 2. Bike 3. By Walk

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4.

Means of transport

1. All parts of the Gulbarga City where Construction is in Progress

1. Municipal Corporation Office 2. All Parts of the Gulbarga City

1. All Parts of the Gulbarga City 2. Mangarwadi

1. Lambani Tanda 2. All parts of the Gulbarga City where Construction is in Progress

1. Kunchi Korwar Galli 2. St. Josephs School 3. All parts of the Gulbarga City where Construction is in Progress

1. All parts of the Gulbarga City where Construction is in Progress 2. Municipal Corporation Offices

In which part of the city they work

In situ

(continued)

Ready to Rehabilitate in a different place

In Situ

Ready to Rehabilitate in a different place

Ready to Rehabilitate in a different place

In Situ

Suggestion for slum development based on community feedback

7.5 Suggestions for Intervention in Policies 149

Hanuman Nagar Part-1

Hanuman Nagar Part-2

Indira Nagar

Sanjay Gandhi Nagar

Ramji Nagar

Bapu Nagar

13

14

15

16

17

18

1. Construction Work. 2. Pig Farming

1. Street Vendoring Selling Vegetables, Fruits. 2. Construction Work.

1. Construction Work. 2. Working in Hotels

1. Construction Work. 2. Digging Working 3. Working in Hotels

1. Construction Work. 2. Tailoring 3. Street Vendoring Selling Vegetables, Chat.

1. Construction Work. 2. Blacksmith

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

Auto Cycle Bike By Walk

Auto Cycle Bike By Walk

Auto Cycle Bike By Walk

1. 2. 3. 4.

Auto Cycle Bike Bus

1. Auto 2. Cycle 3. Bike

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4.

Means of transport

1. Bapu Nagar 2. All parts of the Gulbarga City where Construction is in Progress

1. Darga Area 2. All parts of the Gulbarga City where Construction is in Progress

1. All parts of the Gulbarga City where Construction is in Progress 2. Super Market

1. All parts of the Gulbarga City where Construction is in Progress. 2. Bus stand and Railway station

1. Hanuman Nagar 2. All parts of the Gulbarga City where Construction is in Progress.

1. Hanuman Nagar 2. All parts of the Gulbarga City where Construction is in Progress

In which part of the city they work

(continued)

In situ (improvement)

In situ (improvement)

Ready to rehabilitate in a different place

Ready to rehabilitate in a different place

In situ (improvement)

In situ (upgradation)

Suggestion for slum development based on community feedback

150 7 Slum Development Programs—An Overview

Jai Bheem Nagar

Shaha Bazar Tanda

Sunil Nagar

Budha Nagar (South)

Shamsunder Nagar

19

20

21

22

23

1. Picking the Scrap

1. Plumbing Work 2. Construction Work 3. Auto Driving

1. Construction Work 2. Buffalo Farming 3. Cow Farming

1. Construction Work 2. Goat Farming 3. Auto Driving

1. Working in Government Office. 2. Recovering jobs for different banks.

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

Auto Cycle Bike Bus

1. 2. 3. 4.

1. 2. 3. 4. Auto Cycle Bike Bus

Auto Cycle Bike Bus

1. Auto 2. Cycle 3. Bike

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

Means of transport

All parts of the Gulbarga City

All parts of the Gulbarga City where Construction is in Progress.

1. Sunil Nagar 2. All parts of the Gulbarga City where Construction is in Progress.

1. Shaha Bazar Tanda 2. All parts of the Gulbarga City where Construction is in Progress. 3. All parts of the Gulbarga City

1. Super Market 2. All parts of the Gulbarga City where Construction is in Progress.

In which part of the city they work

(continued)

Ready to rehabilitate in a different place

In situ (improvement)

Ready to rehabilitate in a different place

In situ (upgradation)

In situ (improvement)

Suggestion for slum development based on community feedback

7.5 Suggestions for Intervention in Policies 151

Gandhi Leprosy Colony

Ganga Nagar

Hirapur

Borabai Nagar

Vijaynagar

Ambika Nagar

24

25

26

27

28

29

1. Plumbing Work 2. Construction Work

1. Plumbing Work 2. Construction labor

1. Plumbing Work 2. Construction labor

1. Working in Agriculture fields 2. Cattle Farming 3. Selling Vegetables

1. Construction Work. 2. Digging Working 3. Working in Hotels

1. Construction Work 2. Working in Hotels

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

Auto Cycle Bike Bus

Auto Cycle Bike Bus

1. 2. 3. 4.

Auto Cycle Bike Bus

1. Auto 2. Cycle 3. Bike

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

Means of transport

All parts of the Gulbarga City where Construction is in Progress

All parts of the Gulbarga City where Construction is in Progress

All parts of the Gulbarga City where Construction is in Progress

1. Hirapur 2. Kanni Market

1. All parts of the Gulbarga City where Construction is in Progress. 2. Bus stand and Railway station

Market

In which part of the city they work

(continued)

In situ (upgradation)

In situ (improvement)

In situ

In situ (upgradation)

Ready to rehabilitate in a different place

Already developed under rehabilitation scheme

Suggestion for slum development based on community feedback

152 7 Slum Development Programs—An Overview

Jilanabad

Kirti Nagar (Krishna Nagar)

Syed Galli

Rajiv Gandhi Nagar

Khanapur

Basava Nagar

Hanuman Tanda

30

31

32

33

34

35

36

Means of transport

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

Auto Cycle Bike Bus

1. Buffalo breeding 2. Financing

1. Stone Work 2. Carpenter Work 1. Auto 2. Cycle 3. Bike

1. Auto 2. Cycle 3. Bike

1. Working in Hotels as waiters 1. Auto 2. Selling Vegetable 2. Cycle 3. Bike

1. Construction Work 2. Working in Hotels

1. Selling Vegetables and Fruits 1. Auto 2. Meat shop 2. Cycle 3. Bike 4. Bus

1. Tailoring Working 2. Maid work in houses

1. Selling Vegetables and Fruits 1. Auto 2. Meat shop 2. Cycle 3. Bike 4. Bus

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued) Suggestion for slum development based on community feedback

