Urban Green Space Assessment: Spatial Clustering Method Based on Multisource Data to Facilitate Zoning Planning

Abstract

Urban green spaces (UGSs) have potential significance for urban ecosystems, as they provide environmental benefits and benefits for residents' physical activity and mental health. Objective assessments of UGSs are necessary for optimizing the allocation of green space public resources and providing a basis for social equality. Previous studies have adopted a few indicators to evaluate UGS provision but have neglected multidimensional interaction characteristics. This study proposes a framework that integrates principal component analysis and the spatial clustering method to identify geographically homogeneous subregions with similar characteristics, including dimensions of accessibility, ecological function, quality, and quantity. Shenzhen, China, was selected as a case study, and the results indicate the following: (1) the multiple dimensions of UGSs are unevenly distributed, and it is necessary to evaluate UGSs from multiple dimensions; and (2) there are significant disparities in UGSs among different clusters that can be summarized to guide the improvement of green space equity. It is necessary to increase the number of community parks and optimize the green view index in more densely populated areas and the outskirts. Our study provides an intuitive and comprehensive evaluation framework for the distribution of UGSs and identifies their characteristics in different subregions, which is valuable for space quality improvement and reasonable zoning planning.