Identifying the Nonlinear Impact Mechanisms of Urban Park Vitality: A Case Study of Changsha

Urban parks play an increasingly important role in supporting social interaction, ecological services, and everyday well-being in rapidly urbanizing cities, yet prevailing planning practices still rely on equal-provision logics and linear modeling frameworks, implicitly assuming that park vitality increases proportionally with facilities and surrounding services. Such assumptions overlook the possibility that park vitality responds to built-environment factors in nonlinear, threshold-based, and configuration-dependent ways. This study develops an interpretable machine learning approach to identify the nonlinear effects and structural configurations that drive urban park vitality in Changsha, China. We integrate Baidu Huiyan population heat data with AOI-defined park boundaries and multi-source POI indicators to characterize internal facilities and surrounding built-environments for 147 parks in the city’s main urban area. An XGBoost model is trained to predict park vitality, and SHAP values, partial dependence analysis, and bivariate interaction plots are employed to examine variable importance, threshold behaviors, and synergistic or substitutive relationships among key factors. The results show that sports and leisure facilities are the most influential driver of vitality, followed by shopping services and government service facilities. Their impacts are strongly nonlinear: sports and leisure facilities and public amenities display clear saturation thresholds, while high-density shopping services generate substantial gains in vitality only beyond specific concentration levels. Interaction effects further indicate that park vitality emerges from particular configurations of internal facilities and surrounding residential and service environments, rather than from the additive accumulation of isolated factors. These findings demonstrate the value of interpretable machine learning for shifting urban park planning from equal-provision paradigms toward structurally informed configuration strategies and more efficient public space governance.