Spatial Accessibility and Supply-demand Balance Analysis Method of Park Green Space based on Modified Gravity Model

Exploring the effectiveness of the spatial allocation of park green space resources and the balance of supply and demand is of great significance for urban planning and residents' life in high density cities. Previous studies have rarely explored the accessibility of park green space from the perspectives of multi-scale, multi-traffic travel modes, and combination with big data algorithms. Therefore, the paper proposed an improved gravity model that combines multi-type park green space and multi-level travel time thresholds based on the multi-traffic travel data obtained from Baidu Map, and analyzed the spatial accessibility of park green space and the characteristics of the balance between supply and demand in Yuexiu District of Guangzhou City. The results show that: (1) There are significant differences in the accessibility of multi-scale and multi-type parks in Yuexiu District. The district scale accessibility generally shows a decreasing trend from north to south, and the subdistrict scale accessibility shows obvious "polarization" phenomenon. The accessibility of large parks is much higher than that of community parks and street gardens, which occupies a dominant position in the overall accessibility of the city; (2) In terms of spatial aggregation, the accessibility of district scale park green space shows obvious spatial aggregation and stratification, with high-value clustering, insignificant, low-value clustering from the north to south. Compared to the district scale, the subdistrict scale has fewer high-value clusters, and low-value clusters and non-statistically significant areas have increased; (3) The spatial accessibility results based on the perspective of supply and demand balance show that there is overall balance and local imbalance between the supply of park green space and the demand of residents in Yuexiu District. The local imbalance is mainly concentrated in areas with dense residential areas and relatively sparse park green space resources. © 2022, Science Press. All right reserved.