Modeling self-organized urban growth by incorporating stakeholders' interactions into the neighborhood of cellular automata

Urban modelers have long interpreted urban growth as a coupled evolution of two processes, namely spontaneous and self-organized processes. However, most scholars have always paid attention to the exploration of the driving mechanisms of the spontaneous process. While for the self-organized process, most simulations based on cellular automata (CA) simply abstract it as the clustering process between land use types, ignoring the important role of human behaviors in urban growth. This study presents a framework that integrates a size-adaptive strategy with the multi-agent system (MAS) to establish a neighborhood with stakeholders' interactions (NSI). We employed the CA with NSI (Nsi-CA) to simulate the urban growth of Wuhan from 2000 to 2020 and analyzed its performance. The findings indicate that Nsi-CA performs better than the commonly used CA models at both local and global scales, with an increase in global Figure of Merit (FoM) of about 11.5 %. Moreover, the effectiveness of parameters controlling stakeholders' interactions in the NSI was revealed through a sensitivity analysis. By setting different scenarios with these parameters, the future urban land patterns of Wuhan in 2035 were predicted and used to provide planning suggestions for its future development.