Demand-Led Optimization of Urban Park Services

As the demand for cultural and recreational services grows, the mismatch between the supply and demand of park services significantly affects residents’ well-being. Optimizing the spatial layout of park services is a focal point of urban park and green space research. Taking Hangzhou, Zhejiang Province, as a case study, this research analyzes the spatial patterns and balance of park service supply and demand. Utilizing the Grey Wolf Optimization Model optimized by the K-Nearest Neighbor Model (GWO-KNN), this study proposes construction objectives for optimizing park services. The results indicate the following: (1) significant differences exist in the park service demands of residents in different residential environments; (2) there is a noticeable spatial disparity in park service supply among various residential areas with an overall positive correlation between park service supply levels and resident demands, yet an imbalance exists; (3) this study categorizes spatial types into low-service coordination, high-service coordination, low-service imbalance, and high-service imbalance; (4) the GWO-KNN Model is applied with optimization objectives being the innovative aspect of this study. Strategies for each park category are proposed: emphasizing suburban park construction by utilizing surrounding green resources and adding diverse facilities; introducing facilities friendly to vulnerable groups to meet the needs of diverse populations; enhancing the complementary advantages between “new” and “old” cities by moderately increasing park sizes and improving cultural and facility development levels; optimizing spatial structure with limited land resources to construct an urban park network system. This study aims to provide theoretical and technical support for optimizing urban park and green space systems.