Simulating urban expansion using cellular automata model with spatiotemporally explicit representation of urban demand

Cellular automata (CA) has become one of the most prevalent approaches for spatially explicit urban growth modeling. Previous studies have investigated how the key components of CA models are defined, structured, and coupled to represent the top-down and bottom-up processes of urban growth. However, the spatiotemporal heterogeneity of urban demand at the macro level and its coupling with micro-level urban land configurations have not been fully explored in existing CA models. This study proposes a new urban CA modeling framework to simulate urban expansion by using a spatiotemporally explicit urban demand modeling scheme that guides the patch-based allocation of urban land at the micro level. In this framework, spatiotemporal Gaussian-based models were applied to represent the spatiotemporal heterogeneity of urban demand within a set of concen-tric rings in terms of the fraction of new urban land and frequency of new urban development. An application of the modeling framework to the metropolitan city of Wuhan, China demonstrates that the demand of new urban land in the study area exhibits an outgoing wave-shaped propagation pattern, which can be well fitted by the spatiotemporal Gaussian-based models, with R2 values exceeding 0.8. The proposed spatiotemporally explicit representation of urban demand can improve model performance in capturing urban dynamics at both macro and micro levels, as revealed by pattern-level similarity and cell-level agreement of simulation results.

Automated summary

This study proposes a new urban cellular automata (CA) modeling framework that incorporates a spatiotemporally explicit urban demand modeling scheme. The framework uses spatiotemporal Gaussian-based models to represent the heterogeneity of urban demand. The application of the framework to Wuhan, China demonstrates the ability to capture the wave-shaped propagation pattern of new urban land demand and improve model performance at both macro and micro levels.