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 (Faculty of Economics and Business Administration, Ruppin Academic Center, Israel)

"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"--

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Year of publication:	2025
Other Persons:	Broitman, Dani (ed.) ; Kopczewska, Katarzyna (ed.) ; Czamanski, Daniel Z. (ed.)
Institutions:	Edward Elgar Publishing (publisher)
Publisher:	Cheltenham, UK : Edward Elgar Publishing
Subject:	Big Data \| Urban Development \| Smart City \| Machine Learning \| Spatial \| Urban System \| Künstliche Intelligenz \| Artificial intelligence \| Big data \| Stadtentwicklung \| Urban development \| Smart city

Extent:	1 Online-Ressource (414 pages)
Series:	Research handbooks in urban studies series
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Aufsatzsammlung
Language:	English
Notes:	Includes bibliographical references and index Contents: Preface -- 1. Introduction to the handbook on big data, artificial intelligence and cities / Dani Broitman, Katarzyna Kopczewska and Daniel Czamanski -- 2. Ai, design and planning processes / Michael Batty -- Part I: Big data and cities -- 3. Bayesian modelling and cities / Chris Brunsdon -- 4. A big-data-based framework for the nexus of urban smartness and urban vitality: Spotlights on small and medium-sized towns / 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? / Jean Dubé, Katarzyna Kopczewska and Sarah Desaulniers -- 6. The geography of segregated online social networks in the largest us cities / 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 / João Romão -- 8. Urban economies, land use, and social dynamics in the city: Big data and measurement / Albert Saiz and Arianna Salazar-Miranda -- 9. A two-dimensional framework of citizen participation in digital transformation of European cities / Yilin Wang, Haozhi Pan and Geoffrey Hewings -- 10. Listening and comprehending the pulse of places: Cultural analysis of emotions in big data and polarisation / 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 / Camilla Lenzi -- 12. Self-organising maps for exploring the change in portuguese communities in toronto / Eric Vaz -- 13. Machine learning applications to spatiotemporal land-use change modeling / Emre Tepe -- 14. Urban mining for direct geomarketing: Mobile data analysis with association rules / Maciej Sacharczuk and Katarzyna Kopczewska -- 15. Urban AI for social good: Mapping research directions and imperatives / Laurie A. Schintler, Connie L. McNeely and Vasilii Nosov -- 16. Simulating COVID-19 contagion patterns using a machine-learningaugmented agent-based model / 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 / 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 / Alon Sagi, Avigdor Gal and Dani Broitman -- Index.
ISBN:	978-1-80392-805-0 ; 978-1-80392-804-3
Other identifiers:	10.4337/9781803928050 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

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