Incorporation of spatial anisotropy in urban expansion modelling with cellular automata

Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations.

Automated summary

Cellular Automata (CA) models are widely used for simulating urban expansion. This study develops a method to measure the spatial anisotropy (SA) of urban areas and integrates it into a CA model. The case study in Huizhou, China, demonstrates that considering SA improves the accuracy of the model.