Modelling urban change with cellular automata

Contemporary issues and future research directions

Dados Bibliográficos

ID11950327
AutoresYan Liu (0000-0002-1612-779X, The University of Queensland, autor correspondente), Michael Batty (0000-0002-9931-1305, University College London, UK), Siqin Wang (0000-0002-1809-7088, The University of Queensland), Jacqueline Corcoran (0000-0003-3565-6061, The University of Queensland)
Ano2019
Volume45
Fascículo1
Páginas3-24
Data de publicação2019-12-23
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoProgress in Human Geography (JOURNAL)
Identificadores do periódicoISSN: 0309-1325 • E-ISSN: 1477-0288
EditoraSAGE Publishing (PUBLISHER • US)
DOI10.1177/0309132519895305
OpenAlexW2996712495
IdiomaEN
Citações recebidas34
Referências citadas105

The study of land use change in urban and regional systems has been dramatically transformed in the last four decades by the emergence and application of cellular automata (CA) models. CA models simulate urban land use changes which evolve from the bottom-up. Despite notable achievements in this field, there remain significant gaps between urban processes simulated in CA models and the actual dynamics of evolving urban systems. This article identifies contemporary issues faced in developing urban CA models and draws on this evidence to map out four interrelated thematic areas that require concerted attention by the wider CA urban modelling community. These are: (1) to build models that comprehensively capture the multi-dimensional processes of urban change, including urban regeneration, densification and gentrification, in-fill development, as well as urban shrinkage and vertical urban growth; (2) to establish models that incorporate individual human decision behaviours into the CA analytic framework; (3) to draw on emergent sources of ‘big data’ to calibrate and validate urban CA models and to capture the role of human actors and their impact on urban change dynamics; and (4) to strengthen theory-based CA models that comprehensively explain urban change mechanisms and dynamics. We conclude by advocating cellular automata that embed agent-based models and big data input as the most promising analytical framework through which we can enhance our understanding and planning of the contemporary urban change dynamics

Cartography · Cellular automaton · Civil engineering · Economic geography · Environmental planning · Field (mathematics · Gentrification · Geography · Land use · Management science · Thematic map · Urban planning · 3D Modeling in Geospatial Applications · Computer Science · Engineering · Land Use and Ecosystem Services · Mathematics · Urban Design and Spatial Analysis · Artificial Intelligence