1. Hanuman Tanda

1. SB Temple Area 2. NV College

1. Super Market 2. Mahaboob Nagar 3. Darga Area

2. Darga Area

1. Super Market

1. Sangameshwar Colony 2. SB Temple

(continued)

Ready to rehabilitate in a different place

Ready to rehabilitate in a different place

In situ (improvement)

Ready to rehabilitate in a different place

In situ (upgradation)

Ready to rehabilitate in a different place

1. Kanni Market near Bus stand In situ (upgradation)

In which part of the city they work

7.5 Suggestions for Intervention in Policies 153

Kapnur Harizan Wada (East Part)

Kutumba Kalyan Nagara Kapnur

Pandit Deendayal Upadyanagar 1. Working in Garage 2. Working in Dall Industries 3. Working in Farms

Rajapur Village Harizan Wada

Haralayya Samaj

Sangtras Wadi Near Margamma Temple

37

38

39

40

41

42

1. Working in Hotels 2. Auto Driving 3. Working in Petrol pumps

1. Making Chappals 2. Working in Kirana Shops

1. Working in Farm 2. Stone Cutting Job

1. Working in Garage 2. Working in Dall Industries

1. Working in Garage 2. Working in Dall Industries

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

Auto Cycle Bike Bus

Auto Cycle Bike Bus

Auto Cycle Bike Bus

1. 2. 3. 4.

Auto Cycle Bike Bus

1. Auto 2. Cycle 3. Bike

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4.

Means of transport

1. Sangtras 2. Gunj

1. Super Market

1. Rajapur Village

1. Gunj Area 2. Industrial area 3. Dabarabad Village

1. Gunj Area 2. Industrial area

1. Gunj Area 2. Industrial area

In which part of the city they work

(continued)

Ready to rehabilitate in a different place

In situ

In situ

Already developed under rehabilitation scheme

In situ (upgradation)

Ready to rehabilitate in a different place

Suggestion for slum development based on community feedback

154 7 Slum Development Programs—An Overview

Tarfile (East)

Kanakpur

Heerapur Harijan Wada (Part II) 1. Working in Agriculture fields 2. Cattle Farming 3. Selling Vegetables

Kyateshwar Nagar

Nehru Nagar (Filter bed Area)

Siddhartha Nagar

43

44

45

46

47

48

Auto Cycle Bike Bus

1. 2. 3. 4.

Auto Cycle Bike Bus

1. Auto 2. Cycle 3. Bike

1. 2. 3. 4.

Means of transport

1. 2. 3. 4.

Auto Cycle Bike Bus

1. Working in Hotels as waiters 1. Auto 2. Selling Vegetable 2. Cycle 3. Bike 4. Bus

1. Labor Work 2. Cattle Breeding

1. Construction Work. 1. Auto 2. Helper Work in Kirana Shop 2. Cycle 3. Bike

1. Stone Carving 2. Plumbing Working 3. Construction Work.

1. Plumbing Working 2. Construction Work. 3. Helper Work in Government Offices

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

In situ (upgradation)

Ready to rehabilitate in a different place

In situ

In situ (improvement)

In situ (upgradation)

Suggestion for slum development based on community feedback

(continued)

1. Market In situ (improvement) 2. Kanni Market near Bus stand

1. Gunj area

1. All parts of the Gulbarga City where Construction is in Progress. 2. Kirana Bazar

1. Hirapur 2. Kanni Market

All parts of the Gulbarga City where Construction is in Progress.

All parts of the Gulbarga City where Construction is in Progress.

In which part of the city they work

7.5 Suggestions for Intervention in Policies 155

Mahadev Nagar

Bhavani Nagar

Jagajeevan Ram Nagar (R.T.O office Back Area)

Nehru Nagar Langoti Peer Darga

49

50

51

52

1. Coolie work in Gunj area 2. Working in Garage

1. Wood Selling 2. Working in offices as a helper

1. Working in Garage

1. Working in government offices as helpers 2. Carpenter Works

Slum area name Gulbaraga City Occupation

Sl. no

Table 7.2 (continued)

1. 2. 3. 4.

1. 2. 3. 4.

1. 2. 3. 4.

Auto Cycle Bike Bus

Auto Cycle Bike Bus

Auto Cycle Bike Bus

1. Auto 2. Cycle 3. Bike

Means of transport

1. Gunj area

1. Corporation office

1. Gunj Area

1. Mahadev Nagar

In which part of the city they work

In situ (upgradation)

In situ

Ready to rehabilitate in a different place

In situ (upgradation)

