Spatial differentiation and potential influencing factors of land-surface heat exposure based on street-view imagery: a case study of Luolong District, Luoyang.
- 1School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang, Henan, China.
Abstract
Introduction
Against the backdrop of increasingly frequent extreme heat events, mitigating land-surface heat exposure on urban streets is crucial for safeguarding public health. Existing studies have often overlooked the spatially differentiated effects of complex street environments on land-surface heat exposure and the associated cooling thresholds. Therefore, this study takes Luolong District, Luoyang, as a case study to investigate the spatially heterogeneous effects and nonlinear threshold characteristics of street-view metrics on land-surface heat exposure.Methods
This study integrates Baidu Street View imagery with multi-source spatial data and employs Multiscale Geographically Weighted Regression (MGWR) and the XGBoost-SHAP model to systematically investigate the spatially heterogeneous effects and nonlinear threshold characteristics of micro-scale street-view metrics on land-surface heat exposure, and proposes differentiated optimization strategies accordingly.Results
First, the effects of street morphology on land-surface heat exposure exhibit significant spatial multi-scale differentiation. Road proportion (RDP) and sidewalk proportion (SWP) operate predominantly at the micro scale, whereas the Green View Index (GVI) and Building Coverage Ratio (BCR) exhibit predominantly meso-scale effects; in contrast, the Sky View Factor (SVF) and Enclosure (ENC) demonstrate predominantly macro-scale effects. Second, nonlinear "threshold effects" and saturation points are generally observed between individual street-view metrics and land-surface heat exposure. For example, the cooling benefits of GVI are subject to an upper limit, whereas excessively high SVF values exhibit a strong positive association with increased land-surface heat exposure. Third, the risk classification and targeted renewal strategies developed based on a "current condition-sensitivity matrix" can effectively mitigate localized land-surface heat exposure across streets with different functional characteristics.Discussion
This study provides scientific decision-making support for the fine-grained renewal of high-density urban living environments and climate-adaptive urban governance.
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BibTeX
@article{hu2026spatial,
title = {{Spatial differentiation and potential influencing factors of land-surface heat exposure based on street-view imagery: a case study of Luolong District, Luoyang.}},
author = {Junhui Hu and Xinyao Zhang and Danfeng Liu and Luyao Chen},
journal = {Frontiers in public health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1960954},
url = {https://europepmc.org/article/MED/42909079},
note = {CC BY},
}
RIS
TY - JOUR
TI - Spatial differentiation and potential influencing factors of land-surface heat exposure based on street-view imagery: a case study of Luolong District, Luoyang.
AU - Junhui Hu
AU - Xinyao Zhang
AU - Danfeng Liu
AU - Luyao Chen
PY - 2026
DA - 2026/10/07
JO - Frontiers in public health
DO - 10.3389/fpubh.2026.1960954
UR - https://europepmc.org/article/MED/42909079
AB - Introduction Against the backdrop of increasingly frequent extreme heat events, mitigating land-surface heat exposure on urban streets is crucial for safeguarding public health. Existing studies have often overlooked the spatially differentiated effects of complex street environments on land-surface heat exposure and the associated cooling thresholds. Therefore, this study takes Luolong District, Luoyang, as a case study to investigate the spatially heterogeneous effects and nonlinear threshold characteristics of street-view metrics on land-surface heat exposure.Methods This study integrates Baidu Street View imagery with multi-source spatial data and employs Multiscale Geographically Weighted Regression (MGWR) and the XGBoost-SHAP model to systematically investigate the spatially heterogeneous effects and nonlinear threshold characteristics of micro-scale street-view metrics on land-surface heat exposure, and proposes differentiated optimization strategies accordingly.Results First, the effects of street morphology on land-surface heat exposure exhibit significant spatial multi-scale differentiation. Road proportion (RDP) and sidewalk proportion (SWP) operate predominantly at the micro scale, whereas the Green View Index (GVI) and Building Coverage Ratio (BCR) exhibit predominantly meso-scale effects; in contrast, the Sky View Factor (SVF) and Enclosure (ENC) demonstrate predominantly macro-scale effects. Second, nonlinear "threshold effects" and saturation points are generally observed between individual street-view metrics and land-surface heat exposure. For example, the cooling benefits of GVI are subject to an upper limit, whereas excessively high SVF values exhibit a strong positive association with increased land-surface heat exposure. Third, the risk classification and targeted renewal strategies developed based on a "current condition-sensitivity matrix" can effectively mitigate localized land-surface heat exposure across streets with different functional characteristics.Discussion This study provides scientific decision-making support for the fine-grained renewal of high-density urban living environments and climate-adaptive urban governance.
SN - 2296-2565
ER -