Abstract:

In order to solve the problem of unbalanced supply and demand of park green space in high density cities, this study used a Gaussian based two-step floating catchment area method to analyze the spatial accessibility of existing park green space in Huangpu District of Shanghai, under walking conditions. First, we extracted the point of interest (POI) data of residential areas from the Internet and aggregated them into a 100 m long hexagonal cellular network to calculate the number of people and population density, then through the Application Programming Interface (API) of Baidu Map we extracted the travel time between supply and demand points based on actual road conditions. Second, the paper took the park green space built by the end of 2018 in Huangpu District of Shanghai as an example, and considered the four aspects of residents' demand, accessibility classification, cold and hot spots, and blank areas. The results show that the accessibility level of park green space in Huangpu District is higher in the north and lower in the south, which is negatively correlated with the population density distribution. The dominant blind areas (9.16% of the total area) and the recessive blind areas (16.5% of the total area) should be eliminated. This study made a quantitative analysis on the supply and demand allocation, to provide guidance for the further optimization of green space layout and site selection. Finally, recommendations were made from three aspects of "increasing green space appropriately", "strengthening the quality of existing green space" and "community co-governance", in order to provide theoretical support for the Fourteenth Five-Year Plan of greening Huangpu District.

Key words: park accessibility, Gaussian based two-step floating catchment area (2SFCA) method, fine-grained population data, big data, Huangpu District of Shanghai