Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%-3.9% in two simulation periods compared with the Logistic-CA model with a 3?×?3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration. Extended neighborhood effects and their influence on urban dynamics were addressed in this study.A logistic regression urban CA model incorporating the extracted neighborhood rules, Logistic-LNCA, was developed.The Logistic-LNCA model achieved higher simulation accuracy than the Logistic-CA model with a 3?×?3 kernel.The simulation accuracy and the Kappa coefficient varied with window sizes and radius intervals.There is an optimal window size in cellular space and corresponding optimal parameter configurations.