Modeling urban growth by cellular automata: A case study of Xiamen City, China

Abstract

This paper focus on the modeling of urban growth with regional difference. Firstly, two land use change map of Xiamen city in 2001 and 2007 were acquired by classification based on satellites images. Secondly, nine kinds of driving factors, which were derived from points of interest (POI) and DEM, were also selected by using distance analysis of GIS. Those factors include public services, economic, political and geographical aspects. Basing on these data, this study adopts logistic regression (LR) model to analysis the urban transition and effects contributed by driving factors. The overall accuracy rate of LR model is up to 81.9%-85.9% and the ROC is 0.896, indicating that it is capable to quantitative analysis the mechanism of different driving factors and the spatial-temporal land use change. Finally, a constrained CA model is applied to simulate and predict the future land use situation of Xiamen in 2020. The simulation results reveal that the increasing areas of construction are mainly located outside of the Xiamen Island. The overall land supply and demand are in contradiction obviously, which may lead to increasing pressure on farmland protection. In general, land use issues would become the main bottlenecks of the development of economic and society in Xiamen city. The prediction results can provide reliable guidance about policy implementation for land use planning department.