Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models

In many of the conventional cellular automata (CA) models, particularly Urban-CA which are used for urban growth, the spatial heterogeneities and local differences of the land use conversion processes are ignored. Global logistic regression (LR) is a popular model employed to define the transition rules of Urban-CA. By considering the local characteristics, Geographically Weighted Logistic Regression (GWLR) provides interesting capabilities for urban growth modelling. In this research, in addition to using GWLR in the definition of transition rules, the advantages of integrating GWLR and LR for urban growth simulation were evaluated; these have not been considered in previous studies. Local and global probabilities obtained from the calibration of GWLR and LR were combined to define the transition rules of an Urban-CA. Urban growth was simulated in the Islamshahr sub-region located southwest of Tehran, Iran for the two periods 1992-1996 and 1996-2002, and data from these periods were used for training and testing the prediction abilities, respectively. In the first period, GWLR showed good performance and a significant contribution to the enhancement of the simulation performance, but in the second period, the effectiveness of LR on the prediction accuracy increased. Due to their complementary roles, the integration of the GWLR and LR models resulted in improved simulation performance in both periods.