A cellular automata approach of urban sprawl simulation with Bayesian spatially-varying transformation rules

Incorporating spatial nonstationarity in urban models is essential to accurately capture its spatiotemporal dynamics. Spatially-varying coefficient methods, e.g. geographically weighted regression (GWR) and the Bayesian spatially-varying coefficient (BSVC) model, can reflect spatial nonstationarity. However, GWR possess weak ability eliminating the negative effects of non-constant variance because the method is sensitive to data outliers and bandwidth selection. We proposed a new cellular automata (CA) approach based on BSVC for multi-temporal urban sprawl simulation. With case studies in Hefei and Qingdao of China, we calibrated and validated two CA models, i.e. CA(BSVC)and CA(GWR), to compare their performance in simulating urban sprawl from 2008 to 2018. Our results demonstrate that CA(BSVC)outperformed CA(GWR)in terms of FOM by similar to 2.1% in Hefei and similar to 3.6% in Qingdao during the calibration stage, and showed more accuracy improvement during the validation stage. The CA(BSVC)model simulated urban sprawl more accurately than the CA(GWR)model in regions having similar proximity to the existing built-up areas, especially in less developed regions. We applied CA(BSVC)to predict urban sprawl at Hefei and Qingdao out to 2028, and the urban scenarios suggest that the proposed model shows better performance and reduced bias in reproducing urban sprawl patterns, and extends urban simulation methods by accounting for spatial nonstationarity.