Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation.

Urbanization processes have accelerated over recent decades, prompting efforts to model land use change (LUC) patterns for decision support and urban planning. Cellular automata (CA) are extensively employed given their simplicity, flexibility, and intuitiveness when simulating dynamic LUC. Previous research, however, has ignored the spatial heterogeneity among sub-regions, instead applying the same transition rules across entire regions; moreover, most existing methods extract neighborhood effects with only one data time slice, which is inconsistent with the nature of neighborhood interactions as a long-term process exhibiting obvious spatiotemporal dependency.