Handbook on Big Data, Artificial Intelligence and Cities

Edited by Dani Broitman, Katarzyna Kopczewska () and Daniel Czamanski ()

in Books from Edward Elgar Publishing

Abstract: 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.

Keywords: Big Data; Urban Development; Smart City; Machine Learning; Spatial; Urban System (search for similar items in EconPapers)
Date: 2025
ISBN: 9781803928043
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.elgaronline.com/doi/10.4337/9781803928050 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Chapters in this book:

Ch 1 Introduction to the Handbook on Big Data, Artificial Intelligence and Cities

Dani Broitman, Katarzyna Kopczewska and Daniel Czamanski

Ch 2 AI, design and planning processes

Michael Batty

Ch 3 Bayesian modelling and cities

Chris Brunsdon

Ch 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

Ch 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

Ch 6 The geography of segregated online social networks in the largest US cities

Balázs Lengyel, Eszter Bokányi and Sándor Juhász

Ch 7 How big is your data? Critical remarks on Big Data analytics and co-creation processes in smart urban tourism research

João Romão

Ch 8 Urban economies, land use, and social dynamics in the city: big data and measurement

Albert Saiz and Arianna Salazar-Miranda

Ch 9 A two-dimensional framework of citizen participation in digital transformation of European cities

Yilin Wang, Haozhi Pan and Geoffrey Hewings

Ch 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

Ch 11 The urban geography of artificial intelligence in Europe

Camilla Lenzi

Ch 12 Self-organising maps for exploring the change in Portuguese communities in Toronto

Eric Vaz

Ch 13 Machine learning applications to spatiotemporal land-use change modeling

Emre Tepe

Ch 14 Urban mining for direct geomarketing: mobile data analysis with association rules

Maciej Sacharczuk and Katarzyna Kopczewska

Ch 15 Urban AI for social good: mapping research directions and imperatives

Laurie A. Schintler, Connie L. McNeely and Vasilii Nosov

Ch 16 Simulating COVID-19 contagion patterns using a machine-learning-augmented agent-based model

Zi Hen Lin, Yair Grinberger and Daniel Felsenstein

Ch 17 Detecting and measuring spatial spillover effects and heterogeneity using interpretable tree-based machine learning approaches: an illustration using the Boston housing dataset

Mehmetm Güney Celbiş, Pui-Hang Wong, Karima Kourtit and Peter Nijkamp

Ch 18 Predicting housing price bubbles: the power and limits of selected machine learning methods

Alon Sagi, Avigdor Gal and Dani Broitman

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:elg:eebook:21797

Ordering information: This item can be ordered from
http://www.e-elgar.com

Access Statistics for this book

More books in Books from Edward Elgar Publishing
Bibliographic data for series maintained by Darrel McCalla ().