Handbook on Big Data, Artificial Intelligence and Cities: (Research Handbooks in Urban Studies series)

This pioneering Handbook outlines the ways in which big data and artificial intelligence (AI) are reshaping cities. Leading scholars analyze how innovative computational methods can make use of the vast amounts of data available to gain new insights into urban life, inform policy, and drive innovation. Chapters delve into specific applications of big data and AI including mobility, tourism, and land use, drawing on case studies from diverse urban environments across Europe and North America. Expert authors evaluate future opportunities for leveraging these technologies, addressing the integration of machine learning into spatial econometric models, the use of self-organizing maps to study demographic shifts, and novel approaches to simulating contagion patterns during pandemics. Ultimately, the Handbook emphasizes the potential of AI to contribute to social good. Academics and students in human geography, regional and urban studies, economics, sociology, and management will benefit from this multidisciplinary and comprehensive Handbook. Combining theoretical insights with practical applications, it is also a valuable resource for policymakers and practitioners interested in the ongoing digital transformation of urban spaces.

Table of Contents:
Contents Preface xiii 1 Introduction to the Handbook on Big Data, Artificial Intelligence and Cities 1 Dani Broitman, Katarzyna Kopczewska and Daniel Czamanski 2 AI, design and planning processes 5 Michael Batty PART I BIG DATA AND CITIES 3 Bayesian modelling and cities 16 Chris Brunsdon 4 A big-data-based framework for the nexus of urban smartness and urban vitality: spotlights on small and medium-sized towns 35 Hanna Obracht-Prondzyńska, Karima Kourtit, Peter Nijkamp and Dorota Kamrowska-Załuska 5 Detecting residential reconversion within cities: how can ‘big data’ be mobilized to better understand what is going on? 73 Jean Dubé, Katarzyna Kopczewska and Sarah Desaulniers 6 The geography of segregated online social networks in the largest US cities 92 Balázs Lengyel, Eszter Bokányi and Sándor Juhász 7 How big is your data? Critical remarks on Big Data analytics and co-creation processes in smart urban tourism research 110 João Romão 8 Urban economies, land use, and social dynamics in the city: big data and measurement 125 Albert Saiz and Arianna Salazar-Miranda 9 A two-dimensional framework of citizen participation in digital transformation of European cities 166 Yilin Wang, Haozhi Pan and Geoffrey Hewings 10 Listening and comprehending the pulse of places: cultural analysis of emotions in Big Data and polarisation 189 Annie Tubadji, Frederic Boy, Talita Greyling, Stephanie Rossouw and Yashi Jain PART II ARTIFICIAL INTELLIGENCE AND CITIES 11 The urban geography of artificial intelligence in Europe 224 Camilla Lenzi 12 Self-organising maps for exploring the change in Portuguese communities in Toronto 243 Eric Vaz 13 Machine learning applications to spatiotemporal land-use change modeling 257 Emre Tepe 14 Urban mining for direct geomarketing: mobile data analysis with association rules 277 Maciej Sacharczuk and Katarzyna Kopczewska 15 Urban AI for social good: mapping research directions and imperatives 309 Laurie A. Schintler, Connie L. McNeely and Vasilii Nosov 16 Simulating COVID-19 contagion patterns using a machine-learningaugmented agent-based model 327 Zi Hen Lin, Yair Grinberger and Daniel Felsenstein 17 Detecting and measuring spatial spillover effects and heterogeneity using interpretable tree-based machine learning approaches: an illustration using the Boston housing dataset 349 Mehmet Güney Celbiş, Pui-Hang Wong, Karima Kourtit and Peter Nijkamp 18 Predicting housing price bubbles: the power and limits of selected machine learning methods 377 Alon Sagi, Avigdor Gal and Dani Broitman Index 390

About the Author :
Edited by Dani Broitman, Faculty of Architecture and Town Planning, Technion – Israel Institute of Technology, Israel, Katarzyna Kopczewska, Faculty of Economic Sciences, University of Warsaw, Poland and Daniel Czamanski, Faculty of Economics and Business Administration, Ruppin Academic Center, Israel

Review :
‘Technology is developing increasingly faster and its impact on the world’s nature and culture seems to grow at an exponential pace. This Handbook collects brand new research on two of the most dynamic fields, big data and artificial intelligence and their role in cities transformation. It’s indeed worth reading!’ 'This book comes at a critical time, when big data and artificial intelligence are changing the nature of our cities, and the way they are managed. It comprises some of the best thinkers on urban science, and provides great food for thought on the future of urban areas.' ‘By presenting new methodological and empirical insights into big data, artificial intelligence and cities, the book provides a fascinating insight about the relevant state of knowledge and the challenges that scholars face and attempt to solve using big data in the urban context.’