Abstract
Using the Suzhou-Wuxi-Changzhou (referred to as Su-Xi-Chang) region as a case study, this work applied an Exploratory Spatial Data Analysis model to study the characteristics associated with the evolution in the urban spatial patterns in the region from 2002 to 2018. A geographical weighted regression model and Local indicator of spatial association Index are used to analyze the degrees of influence that different driving factors have on urban spatial patterns in the Su-Xi-Chang region. Two major points emerged from the results. First, the urban development of the Su-Xi-Chang metropolitan area has a relatively concentrated spatial distribution. When considering the local spatial correlation, there is a relatively large proportion of areas with H–H correlation and L–H correlation. The H–H correlation area is mainly concentrated in the central urban area of Suzhou and Wuxi, and Kunshan, which connects Suzhou and Shanghai. This forms a spatial concentration area with high urban development levels. The L–H correlation area is mainly concentrated in cities such as Yixing and Changshu. After the central city developed to a certain stage in 2010, the spatial agglomeration of small and medium-sized cities that lagged in size became more clear. The L–L agglomeration area is mainly concentrated in Liyang and Jintan, with a widening development gap from surrounding cities and counties. This has led to a development trend of marginalization. Second, the urbanization rate index had a weak driving effect on the evolution and development of urban spatial pattern.
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Funding
This paper is supported by Humanity and Social Science Youth foundation of Ministry of Education of the People's Republic of China: “Research on the Influence on the urban pattern of coordinated development from Administrative Division Adjustment—Case Studies of the Suzhou, Wuxi & Changzhou area in China” (Funding No. 18YJC840038).
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Wang, X., Zhou, Y. Study on changes of urban spatial pattern and heterogeneity of driving factors in the Su-Xi-Chang region. Environ Ecol Stat 29, 339–357 (2022). https://doi.org/10.1007/s10651-021-00523-2
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DOI: https://doi.org/10.1007/s10651-021-00523-2