Community park visits determined by the interactions between built environment attributes: An explainable machine learning method

Uncovering the association between built environment (BE) attributes and community park visits by considering potential nonlinear effects can inform more effective spatial policies. This study utilizes real-time population visitation big data to depict the spatial variances in community park visits in the case city of Shenzhen. An explainable machine learning method incorporating random forest and Shapley Additive exPlanations (SHAP) is applied to reveal the relative importance of BE attributes and to examine the nonlinear associations and interaction effects on park visits. The results confirm the decisive roles of park size and walking-based street connectivity on associating with visits, with threshold points at 2 hm²for park size and 0.3 for network warp. The revealed interaction between park size and surrounding BE attributes benefits defining the optimal scale by considering surrounding attributes of both attraction and demand factors. Based on the findings, we further discuss the possible patterns of threshold effects and interaction effects rooted in the examined nonlinearity. The findings guide policy makers in adopting smarter and more effective strategies to improve community park visits.