Optimizing the spatial accessibility of outdoor sports facilities: a greedy heuristic algorithm based on remote sensing images

Recent studies on the accessibility of sports facilities have rarely considered the specific attributes of the facilities, limiting their ability to define service potential, and have often neglected the critical aspect of equitable access. This study proposed a novel approach based on remote sensing images to optimize the spatial accessibility of outdoor sports facilities. Using Shanghai, China, as the study area, the study identified four types of sports facilities using a deep learning object detection method, which allowed their service capacities (areas) to be measured more precisely. A greedy heuristic algorithm was then developed based on a "trade-off" strategy that seeks to optimize facility access by reconciling the objectives of enhancing access and ensuring equality and by weighing the benefits of utilizing existing resources (school facilities) against the necessity of developing new ones. The object detection method achieved precision and recall rates of 88% and 96%, respectively, and the optimization efforts resulted in a 73% increase in accessibility while also significantly reducing the Gini coefficient from 0.58 to 0.34. The proposed algorithm outperformed the random selection and all-school-opening strategies. The results indicated that the methodology can effectively create refined datasets for outdoor sports facilities and enhance their accessibility.