Suggestion for slum development based on community feedback

156 7 Slum Development Programs—An Overview

References

157

References Appadurai A (2012) Why enumeration counts. Environ Urban Int Inst Environ Develop (IIED) 24(2):639–641 Buckley RM, Singh M, Kalarickal J (2007) Strategizing slum improvement in India: a method to monitor and refocus slum development programs. Global Urban Develop Mag 3:1. www.global urban.org/GUDMag07Vol3Iss1/Buckley.htm Burra S(2005) Towards a pro-poor framework for slum upgrading in Mumbai, India. Environ Urban 17:67–88 d’Cruz C, Mudimu P (2013) Community savings that mobilize federations, build women’s leadership and support slum upgrading. Environ Urban 25:31–45. First published on 11 Feb 2013 Ferguson B, Navarrete J (2003) A financial framework for reducing slums: lessons from experience in Latin America. Environ Urban 15(2):201–216 Jackson S (2008) The city from thirty thousand feet: embodiment, creativity and the use of geographic information systems as urban planning tools. Technol Cult 49(2):340–341 Joshi P, Sen S, Hobson J (2002) Experiences with surveying and mapping Pune and Sangli slums on a geographical information system (GIS). Environ Urban 14:225–240 Livengood A, Kunte K (2012) Enabling participatory planning with GIS: a case study of settlement mapping in Cuttack, India. Environ Urban 24(1):77–97 M/o HUPA (2006) Annual Report 2005–2006. http://mohua.gov.in/upload/uploadfiles/files/12A R2005_06.pdf Patel S (2013) Upgrade, rehouse or resettle? An assessment of the Indian government’s Basic Services for the Urban Poor (BSUP) programme. Environ Urban 25:177–188 Patel S, Burra S, D’Cruz C (2001) Slum/Shack dwellers international (SDI): foundations to treetops. Environ Urban 13:45 Patel B, Joshi R, Ballaney S, Nohn M (2011) Slum planning schemes: a statutory framework for establishing secure tenure and improving living conditions in Indian slums. Environ Urban Asia 2:45. http://eua.sagepub.com/content/2/1/45 Risbud N (2010) Typology of slums and land tenure in Indian cities. In: Presented during the national workshop on land tenure issues in slum free planning, Organized by: Centre of Urban Equity, CEPT University Ahmadabad Sawhney U (2013) Slum population in India: extent and policy response. Int J Res Business Soc Sci IJRBS 2(1), 2013 ISSN: 2147-4478. Available online at www.ssbfnet.com Slum Dwellers Act (2011) Draft model property rights to slum dwellers Act, 2011 and central legislation for street vendors. In: Ministry of housing and urban poverty alleviation, 13th Nov 2011. Press Information Bureau, Government of India http://pib.nic.in/newsite/erelease.aspx? relid=77137 Subbaraman R, O’brien J, Shitole T, Shitole S, Sawant K, Bloom DE, Patil-Deshmukh A (2012) Off the map: the health and social implications of being a non-notified slum in India. Environ Urban 24:643–663 http://www.spa.ac.in/NRC/SPA’sPresentationSlumTypologyGrading.pdf http://mhupa.gov.in/ https://pmaymis.gov.in/ https://pmaymis.gov.in/# https://pmay-urban.gov.in/assets/nw-pdf/ARHC.pdf) www.mhupa.gov.in www.pibguwahati.nic.in

Chapter 8

Slum-Spatial Decision Support System

City planners and policymakers can enhance the well-being of a significant portion of the human family. Let us hear from people who live in slums what has worked and what has not and what we need to do. —Ban Ki-moon, UN Secretary-General on 6 October 2014

Abstract Spatial decision-making plays a vital role in resolving spatial problems over standard decision support systems. Since spatial problems are complicated, there is no single correct solution and it needs to provide alternatives to let the user choose the solution that is appropriate to the given conditions. Slum development is a multidimensional problem and involves various stakeholders. To improve the slum situation, there were many schemes and programs by the central and state governments and many non-government organizations also worked toward the same. Despite all this, the desired results were not achieved and research was needed for identifying why this was so and to also find out what was required to attain the target. Lack of spatial component and multi-stakeholder participation in decision-making were identified as two of the important causes of such failures. The present chapter discusses the need for the spatial component in the decision support system and introduces the Spatial Decision Support System (SDSS) for slums. It also insists upon multistakeholder participation in spatial decision-making. The detailed structure of SDSS was explained and to showcase the efficacy of the slum-SDSS, a sample slum area was chosen from within the 60 slums of Kalaburagi city. The selection of the sample slum was not done arbitrarily but instead done scientifically using spatial analysis in the GIS environment. The system was developed with slum community participation at the core along with other stakeholders ranging from GIS experts to local administrators. The system developed alternative scenarios based on community input and the results were visualized as a three-dimensional model using appropriate software. Involving all the stakeholders in the spatial decision support system will ensure the successful implementation of slum development programs and achievement of the national mission. Keywords Slum · Spatial decision support system · Stakeholders · Kalaburagi city · Slum developmental programs

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Shekhar, Slum Development in India, The Urban Book Series, https://doi.org/10.1007/978-3-030-72292-0_8

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8.1 Introduction Though the government has taken significant steps toward “Slum-Free India” and adopted various strategies to improve the slum situation in India, it did not achieve the expected results. From “slum removal” (clearance) to “slum renewal”; from in situ slum upgradation to slum rehabilitation, there are various slum development programs, policies, and plans that have existed and are even presently in practice in India and but their performance has failed to meet expectations. The central and state governments and urban local body schemes were either underutilized or never utilized for those who needed it. Many programs started out well and generated a lot of expectations initially and but never saw the light of the day. The slum development process should be inclusive and interactive by including the slum residents in every stage of slum development. Before implementing the slum development programs, the slum dwellers should be consulted, involved in discussions, and their consent must be obtained for the design, structure, and other scheduled activities. They must be aware of individual stakeholder’s responsibilities in the implementation of such programs. Though the process of involving the slum community seems to slow down the implementation process, the multi-stakeholder approach is viewed as one of the best methods for the successful implementation of slum developmental projects. When the slum community is involved, it enhances the belongingness and self-responsibility to monitor the progress and the quality of implementation of the project. Even in the initial stage, this participatory approach will help in providing necessary inputs for slum upgradation. When decisions are made with their involvement and consent, they too feel accountable and help in the intervention of slum policies (Sheela 2013). Slum formation has become a part of the urbanization process in many developing countries. It is unavoidable but at the same time preventable. Creating an affordable formal housing stock will reduce the formation of new slums (Ferguson and Navarrete 2003). This is only one of many ways of going about it. On the other hand, improving the conditions in extant slums by providing the necessary infrastructure and space for their livelihood will give a facelift to slum situations. If upgradation is not possible in the same area, the slum households can be shifted to a new area with the consent of its residents and decent housing should be provided with secured tenure. But how does one arrive at the right decision? By designing an exclusive slum-spatial decision support system (Slum-SDSS), this chapter is trying to find an answer to these questions. Decision-makers often come across complex spatial problems of different kinds including well-structured and semi-structured problems. If the decision-maker can define the problem and enunciate the objectives desired in a solution, then the problem is a well-structured spatial problem. On the contrary, if one could not define precisely the problem and cannot articulate the objectives toward a sustainable solution, it is a semi-structured spatial problem (Gorry and Marton 1971; Alter 1980; Hopkins 1984 as stated in Densham 1991 and Shekhar 2014).

