From Perception to Behavior: Exploring the Impact Mechanism of Street Built Environment on Mobile Physical Activity Using Multi-Source Data and Explainable Machine Learning

This study explores the mechanisms through which the street built environment (BE) influences mobile physical activity (MPA) using multi-source data and explainable machine learning methods. The research combines Geographically Weighted Regression (GWR) and Random Forest (RF) models to reveal the complex spatial heterogeneity between BE factors and MPA, and enhances the interpretability of results through the SHAP model, providing theoretical support for future targeted urban planning and MPA interventions. The study finds that the “density” dimension of BE plays a crucial role in MPA, particularly population density and building density. Additionally, accessibility and safety also significantly influence MPA, while design factors such as greening rates, water landscapes, and building façade design promote MPA. The study emphasizes that the influence of BE factors on MPA is nonlinear, with significant interaction effects between different variables, indicating that improving a single variable alone cannot fully explain changes in MPA. This research provides a new theoretical perspective for understanding the impact of BE factors on MPA and offers empirical evidence for precise interventions. In areas with low MPA participation, improving street design, enhancing traffic safety, and increasing green and water-friendly spaces can significantly promote residents’ MPA, thereby improving public health.