Investigating the Influence Patterns of the Built Environment on Residents’ Self-Rated Health: An Interpretable Machine Learning Approach

With the acceleration of urbanization, the impact of built community environments on residents’ health has emerged as a research focus in urban geography and public health. This study examines 25 representative communities in Wuhan, China, employing a combination of questionnaire surveys and multi-source geospatial data. It systematically analyzes the influence patterns of built environment characteristics on residents’ self-rated health from dual perspectives: subjective perception and objective measurement. The XGBoost model was employed to achieve nonlinear fitting and prediction of residents’ self-rated health, while the SHAP method was introduced to interpret model outputs, identifying key environmental factors and their complex effect patterns. The results show that the built environment and health exhibit significant nonlinear relationships, with XGBoost outperforming other models. Residents’ health perception is jointly influenced by subjective and objective factors, with satisfaction with commercial services contributing most. Key environmental elements display threshold effects, indicating that excessive mixing may not further improve health. Furthermore, complex local interactions exist, where good transport accessibility enhances the health benefits of medical facilities and green spaces. This study demonstrates the applicability of interpretable machine learning in health geography, thus providing scientific guidance for health-oriented community planning.