8.1 Introduction

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Improving the existing conditions of a slum is a kind of semi-structured spatial problem, where the objectives to attain a solution cannot be fully or precisely defined. There is no linear direction to achieve the target and often the route toward the goal is complicated. The solutions to semi-structured problems are always obtained by generating a number of alternative scenarios and selecting the most feasible one through a spatial decision support system (Shekhar 2014). Environmental decisionmaking needs to take into consideration the spatial dimension, and SDSS is the right step in this direction (Rodela et al. 2017).

8.2 Review on Spatial Decision Support System It is in the late eighties that the “Spatial Decision Support Systems (SDSS)” emerged as a new concept but by the beginning of this century, lots of literature on SDSS was available (Jabeur et al. 2011). In this work, the term “geospatial” is preferred over “spatial” as the overwhelming majority of spatial decisions occur in the geographical space. These two terms however do not have any functional difference. Malczewski’s (1999) definition of an SDSS is that it is “an interactive computerbased system designed to support a user or group of users in achieving higher effectiveness of decision-making while solving a semi-structured spatial decision problem”. Spatial problems however aren’t well structured. They are multifaceted and characterized by ambiguity. Spatial problems have no formal definitions and have no singular correct solution (Densham 1991; Gao et al. 2004; Ademiluyi and Otun 2009). Above all, solutions to the spatial problem cannot be decided individually since spatial decisions demand suggestions from various stakeholders through a process that is iterative, interactive, and participative (Densham and Goodchild 1989; Goel 1999). Multiple domains have benefitted from the application of SDSS, such as sustainable urban development, environmental management, natural resources planning, transportation, and identifying suitable sites for business activities, etc., with each of these making use of different spatial modeling techniques and spatial technologies (Jabeur et al. 2011). Some of these applications of SDSS include rural land-use planning (T V Rama Chandra et al.); water shed management (Adinarayana et al. 2000; Rao and Satish Kumar 2004); rural development (Ghose 2004); renewable energy (Andrew 2010); malaria elimination (Kelly et al. 2012); biodiversity conservation (Ravan 2002); automated tree crop management (Peeters et al. 2012); agriculture (Reddy and Rao 1995; Sreekanth et al. 2013); Indian Railways (Cook and Mukerjee 1996); groundwater (Kumar and Prasad 2002); water (Vairavamoorthy et al. 2004); emergency management system (Pujara and Vyas 2012); cyclone management (Adityam and Debashish 1998); solid waste management (Ohri and Singh 2010; Katpatal and Rao Rama 2011); infrastructure—health and education (Murty et al. 1999; Ghose et al. 2002); location planning Sikder and Yasmin (1997); and land-use allocation (Alshuwaikhat and Nasef 1996).

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8.3 Decision-Making Process in Slum Development Spatial decision-making includes the aggregation of spatial and aspatial data, from multiple stakeholders in the process of studying and structuring the spatial problems, speedy visualization of the alternative scenarios, and identifying the viable solution to meet the goals and objectives (Sugumaran and Degroote 2010). The process of spatial decision-making (Keller 1997) can be understood as an algorithm that begins with the identification of the problem, collection of the required data, using it to clearly define the problem, finding procedures that would be appropriate as solutions, and finally, resolving the problem by locating the solution that is most optimal. In the case of slum development, the decision-maker has to find answers to these questions, before he proceeds to make a final decision. The first and foremost question that must be addressed in the process of slum development is whether the aim is slum improvement, slum upgradation, or slum rehabilitation? In order to make the objectives clearer, other questions must be answered, including the following: 1. 2. 3. 4. 5. 6. 7.

How many more facilities need to be added to improve the existing slum? Will upgradation be done at the same structural level, or is a new layout planning required? Will in situ upgradation be possible or will it require a new site? If the relocation of slums is required, where should the new settlements be located? Will slum dwellers agree to move to the new sites once they are developed? Will the new sites provide the slum dwellers accessibility to all their basic needs? How to create an affordable stock of homes and where to create them?

Some of these questions are hard to answer because they involve factors that are difficult to evaluate or predict. It is obvious that these problems are difficult to define precisely and need a set of alternate solutions and thus it needs a Spatial Decision Support System.

8.4 Spatial Decision Support System (SDSS) Concepts SDSS exists to enable research for arriving at decisions in spatial problems with high complexity. Over the course of the process, multiple stakeholders including the decision-makers undergo an iterative process in decision-making by developing a large number of conceivable alternatives, compared with non-spatial problems that usually have a small or a fixed number of alternatives. The concept of an SDSS is to bring together different capabilities that are flexible in nature. This can be brought about by utilizing a collection of software modules with links between them (Amstrong et al. 1986; Densham and Amstrong 1987). Working within Sprague’s (1980) framework, Armstrong et al. (1986) together conceptualized an architecture that is made up of five unified modules of the software.

8.4 Spatial Decision Support System (SDSS) Concepts

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Fig. 8.1 Conceived SDSS model by Armstrong et al. (1986)

The modules are collections of interconnected functions with there being a database module, model base management module, display module, report generation module, and a UI module. In the above figure (Fig. 8.1), modules are represented by boxes. The user interface encompasses the other four modules because all interactions with the user take place via the interface. The flow of data between modules is indicated using arrows (Shekhar 2014). In this model, the decision-makers are not part of the system and they rely on the outputs of the system presented to them. The decision-makers interact with the system either directly or through the other stakeholders for evaluating the alternatives developed by the system based on the inputs given to it. If they found a viable alternative to solve the spatial problem, the process ends there; otherwise, the iterative process will be continued until they get an efficient solution. The number of components mentioned in the SDSS literature and its description vary from three components as stated by Lolonis (1990) and Malczewski (1999); four components as designed by Densham and Goodchild (1989); to five as stated by Armstrong et al. (1990). The following table (Table 8.1) summarizes the major components of SDSS (Shekhar 2014).

