How Do Street Physical Environments Shape Pedestrian Safety Perception? Evidence from Street-View Imagery, Machine Learning, and Multiscale Geographically Weighted Regression

In high-density urban cores, pedestrian safety perception is shaped not only by street physical environments but also by pronounced spatial heterogeneity. However, existing studies often rely on global regression or small-sample surveys, making it difficult to simultaneously reveal city-scale regularities and localized mechanisms. Taking Futian District, Shenzhen, as a case study, this study develops an integrated analytical framework that combines street-view imagery, machine learning, and multiscale geographically weighted regression (MGWR) to measure pedestrian safety perception at the city scale and to unpack its spatial mechanisms. The results show that model explanatory power improves markedly after accounting for spatial non-stationarity, indicating strong context dependence in the formation of pedestrian safety perception. MGWR further reveals clear multiscale differentiation across streetscape visual elements: greenery-related elements (e.g., tree and plant) exhibit near-global and consistently positive effects, whereas traffic exposure and interface-related elements (e.g., car, road, and wall) operate more locally, with both the direction and magnitude of their effects varying substantially with neighborhood structure and traffic contexts. These findings suggest that the impacts of individual street elements on pedestrian safety perception are not universally transferable and should be interpreted within a spatial-scale and contextual framework. By integrating machine learning-based prediction with MGWR-based spatial interpretation, this study enables both efficient city-scale measurement and multiscale mechanism identification of pedestrian safety perception, providing empirical support for safety perception-oriented street planning and fine-grained urban design.