An urban cellular automata model based on a spatiotemporal non-stationary neighborhood

Spatiotemporal modeling has long been a major concern of geographic information science. Even though previous research has shown the importance of temporal and spatial information in quantifying the neighborhood effects of urban cellular automata (CA) models, constructing a spatiotemporal non-stationary neighborhood remains a challenge, due to the complexity of the spatiotemporal models. In this study, we introduced spatiotemporal modeling into the neighborhood of an urban CA model and constructed a geographically and temporally weighted neighborhood (GTWN). A corresponding approach to optimizing the bandwidth of the GTWN was also developed. Taking Beijing and Wuhan in China as examples, the GTWN-CA model was employed to simulate their urban expansion. The experimental results indicate that the GTWN-CA model has a better and performance than other CA models whose neighborhood is constructed based on the assumption of temporal or spatial stationarity, highlighting the advantages of spatiotemporal modeling in quantifying the neighborhood effect. Compared with the commonly used CA model with a homogeneous neighborhood (HON-CA), in terms of the figure of merit (FoM), the calibration accuracy of the GTWN-CA model was improved by 0.87% in Beijing and 5.4% in Wuhan, and the validation accuracy was improved by 7.9% in Beijing and 8.9% in Wuhan.