8.4.1 User Interface This is a new addition to SDSS, which will help the user to make appropriate decisions by interacting with the system. For effective decision-making, the user interface must be easy to use. It must be suitable for the graphic display of spatial information in both

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Table 8.1 Major components in traditional SDSS Major components

Role in SDSS

“Database Management System” (DBMS)

This is the core of the system. Its capabilities are the storage and manipulation of locational, topological, and thematic data types. This enables it to handle the display of cartographic data, spatial query building, and analytical modeling. The DBMS needs to allow the user of the system to create and manipulate complex spatial relations among different data types at varying levels of scale and different resolutions, and aggregation levels

“Model Base Management System” (MBMS)

To embed the modeling techniques in SDSS, there are three approaches available. The simplest is to use the DBMS’s macro or script programming language to implement modeling capabilities within the DBMS itself. A second approach for incorporating analytical models is to develop libraries of analytical subroutines (Dixon et al. 1987). The development of an MBMS is a third approach for embedding models in SDSS. It is to use an organizing structure that supports the representation and exploitation of relationships between items and minimizes redundancy of storage. The flexibility of the MBMS permits the designers of the SDSS to support a variety of modeling strategies and also model base can be updated easily. It provides the opportunity to develop and assess new algorithms or new formulas rapidly

“Graphical and tabular report generators”

It should provide a number of capabilities including the generation of high-resolution cartographic displays. These displays must be supplemented with two- and three-dimensional scatter plots, graphs, and often these specialized graphics are domain-specific

graphical and tabular forms. Hence, the user interface needs to represent objective space which depicts the parameters and solution space of an analytical model while the space representing the map space serves as a representation of the area under study and to also provide the model’s output. The user must be able to view these three spaces simultaneously. Moreover, changes made in one space should be reflected automatically in other spaces (Shekhar 2014). Such a setup makes the process of decision-making something that the user can participate in. There are however some issues related to building interactive models and changing the scenarios quickly based on the dynamic inputs (Hurion 1986; Alter 1980; Vazsonyi 1978, 1982; and Keen and Gambino 1983). Due to its complexity, the SDSS can be misinterpreted (Armstrong et al. 1990). To avoid this, the system should be built upon three types of knowledge, namely environmental knowledge (studying the problem), procedural knowledge (designing the process and determining the values of parameters in the models), and structural

8.4 Spatial Decision Support System (SDSS) Concepts

165

knowledge (the steps of algorithms and data structures on which they operate) in an integrated manner (Shekhar 2014)

8.5 Designing SDSS for Slum Development The project attempts to develop an interactive SDSS for slum development programs at the city level. The goal here is to create something that can aid the decisionmakers and planners in finding the best solution through a process that involves evaluation of the spatial properties involved in a quantitative manner and also its verification. RAY is one of the slum development programs of the Government of India. In this study, the possibilities for slum development are explored within its framework. The required software modules to implement the SDSS were created by combining commercial Environmental Systems Research Institute (ESRI) software and open-source software (CommunityViz software). The following points from the literature have been taken into account, before creating an SDSS for slums. 1.

2.

3.

4.

5.

Most stakeholders, particularly the decision-makers, are not well versed in GIS software and lack the technical know-how in integrating spatial knowledge and the abstraction of reality (Davies and Medyckyj-Scott 1996). This results in a “GIS bottleneck” and to overcome this, they need the support of domain experts and their interpretations (Armstrong 1994). There also exists a “conceptual access barrier” that restricts the efficacy of the decision support systems (Armstrong and Densham 1995). This is due to a poor understanding of the relationships between the observed phenomenon and the spatial interaction. This is complicated further by unfamiliarity with the software modules and the link between them in model development. A well-developed SDSS often needs an in-depth understanding of a problem that has been studied in detail and structured into several sub-problems. This enables the system to look into the various aspects of the problem and build necessary models to visualize and assess the results (Taylor et al. 1999). To back a “decision research” process, the decision support system should facilitate the addition/deletion of new factors into analyses. The system should permit setting the relative importance of the different factors in the analyses. It should consider multi-stakeholder participation and reflect different opinions and objectives for sustainable solutions (Densham 1991). It should be capable of displaying the analysis’s results in different ways that aid users in gleaning insights.

The methodology adopted in designing the Slum-SDSS is shown below (Fig. 8.2). The SDSS for slum development has been designed (Fig. 8.3) by taking into account the recent developments in information and communication technology, computer hardware and software, and the general usage of high-end gadgets. Hence, this design has some alterations to the basic model of Armstrong et al. (1986).

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Fig. 8.2 Methodology for building Slum-SDSS

8.6 Building SDSS Once the conceptual design was developed, it was transferred into the actual Spatial Decision Support System using ArcGIS and CommunityViz software. CommunityViz is an ArcGIS Desktop extension. It has various levels of functionalities that let it cater to different skill levels and makes it useful for various applications (www.pla ceways.com). The two components that make up the software are Scenario 360 and Scenario 3D. Scenario 360 is GIS-based decision support software that has great utility for

8.6 Building SDSS

167

Fig. 8.3 Spatial decision support system designed for slum development

local and regional planners. Scenario 3D helps to view the model output in a threedimensional form that is closer to reality (https://communityviz.city-explained.com/ communityviz/scenario360.html). The basic components, their role in SDSS, and the software module used for each component are given below in form of a table (Table 8.2).

8.7 Details of Slum-SDSS The basic components, such as DBMS, MBMS, and graphical display, are not mutually exclusive in the new design but are instead complementary to each other. The following section describes them in detail.

