What question did this study set out to answer?

This research aims to assess urban heat risk by examining the influence of urban morphological characteristics using machine learning.

February 12, 2026Open Access

Influence of Urban Morphological Characteristics on Street-Level Urban Heat Risk: A Geographically Weighted Machine Learning Approach

Puntos clave

This research aims to assess urban heat risk by examining the influence of urban morphological characteristics using machine learning.
Applied the Hazard–Exposure–Vulnerability–Adaptation (HEVA) framework.
Integrated remote sensing, road network, and socio-demographic data.
Developed a heat risk index using the CRITIC weighting method.
Employed geographically weighted machine learning models to identify drivers and spatial relationships.
Assessed model reliability through three-fold cross-validation.
GW-XGBoost model achieved R2 = 0.672 and MAE = 0.142 for prediction accuracy.
Elevated heat risk found in peripheral transitional zones rather than just urban cores.
Greater development intensity correlates with higher heat risk, while increased vegetation cover reduces risk.
Vulnerable populations identified in areas with high heat stress and low resource access.

Resumen

As extreme heat events become increasingly frequent worldwide, there is an urgent need for fine-scale assessment of urban heat risk and for identifying its key determinants. Conventional approaches often struggle to capture complex intra-urban spatial heterogeneity, limiting effective heat risk governance and resource allocation. This study applies the Hazard–Exposure–Vulnerability–Adaptation (HEVA) framework by integrating remote sensing, road network, and socio-demographic data. Using the CRITIC weighting method, we quantify and map a street-level heat risk index (HRI) in Tianjin, China. We further employ geographically weighted machine learning models to identify dominant drivers and to characterise nonlinear effects, interaction patterns, and spatially varying relationships. Model reliability is assessed by benchmarking geographically weighted models against global nonlinear baselines under three-fold cross-validation; GW-XGBoost achieves comparable explanatory power to the best global model (R2 = 0.672) while yielding lower prediction errors (MAE = 0.142), supporting robust spatial inference. Results show that elevated heat risk is not confined to the urban core; instead, it is more pronounced in peripheral transitional zones around central districts. These areas often exhibit coincident heat stress and high population exposure, a higher concentration of vulnerable groups and ageing residential neighbourhoods, and comparatively limited access to medical and cooling resources. Mechanistically, greater development intensity is generally associated with higher heat risk, whereas higher vegetation cover tends to reduce risk; however, the strength and, in some locations, the direction of these effects vary substantially across streets. These findings suggest that heat risk management should prioritise peripheral transitional zones. Targeted interventions should balance development intensity, expand effective greening and shading, and improve the provision and accessibility of healthcare and cooling services to reduce street-level heat risk.

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