Integrating multi-criteria analysis and spherical cellular automata approach for modelling global urban land-use change

Existing geosimulation land-use change models are predominantly designed to operate at local or regional spatial scales. When these models are applied on data at the global level, they do not consider the effects of spatial distortions caused by the curvature of the Earth's surface and often lack some refinements related to land suitability analysis. Therefore, the main objective of this study is to integrate multi-criteria evaluation with spherical cellular automata, to develop a novel modelling approach for simulating global urban land-use change. The world region is clustered into sub-regions to improve the suitability analysis. The obtained results reveal differences in urban growth rate and the size of the extent across the four clusters. The 64% of the total global urbanization are occurring in urban region clusters characterized by high gross domestic product and population density, while urban regions in isolated locations have the lowest urban growth rate.

Automated summary

Existing geosimulation land-use change models are mainly designed for local or regional spatial scales, but they lack considerations for spatial distortions and land suitability analysis at the global level. This study integrates multi-criteria evaluation with spherical cellular automata to develop a novel modeling approach for simulating global urban land-use change.