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Table 8.2 Basic components of SDSS in the new design Components of SDSS

Generate and evaluate alternaves

Role in SDSS

SDSS software

The three major components of SDSS are DBMS, MBMS, and Display unit. The ESRI commercial software is capable of integrating these modules and can also connect with open-source software that is used for better presentation

CommunityViz 4.4, an extension for Esri’s ArcGIS platform, consists of two components: Scenario 360 for analysis and communication, and Scenario 3D for 3D visualization. (https://communityviz.city-explai ned.com/communityviz/scenario360.html)

Developing alternate scenarios based on the input, and evaluating the results with the community participation along with other stakeholders

Scenario generation

3D visualization

(continued)

8.7 Details of Slum-SDSS

169

Table 8.2 (continued) Components of SDSS

User Interface

Alternave selected

Role in SDSS

SDSS software

Involvement of stakeholders from data generation to the evaluation of alternative scenarios for decision-making Alternative selected by the community will be accepted by the authority for further necessary action

8.7.1 Database Management System The database for slum development has been stored in a geodatabase file. It contains slum information in the form of point, line, and polygon features. The geo-referenced GeoEye satellite data provided other necessary spatial information both at the slum and city level. Table 8.3 gives the details of the spatial and non-spatial geodatabase.

8.7.2 Model Base Management System (MBMS) After the slums were mapped using the designed slum ontology, the slum characteristics were understood thoroughly and provided necessary inputs for model building. The following parameters were considered for model building and their brief descriptions are given below. 1. 2. 3.

4. 5.

Slum population density: Total population living in a slum/total area of the slum Ownership of the land/house and details of tenure: Land belongs to the Central/State/Urban Local body and secured or non-secured tenure Availability of basic infrastructure: Availability of potable drinking water, sanitation facility, sewage/drainage facility, electricity, access to houses, green space/community space, space for livestock, etc. Housing condition: Durability of house structure (dilapidated or livable) and number of households living or sharing a single housing structure Accessibility: Neighborhood characteristics like nearness to amenities such as schools, health centers, working place, bus stand, and market area (Shekhar 2014).

Non-spatial data

Land value Land ownership Density of dwellings Tenure Distance to Rly line Historical site

No of households having: Toilets availability Provision for waste disposal At Household level: Monthly income Head of the family occupation Housing condition

Distance close to: existing slums Existing infrastructure Category under proposed land use

Area of the slum No of houses House structure

Spatial data

Slum boundary Slum households location

Infrastructure: Road network Water supply Primary schools Primary health centers community workspace Community halls Topography

Vacant land Green space Proposed land use

Slum level Individual house level

Detailed TS survey at slum level Community participation

Generate data from sat image from development plan map (2021) Community participation

GPS survey Household survey (Questionnaire) Community participation

Community participation GPS survey

Methodology to collect data

Table 8.3 Database and methodology for slum development

Layout plan of slum House plan

Suitability Map for slum relocation

Slum classification: Based on available infrastructure Based on available space for upgradation Based on location

Slum classification: Based on land value Based on land ownership Based on the density of dwellings Based on tenure Based on tenability

Output

Designing the house structure and prepare slum layout

Buffer analysis MCE analysis to identify the suitable locations

Creation and visualization of different slum maps

Creation and visualization of different slum maps

Gis operations

In situ development with the redesign

New settlement development

To decide the type of action such as Slum improvement/upgradation Slum redevelopment\In situ development Densification of slum

To decide the type of action such as Slum improvement/upgradation Slum redevelopment\In situ development Densification of slum

Decision

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8.7 Details of Slum-SDSS

171

Based on this information, different suitability layers are created for slum development, and the workflow is shown in the following figures

8.8 Selection of Slum In order to identify the slum for the development program (Figs. 8.4 and 8.5), multicriteria-based hotspot analysis was used. The slum areas with poor infrastructure and a large number of tin roof houses were identified as hot spots (Fig. 8.6) and selected for the development program. Once the slum was identified, the next step was to select the optimal option for slum planning. Borabai Nagar, a slum area from ward number 40 of Gulbarga Municipal Corporation (Fig. 8.7), was selected for sample analysis in CommunityViz 4.3. The study area subset was processed in ENVI software and by using a feature extraction module, built up, open space, and green areas were extracted. The features were then exported to ArcGIS as.shp files. These feature classes were stored in slum file geodatabase along with hospitals and schools data at the city level. The existing condition is considered as New Scenario 1 in CommunityViz. Then the parcel layer was edited by deleting few parcels and renamed as “Parcels edited”. This layer along with changes in the open area layer was used to form the New Scenario 2 layer. The New Scenario 3 was created with new parcels and open area that can lead to comfortable living in a slum with three-level house structures (ground + two floors). Having these settings in the background, the foremost question of whether this slum needs an improvement, upgradation, or rehabilitation was answered in various ways. Along with the spatial analysis, the same was discussed in detail with various

Fig. 8.4 Slum have secured tenure for improvement and upgradation

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Fig. 8.5 Insecure tenure slums for improvement, upgradation, and redevelopment

Fig. 8.6 Selecting the slums through hot spot analysis

8.8 Selection of Slum

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Fig. 8.7 Kalaburagi-Borabai Nagar slum (case study)

stakeholders. A stakeholders meeting was conducted to take necessary inputs for building alternate scenarios as “community voice” is the key element in the successful implementation of a slum project. To understand the present locational advantages and disadvantages of a slum and its household conditions, environmental conditions were studied in detail. In addition to the field visits, spatial technology was used to build spatial queries, and buffer zones were created to assess the locational advantages to the slum dwellers. The results (Fig. 8.8) helped in selecting the right choice for improving the living conditions of a slum such as in situ or a rehabilitation development method. In general, earlier slum development programs planned for new residential setup in open lands located in the outskirts of the city. Most of these new housing structures are far away from the basic facilities and the place of work of its residents. This is one of the main reasons for the failure of earlier programs. The slum area Borabai Nagar is located within 2 km distance from the bus stand and railway station and well connected to other parts of the city. The slum area has enough open space but is lacking in infrastructure and building structures. The slum density is moderate (has to be verified with population data). It is surrounded by 38 schools (includes primary and high school) and 16 hospitals within a radius of 1 km.

