Handbook on Big Data, Artificial Intelligence and Cities

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, The Philip & Ethel Klutznick Center for Urban and Regional Studies, Technion, Israel Institute of Technology, Israel

‘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!’

• ContentsPreface xiii1 Introduction to the Handbook on Big Data, Artificial Intelligence andCities 1Dani Broitman, Katarzyna Kopczewska and Daniel Czamanski2 AI, design and planning processes 5Michael BattyPART I BIG DATA AND CITIES3 Bayesian modelling and cities 16Chris Brunsdon4 A big-data-based framework for the nexus of urban smartness and urbanvitality: spotlights on small and medium-sized towns 35Hanna Obracht-Prondzyńska, Karima Kourtit, Peter Nijkamp andDorota Kamrowska-Załuska5 Detecting residential reconversion within cities: how can ‘big data’ bemobilized to better understand what is going on? 73Jean Dubé, Katarzyna Kopczewska and Sarah Desaulniers6 The geography of segregated online social networks in the largest US cities 92Balázs Lengyel, Eszter Bokányi and Sándor Juhász7 How big is your data? Critical remarks on Big Data analytics andco-creation processes in smart urban tourism research 110João Romão8 Urban economies, land use, and social dynamics in the city: big data andmeasurement 125Albert Saiz and Arianna Salazar-Miranda9 A two-dimensional framework of citizen participation in digitaltransformation of European cities 166Yilin Wang, Haozhi Pan and Geoffrey Hewings10 Listening and comprehending the pulse of places: cultural analysis ofemotions in Big Data and polarisation 189Annie Tubadji, Frederic Boy, Talita Greyling, Stephanie Rossouw andYashi JainPART II ARTIFICIAL INTELLIGENCE AND CITIES11 The urban geography of artificial intelligence in Europe 224Camilla Lenzi12 Self-organising maps for exploring the change in Portuguese communitiesin Toronto 243Eric Vaz13 Machine learning applications to spatiotemporal land-use change modeling 257Emre Tepe14 Urban mining for direct geomarketing: mobile data analysis withassociation rules 277Maciej Sacharczuk and Katarzyna Kopczewska15 Urban AI for social good: mapping research directions and imperatives 309Laurie A. Schintler, Connie L. McNeely and Vasilii Nosov16 Simulating COVID-19 contagion patterns using a machine-learningaugmented agent-based model 327Zi Hen Lin, Yair Grinberger and Daniel Felsenstein17 Detecting and measuring spatial spillover effects and heterogeneity usinginterpretable tree-based machine learning approaches: an illustration usingthe Boston housing dataset 349Mehmet Güney Celbiş, Pui-Hang Wong, Karima Kourtit and Peter Nijkamp18 Predicting housing price bubbles: the power and limits of selected machinelearning methods 377Alon Sagi, Avigdor Gal and Dani BroitmanIndex 390