Spatiotemporal distribution and driving factors of regional green spaces during rapid urbanization in Nanjing metropolitan area, China

• Published in: Ecological indicators 2023-04, Vol.148, p.110058, Article 110058
• Main Authors: Liu, Wei, Li, Huanxin, Xu, Hao, Zhang, Xiaotong, Xie, Yinchao
• Format: Article
• Language: English
• Subjects: Driving factor; Geographically weighted regression; Nanjing metropolitan area; Regional green spaces; Spatiotemporal distribution
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2023.110058

[Display omitted] •The regional green space in the Nanjing metropolitan area decreased from 2000 to 2020.•Precipitation, slope, and distance from built-up area strongly effect distribution.•Influence of socioeconomic factors shows a more flexible spatial heterogeneity.•Correlation analysis, OLS, and GWR combined can better reveal driver heterogeneity. In China, regional green spaces (RGSs) are green spaces outside urban built-up areas and have a wide distribution, large scale, and outstanding ecological function. RGSs have become increasingly important as urban clusters expand and increase in number. However, studies on RGSs are rare and it remains unclear what drives their distribution in the context of rapid urbanization. This study used integrated approaches to explore the distribution and driving factors of RGSs in the Nanjing metropolitan area (NMA) between 2000 and 2020, using Landsat image data. Spatiotemporal variations in the distribution of RGSs were obtained using the net change rate index and standard deviational ellipse. The driving factors were identified using Pearson correlation, ordinary least squares (OLS), and geographically weighted regression (GWR). We found that: (1) More RGSs are in the south of the NMA, and less in the north region. A “V” pattern of RGSs area change was observed, with substantial losses shifting from Yangtze River coastline to hilly and mountainous regions from 2000 to 2020. (2) The distribution of RGSs was affected by a combination of physical geographic, socioeconomic, and policy management factors. The functions of these influencing factors have pronounced spatiotemporal heterogeneity in direction or magnitude. Physical geographic factors including slope and annual precipitation exhibited the strongest correlation with RGSs distribution, and the regression coefficients showed relatively stable spatial performance. Among the socioeconomic factors, the distance from built-up areas and GDP played an important role. Policy management has played a guiding role, and the positive influence generated by dedicated financial expenditure and statutory green space area tends to increase and maintain a balance. The results of this study can help further understand the spatial distribution of RGSs and provide theoretical support for green space construction and ecological management of metropolitan areas during rapid urbanization.