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Fig. 8.8 Result of spatial query

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8.8 Selection of Slum

175

Fig. 8.9 3D view of slum area

So, the slum needs an upgradation of existing building structure and infrastructure rather than a mere improvement or complete resettlement.

8.8.1 Selection of Households and Housing Structure After deciding the slum development method, in this case, in situ development, the next step is selecting the housing structure with the consent of slum dwellers. The slum details such as availability of open space and number of households needing upgradation/improvement/complete renovation/reconstruction in the new site were decided with the help of all stakeholders (slum board, municipal body, corporation, NGOs, slum dweller association, residents). The housing structure whether it should be a single floor or double stored structure was also decided as per the residents’ need. The structure of housing layout and house structure for the particular slum can be created in ArcMAP with the help of CommunityViz. They are developed in different scenarios and a 3D model of the same can be displayed for their visual understanding (Figs. 8.9 and 8.10). This will help them to feel and analyze the advantage of each scenario of development and in deciding the best possible development suitable for them. The housing structure shown in Fig. 8.9 includes sample structures for demonstration purpose. Ideally, all these activities should be done with the consent of all stakeholders during the implementation of slum developmental programs for best results and that’s what was attempted. The following Fig. 8.10 shows the three possible scenarios generated (just for study purpose and not with the consent of slum dwellers) for the particular slum with the help of CommunityViz along with their graphical display in the form of bar charts. Any changes in the scenario will immediately be reflected in the graphical display. If it crosses the described limit for open space or number of houses, it will also create “alerts”, so that planning is done as per the prescribed limit of households, housing structure, and so on (Fig. 8.11).

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Fig. 8.10 3D scenes of different housing structures

Fig. 8.11 Scenarios in Community Viz

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Availability of a large number of options for generating housing structures will help to build housing structures very close to reality in CommunityViz. When these models are shown to the stakeholders, they can realize and feel the real structures and layout of their plan through the maneuver option in scenario 3D viewer. This will help them to select the best scenario for their future. If the slum development program is implemented with their involvement and decisions, then it will surely be successful in achieving its target.

8.9 Closing Words While it is true that this project has been focused on a small second-tier city, not only was care taken to ensure that the methodologies employed can be seamlessly translated over to other cities, this is also aided by another factor. The slums of India show great similarity in their structure and living conditions across the length and breadth of this vast and diverse nation. The best solution that is presently available to meet the goal of Sustainable Cities 2030 is affordable housing. With only 10 years left to meet this ambitious goal, there’s a need to go about it in the most efficient manner possible. Affordable housing requires identification of the sites that are most amenable to the ones in need of it and this can be done in a speedy and accurate manner with geospatial data and techniques but it needs to be supplemented by an SDSS for identification of the most optimum locations. It is toward this end that I hope this book and my efforts make at least a small contribution so that the dream of affordable housing and decent standards of living for all can be realized.

References Ademiluyi IA, Otun WO (2009) Spatial Decision Support Systems (SDSS) and Sustainable Development of the Third World. J Sustainable Dev Afr 10(4) Adinarayana J, Maitra S, Dent DL (2000) Development of a spatial decision support system for land use planning at district level in India. The Land 4(2):111–130 Adityam KV, Debashish S (1998) Development of a gis-based decision support system for indian cities affected by cyclones, INCEDE rep 239–50 Alshuwaikhat HM, Nasef K (1996) A GIS-Based spatial decision support system for suitability assessment and land use allocation. Arab J Sci 21(4):525–43 Alter SL (1980) Decision support sytems: current practice and continuing challenges. AddisionWesley, Reading Massachusetts Andrew A (2010) A spatial decision support system for the development of multi-source renewable energy systems, dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfilment of the requirements for the degree of Doctor of Philosophy, June, 2010

