![]() ![]() The spatial allocation accuracy and kappa values of NE-autologistic- CLUE-S were higher than those of logistic- CLUE-S, autologistic- CLUE-S, and NE-logistic- CLUE-S for the simulations of two periods, 09, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. The Zengcheng District of Guangzhou, China was selected as the study area. ![]() Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. The Conversion of Land Use and its Effects at Small regional extent ( CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China ![]()
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