Image-based streetscape assessments commonly report separate perception scores or an aggregate index. Joint profiles additionally identify which unfavorable scores occur together. We examine joint lower-tail profiles of transferred safety, beauty, and liveliness estimates in 2304 grid cells represented by 48,011 street-view records from central urban Shanghai. A Perceptual Streetscape Assessment Chain separates visible content, estimated impressions, joint shortfalls, and contextual distribution. The Spatial Multidimensional Tail-Equity (SMTE) model represents eight profiles jointly and derives compound and dimension-specific probabilities with multilevel and spatial structure. At the primary lower-quartile threshold, 24.44% of cells had at least two low scores. Strong empirical overlap between safety and beauty made domain construction central to the interpretation of this classification. In this specification, the clearest conditional price-rank difference concerned low liveliness; its interval-based support varied across alternative thresholds. Mathematical exchanges toward greenery were associated with lower fitted compound risk under the specified composition model. The contribution is a profile-based screening approach that makes dependence and identification choices explicit. Its findings concern relative model-estimated visual conditions and housing-price contexts within the sampled central urban area, with resident ratings and household circumstances defining further targets for locally grounded evaluation.

Perceived street vitality directly reflects residents’ assessments of the attractiveness of the street environment; it is not only an important focus of urban vitality research but also closely related to human-centred sustainable urban development. However, limited data availability and the complexity of urban environments have constrained fine-grained spatial analysis at the city scale. To address this issue, this study quantified perceived street vitality by collecting street-view imagery, extracting streetscape features, and integrating these data with questionnaire survey results. After comparing multiple models, a geographically weighted machine learning model was employed to identify key visual predictors, model-estimated marginal associations, interaction patterns, and spatial heterogeneity related to perceived street vitality. The results show that areas with high perceived street vitality are mainly located along street segments with abundant greenery and open spaces, whereas low-value areas are concentrated in densely built and enclosed environments. Among the various streetscape elements, buildings, vegetation, and sky are the key visual elements most strongly associated with perceived street vitality. A model incorporating these elements accounted for 67.2% of the variance in perceived street vitality. Notably, the strength of these associations varied significantly across different areas. This study provides empirical evidence and evidence-based support for sustainable urban renewal, the optimisation of street-space layouts in high-density urban areas, and the improvement in street environmental quality.

Extreme heat increasingly threatens public health and urban sustainability, creating a need for fine-scale assessments that connect spatial risk patterns with climate-resilient planning. This study assessed heat risk across 1639 blocks in Tianjin’s six central districts, China. A block-scale heat-risk index (HRI) was constructed within the Hazard–Exposure–Vulnerability–Adaptability (HEVA) framework by integrating remote-sensing, population, built-environment, socioeconomic, and public-service data using a modified CRITIC weighting method. Differences in HRI among Local Climate Zones (LCZs) were examined, and three machine-learning models were compared. Among the three evaluated models, Random Forest showed the best overall test-set performance, and supplementary five-fold spatial block validation retained the same model ranking and major feature-importance ordering despite lower absolute predictive performance. Feature importance, partial dependence plots, and SHapley Additive exPlanations were subsequently used to interpret feature contributions and nonlinear associations. HRI was lower in the resource-rich urban core and higher in peripheral transitional zones, accompanied by clear spatial differences in adaptive-resource provision. The urban core generally exhibited higher adaptive-resource provision, whereas peripheral areas showed more limited provision. Under the adopted HRI formulation, higher values of this component contributed mathematically to lower composite HRI, rather than demonstrating a realised risk-reduction effect. Built LCZs generally exhibited higher HRI than land-cover LCZs; LCZ 5 (open mid-rise) had the highest median HRI, whereas LCZ G (water) had the lowest. Higher sky view factor and vegetation cover were generally associated with lower HRI, while larger blocks, taller buildings, and greater distances from water bodies were associated with higher HRI. These findings support differentiated block-scale interventions, heat-response resource allocation, and climate-resilient urban regeneration.

Rapid urbanization and intensifying thermal stress pose challenges to urban livability, climate resilience, and sustainable development. However, studies overlook the seasonal nonlinearity and spatial non-stationarity of urban thermal comfort and its driving factors, constraining seasonally differentiated, climate-adaptive planning strategies. Taking central Tianjin, China, as a case study, this research integrates a geographically weighted XGBoost model with shapley additive explanations to disentangle the seasonal and spatial differentiation, nonlinear relationships, and conditional interaction patterns of the Universal Thermal Climate Index (UTCI). Results indicate: (1) UTCI exhibits seasonal differences in spatial thermal gradients, with relatively high values concentrated in central and northern-central areas; (2) driver contributions shift across seasons, with evapotranspiration leading in summer, digital elevation model in autumn, and building density in winter, while landscape composition contributes most strongly in winter; (3) candidate nonlinear breakpoints include winter sky view factor (0.683; 95% interval 0.662–0.752) and water patch density (15.6 patches/100 ha; 13–17.8); (4) conditional interactions vary seasonally, involving environmental–terrain combinations in summer and building-morphology–landscape combinations in winter. These findings reveal season-specific thermal comfort associations, providing quantitative evidence for translating nonlinear relationships and spatial heterogeneity into climate-adaptive urban design. This supports targeted thermal-environment interventions for urban resilience and sustainability.