Revealing the Nonlinear Associations and Spatial Heterogeneity of Urban Environmental Indicators in Emotional Perception: A Machine Learning Perspective from Shanghai

Streets are major public spaces in high-density cities, and their visual environments are closely related to shaping emotional experience and wellbeing. However, existing studies often examine macro-scale urban form and pedestrian-level streetscape perception separately, while paying limited attention to nonlinear relationships and spatial heterogeneity. This limits the evidence available for fine-grained urban renewal in high-density contexts. Focusing on the area within Shanghai’s Outer Ring, this study develops a large-scale street-view dataset of 512,764 Baidu Street View images. Six perceptual dimensions—safety, lively, beautiful, wealthy, boring, and depressing—are estimated using a perception model trained on Place Pulse 2.0 and integrated into a composite Psychological and Emotional Index (PEI). XGBoost–SHAP is used to examine nonlinear relationships and threshold effects between perceptions and environmental indicators, while MGWR is employed to capture spatial nonstationarity and scale-dependent effects. The results show significant spatial heterogeneity and positive spatial autocorrelation across the six perceptual dimensions and the PEI. Compared with traditional morphological indicators, visual features showed stronger explanatory power and clearer threshold effects. Population density acts as a globally stable negative factor, whereas visual entropy and mixture show strong local sensitivity. These findings provide a data-driven basis for identifying context-specific priorities in urban renewal and spatial governance in high-density cities.