Journal of Resources and Ecology ›› 2017, Vol. 8 ›› Issue (3): 277-286.DOI: 10.5814/j.issn.1674-764x.2017.03.008

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Urban Expansion and Spatiotemporal Relationships with Driving Factors Revealed by Geographically Weighted Logistic Regression

DONG Guanglong1,2, XU Erqi1, ZHANG Hongqi1,*   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-02-17 Revised:2017-04-10 Online:2017-05-20 Published:2017-05-20
  • Contact: ZHANG Hongqi, E-mail: zhanghq@igsnrr.ac.cn
  • Supported by:
    Major consulting project of the Chinese academy of engineering (201405ZD001).

Abstract: Urbanization improves our lives but also threatens human health and sustainable development. Revealing the spatiotemporal pattern of urban expansion and spatiotemporal relationships with driving forces, especially in terms of the ubiquitous and fast growing small city, is a crucial prerequisite to solving these problems and realizing sustainable development. Kunshan, China was used as a case study here. Eleven variables from four aspects covering physical, socioeconomic, accessibility and neighborhood were selected, and logistic regression and geographically weighted logistic regression modeling were employed to explore spatiotemporal relationships from 1991-2014. Results reveal that urban expansion in Kunshan shows an accelerating tendency with annual expansion from 2000-2014 four times higher than for 1991-2000. More importantly, the annual expansion rate of Kunshan of 28.42% in 2000-2014 is higher than that of a large city. Urban expansion and related factors have spatiotemporal varying relationships. From a global perspective, the closer to a city, town, main road and the higher the GDP, the more likely a region will undergo urbanization. Interestingly, the effect of population on urban expansion is decreasing, especially in developed areas, and the effect of distance to lake is enhanced. From a local perspective, the magnitude and even the sign of the coefficients vary across the study area. However, the range of the coefficient of GWLR is around that of the corresponding variable in LR, and the sign of most variables in GWLR is consistent with that of corresponding variables in LR. GWLR surpasses LR with the same explanatory variables in revealing regional differences and improving model reliability. Based on these findings, more attention should be given to small cities in China. Promoting the connotation of city culture and public services to realize New-type Urbanization and regional diversity policy in order to manage urban expansion scientifically are also recommended.

Key words: China, geographically weighted logistic regression, spatial heterogeneity, spatiotemporal variation, urban expansion