Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration

Rui Xiao, Xiaoyu Yu, Zhonghao Zhang and Xue Wang

Growth and Change, 2021, vol. 52, issue 3, 1804-1825

Abstract: Simulating and predicting the urban land use change can provide deeper spatial insights into dynamics and sustainable developments of urban planning. This research takes the Shanghai‐Hangzhou Bay (SHB) agglomeration as a study area and selects natural, economic, social, and policy variables as restraint conditions. A cellular automaton (CA) model and the naive Bayes‐cellular automaton (NB‐CA) model are employed and compared to simulate the built‐up land in SHB. Results show that the NB‐CA model greatly improves the simulation accuracy of built‐up land compared to CA model. Specifically, the simulation accuracy of the NB‐CA model is 14.68%, 14.03%, 7.43%, 6.00%, 5.32%, and 2.65% higher than that of the traditional CA model in Shanghai, Hangzhou, Huzhou, Jiaxing, Ningbo, and Shaoxing, respectively. Among the four restraint conditions, the least influential variable is the natural variable and the most influential is the policy variable in Shanghai, Ningbo, and Shaoxing and the social variable in Hangzhou, Huzhou, and Jiaxing. It is the first usage of naive Bayes and CA to simulate built‐up expansion and this new combination method highlights the improvement of simulation accuracy. The naive Bayes technology implies that government policy is an unstable factor that can influence the simulation of built‐up land change. The methodology will be applicable to other regions experiencing rapid built‐up land expansion under government policy.

Date: 2021
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