A spatial error-based cellular automata approach to reproducing and projecting dynamic urban expansion

Abstract

Urban systems are featured by spatial autocorrelation, which may produce clustering of model residuals when simulating urban expansion using cellular automata (CA). Accurate identification of spatial autocorrelation and reduction of residual clustering are essential to accurate CA modeling of urban expansion. We developed a new CA approach (CA_SEM) using a spatial error model (SEM) that incorporates spatial autocorrelation. Using Zhengzhou City as a case study, we calibrated three types of CA models [e.g., logistic regression (Logit), spatial lag model (SLM) and SEM] from 2000 to 2010. Here, two important issues are the choice of the appropriate method (SLM vs. SEM) for urban expansion modeling and the applicability of CA_SEM for projecting urban scenarios. We validated the CA_SEM model from 2010 to 2017 and projected urban scenarios out to the year 2030 using this model. End-state assessment reveals that CA_SEM yields a higher overall accuracy (91.4%) in the calibration, but lower overall accuracy (83.8%) in the validation. For change assessment, CA_SEM yields a lower figure-of-merit (FOM; 31.8%) in the calibration but a higher FOM (35.2%) in the validation. We conclude that CA_SEM can accurately simulate urban expansion at Zhengzhou considering the fit performance of urban land transition rules, and the accuracy assessment of urban patterns and expansion. Scenario prediction using CA_SEM is therefore valuable for formulating useful urban planning regulations and in supporting sustainable urban development.

Keywords:

• Urban expansion
• cellular automata
• spatial autocorrelation
• spatial error model (SEM)
• model assessment
• Zhengzhou city

Disclosure statement

No potential conflict of interest was reported by the author(s).