Modelling urban change with cellular automata: Contemporary issues and future research directions

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.

Automated summary

The study of land use change in urban and regional systems has been significantly transformed by the emergence of cellular automata models in the last four decades. There are still gaps between urban processes simulated in CA models and actual urban dynamics, leading to the need for comprehensive models that capture multi-dimensional processes, incorporation of human decision behaviors, utilization of big data for calibration and validation, and strengthening of theory-based models to explain urban change mechanisms comprehensively. Cellular automata embedded with agent-based models and big data input are seen as the most promising analytical framework to enhance understanding and planning of contemporary urban change dynamics.