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.

Urban noise complaints reflect not only perceived acoustic disturbance, but also complaint behaviour shaped by thermal conditions and the built environment. Using Sanya, China, as a case study, this study integrated Landsat-derived land surface temperature data with the spatial distribution of noise complaints to examine how thermal environment and urban contextual factors jointly influence complaint patterns. An interpretable modeling framework combining eXtreme Gradient Boosting (XGBoost) and Multiscale Geographically Weighted Regression (MGWR) was employed to assess the associations of urban heat island intensity (UHI), road density, point of interest (POI) count, and population density on complaint occurrence and intensity, while also generating spatial predictions of complaint distribution. The results revealed a weak but statistically significant spatial association between the thermal environment and noise complaints, with urban heat island intensity showing nonlinear and spatially heterogeneous associations with complaint counts. POI count emerged as the strongest global predictor, while road density, population density, and thermal environment exhibited substantial spatial heterogeneity in their associations with complaint patterns. The integrated model outperformed both individual models alone, achieving the highest prediction accuracy. Overall, the findings suggest that urban noise complaint patterns reflect not only perceived acoustic disturbance, but also context-dependent social perception and reporting behaviour, providing empirical support for more place-sensitive and people-centered urban noise governance.

Urban heat mitigation in megacities depends not only on cooling sources, but also on the connectivity through which cooling effects are transmitted across heterogeneous landscapes. However, existing studies have mainly focused on the static patterns of urban cold islands (UCIs), while the connectivity and disturbance response of urban cooling systems remain poorly understood. Taking Landsat-based summer thermal observations in Beijing, this study developed an integrated framework to assess the structure and resilience of the urban cold island network (CIN) by combining thermal source identification, resistance-surface construction, connectivity modeling, and disturbance simulations. Land surface temperature (LST) was extracted from Landsat 8 OLI/TIRS Collection 2 Level-2 surface temperature products acquired in July–August 2022, and cold island core sources (CICS) were subsequently identified by integrating thermal conditions with land-use characteristics. GeoDetector was used to quantify the explanatory power and interaction effects of natural, land-use, and socio-economic factors on LST spatial heterogeneity, serving as an attribution tool for interpreting thermal-environment drivers. These factors were then integrated into a resistance surface for circuit-theory-based connectivity analysis. Under the summer heat-stress scenario, 202 CICS covering 6416.95 km² were identified, mainly concentrated in peripheral mountainous areas. A total of 401 corridors were identified, including 70 primary corridors forming the structural backbone of the CIN. This spatial distribution reveals a mountain–plain cooling structure in Beijing, in which mountainous CICS constitute the regional cooling-supply base, while potential cooling transmission toward the urban core mainly depends on a limited number of backbone corridors. LULC was the dominant driver of LST, and its interactions with PD, NTL, and vegetation-related factors substantially enhanced explanatory power. Compared with random disturbance, targeted node removal led to an earlier and sharper decline in network resilience, with substantial deterioration already evident after approximately 20–30% of critical nodes were removed. These summer-based findings provide spatially explicit evidence for prioritizing cooling corridors, critical nodes, and restoration areas in connectivity-oriented urban heat mitigation and climate-responsive planning, thereby supporting hierarchical maintenance and restoration strategies based on their relative importance within the cooling network.