A cellular automata approach of urban sprawl simulation with Bayesian spatially-varying transformation rules. Issue 7 (2nd October 2020)

• Record Type: Journal Article
• Authors: Chen, Shurui; Feng, Yongjiu; Ye, Zhen; Tong, Xiaohua; Wang, Rong; Zhai, Shuting; Gao, Chen; Lei, Zhenkun; Jin, Yanmin
• Is Part Of: GIScience & remote sensing. Volume 57:Issue 7(2020)
• Page Start: 924
• Page End: 942
• Publication Date: 2020-10-02
• Subjects: Urban sprawl modeling -- cellular automata -- spatially-varying coefficient -- spatial nonstationarity -- figure-of-merit (FOM); Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2020.1829376
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: 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. CABSVC and CAGWR, to compare their performance in simulating urban sprawl from 2008 to 2018. Our results demonstrate that CABSVC outperformed CAGWR in terms of FOM by ~2.1% in Hefei and ~3.6% in Qingdao during the calibration stage, and showed more accuracy improvement during the validation stage. The CABSVC model simulated urban sprawl more accurately than the CAGWR model in regions having similar proximity to the existing built-up areas, especially in less developed regions. We applied CABSVC 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.