A methodology to Geographic Cellular Automata model accounting for spatial heterogeneity and adaptive neighborhoods

The neighborhood effect, a pivotal element within the realm of Geographic Cellular Automata (GCA) modeling, has garnered significant attention in research. However, no research has yet investigated GCA modeling based on varying neighborhood sensitivity for different land use types. In this study, we sought to bridge this gap by integrating the First Law of Geography with diverse sensitivities of different land use types, thus introducing a novel approach termed Adaptive Spatially Heterogeneous Neighborhood (ASHN) for GCA modeling. By applying this innovative framework to three regions, namely Beijing, Wuhan, and the Pearl River Delta, we elucidated the implementation process and conducted comprehensive land use change simulations. The calibration period spanned from 2000 to 2010, followed by the validation period from 2010 to 2020. The results demonstrated that the ASHN-GCA model outperformed both the Adaptive Homogeneous Neighborhood Geographic Cellular Automata (AHN-GCA) model and the Homogeneous Neighborhood Geographic Cellular Automata (HN-GCA) model, yielding superior Overall Accuracy (OA), kappa, fuzzy kappa, and Figure of Merit (FoM) scores. Furthermore, the ASHN-GCA model provided more nuanced and detailed insights into landscape patterns, further highlighting its efficacy and potential for advancing GCA modeling in land use dynamics.