178

8 Slum-Spatial Decision Support System

Armstrong MP, Densham PJ, Rushton G (1986) Architecture for a microcomputer based decision support system. In: Proceedings of the 2nd international symposium on spatial data handling. International Geographical Union, Williamsville New York, pp 120–31 Armstrong MP (1994) Requirements for the development of GIS-based group decision support systems. J Am Soc Inf Sci 45(9):669–677 Armstrong MP, Densham P (1995) Cartographic support for collaborative spatial decision-making. In: Conference: twelfth international symposium on computer-assisted cartography (Auto Carto 12) Armstrong MP, Densham PJ, Lolonis P, Rushton G, Tewari VK (1990) A knowledge-based approach for supporting locational decision making. Environ Plan B17:341–64 Cook P, Mukerjee A (1996) India Railways GIS based Decision-Support System. ESRI User Conference. http://trid.trb.org/view.aspx?id=677255 Davies C, Medyckyj-Scott D (1996) GIS users observed. Int J Geogr Inf Sci 10:363–384. https:// doi.org/10.1080/02693799608902085 Densham PJ, Armstrong MP (1987) A spatial decision support system for locational planning: design, implementation and operation. In: Proceedings of AUTOCARTO 8. ACSM/ASPRS, Bethesda Maryland, pp 112–21 Densham PJ, Goodchild MF (1989) Spatial decision support system: a research agenda. In: Proceedings of GIS/LIS’89, Florida, pp 707–716 Densham P (1991) Spatial decision support systems. In: Maguire DJ, Goodchild MF, Rhind DW (ed) Geographical information systems: principles and applications, Longman, London Dixon JF, Openshaw S, Wymer C (1987) A proposal and specification for a geographical analysis subroutine library. Northern Regional Research Laboratory Research Report3 NRRL, University of Newcastle-upon-Tyne Durga Rao KHV, Satish Kumar D (2004) Spatial Decision support system for watershed management. Water Resour Manag 18(5):407–423 Ferguson B, Navarrete J (2003) A financial framework for reducing slums: lessons from experience in Latin America. Enviro Urban 15(2):201–216 Gao S, Sundaram D, Paynter J (2004) Flexible support for spatial decision making. In: Paper presented at the 37th annual Hawaii international conference on system sciences, Honolulu, Hawaii Goel RK (1999) Suggested framework (along with prototype) for realizing spatial decision support systems (SDSS). In: Paper presented at map India 1999 natural resources information system conference, New Delhi, India Ghose A (2004) An Integrated spatial decision support system for rural development department of orissa, 1–5 https://www.isprs.org/proceedings/XXXV/congress/comm7/papers/242.pdf Ghosh M, Lal S, Nathawat MS (2002) Spatial decision support system using GIS based infrastructure: planning in health & education for Ranchi District. In Map India Gorry GA, Mortan MS (1971) A framework for management information systems. Sloan Manag Rev 13:56–70 Hopkins L (1984) Evaluation methods for exploring ill defined problems. Environ Plan B11:339–48 Hurion RD (1986) Visual interactive modelling. Eur J Oper Res 23:281–7 Jabeur, N, Sahli N, Haddad H (2011) Sensor network and geosimulation: keystones for spatial decision support system. In: Jao C (ed). ISBN 978-953-307-441-2, 478 pp., Publisher: InTech, Chapters Published September 06, 2011 under CC BY-NC-SA 3.0 License Katpatal Yashwant B, Rao Rama BVS (2011) Urban spatial decision support system for municipal solid waste management of nagpur urban area using high resolution satellite data and geographic information system. J Urban Plan Dev, 65–76. https://doi.org/10.1061/(ASCE)UP.1943-5444. 0000043 Keen PGW, Gambino TJ (1983) Building a decision support system: the mythicalman-month revisited. In: Bennett JL (ed) Building decision support systems. Addison-Wesley, Reading, MA, pp 133–172

References

179

Kelly GC, Tanner M, Vallely A, Clements A (2012) Malaria elimination: moving forward with spatial decision support systems. Trends Parasitol 28(7):297–304. http://www.ncbi.nlm.nih.gov/ pubmed/22607693 Keller CP. (1997) Unit 57-decision-making using multiple criteria. http://www.geog.ubc.ca/cou rses/klink/gis.notes/ncgia/u57.html. Accessed 9 Dec 2013 Kumar A, Prasad LB (2002) Development of spatial decision support system for groundwater using gis based numerical modelling technique-Margajo Watershed, Hazaribagh. In Hydrology and Watershed Management: Proceedings of International Conference: with a focal theme on water quality and conservation for sustainable development Lolonis P (1990) Methodologies for supporting location decision making: state of the art and research directions. In: Working paper, Department of Geography, The University of Iowa Malczewski J (1999) GIS and multi criteria decision analysis. John Wiley & Sons Inc, Newyork Murty KCS, Raheja JL, Ranjan P (1999) Spatial decision support systems for gis based infrastructure planning. In Map India Ohri A, Singh DP (2010) GIS based secondary storage and transportation system planning for municipal solid waste, Int J Civ Eng Technol (IJCIET), ISSN 0976 – 6308(Print) ISSN 0976 – 6316(Online), May - June (2010), 1(1):108–130 Peeters A, Ben-Gal A., Hetzroni A., Zude M (2012) Developing a GIS-Based Spatial decision support system for automated tree crop management to optimize Irrigation Inputs, International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Leipzig, Germany, Volume: http://www.iemss.org/society/index.php/iemss-2012-proceedings Pujara Goral K, Anjana V (2007) Open-Source GIS Software to Improve Decision Support System of Emergency Management System”, no. Unisdr: 1–20. https://www.urisa.org/clientuploads/direct ory/Documents/Journal/Under%20review/Pujara_Vyas_GIS_in_emergency_management.pdf Ravan S (2002) Spatial decision support system for biodiversity conservation. [email protected] Rodela R, Bregt AK, Ligtenberg A, Pérez-Soba M, Verweij P (2017) The social side of spatial decision support systems: investigating knowledge integration and learning. Environ Sci Policy 76(177–184). https://doi.org/10.1016/j.envsci.2017.06.015. Sheela P (2013) Upgrade, rehouse or resettle? An assessment of the Indian government’s Basic Services for the Urban Poor (BSUP) programme. Environ Urban 25(1):177–188 Sikder IU, Yasmin N (1997) Spatial decision support system for location planning. International Journal of Aerospace Survey and Earth Sciences 3(4):1–10 Shekhar S (2014) Improving the slum planning through geospatial decision support system. Int Arch Photogramm Remote Sens Spat Inf Sci XL-2:99–105 Sprague RH (1980) A framework for the development decision support systems. Manage Inf Sci Quart 4:1–26 Sreekanth PD, Soam SK, Kumar KV (2013) Spatial Decision support system for managing agricultural experimental farms. Current Science, 105(11) Sugumaran R, Degroote J (2010) Spatial decision support systems: principles and practices. CRC Press, 507 pp. Taylor K, Walker G, Abel D (1999) A framework for model integration in spatial decision support systems. Int J Geograph Inf Sci 13:533–555 Vairavamoorthy KJM, Yan H, Galgale UK, S Mohan S, and S D Gorantiwar SD(2004) PeopleCentred approaches to water and environmental sanitation-A GIS based spatial decision support system for modelling contaminant intrusion into water distribution systems. In 30th WEDC International Conference, Vientiana, LaoPDR, 513–20

180

8 Slum-Spatial Decision Support System

Vazsonyi A (1978) Decision support systems: the new technology of decision making? Interfaces 9:74–8 Vazsonyi A (1982) Decision support systems, computer literacy and electronic models. Interfaces 12:74–8 www.placeways.com https://communityviz.city-explained.com/communityviz/scenario360.html)