2025-05-16
来源：CITIES Volume: 162 DOI: 10.1016/j.cities.2025.105963
作者：龚芳颖（中国人民大学公共管理学院 讲师）、杨泽如（中国人民大学公共管理学院 硕士生）、邓诗桦（中国人民大学公共管理学院 本科生）
龚芳颖｜中国人民大学公共管理学院讲师
摘要：
Quantifying heat health risks by integrating high-temperature hazards with demographic, economic and environmental components is critical to understanding the comprehensive impacts of urban thermal environments. The objective is to develop a fine-scale methodology for assessing heat hazard, exposure, and vulnerability in urban areas, accounting for both daytime and nighttime conditions, to inform more targeted and effective heat mitigation strategies. We propose a novel framework that combines multiple high-resolution data sources to assess heat health risks at the sub-district level. The framework combines NASA ECOSTRESS land surface temperature data, mobile signaling for population mobility, and 3D urban morphology from street view images via deep learning. The Heat Health Risk Index quantifies heat exposure, accounting for temporal and spatial urban variations. Key findings include: (1) Distinct diurnal migration patterns of heat hazards were observed, shifting from northeastern areas during nighttime to southwestern regions during the daytime; (2) Population and environmental exposure analysis showed higher daytime levels in southern and eastern areas between the 4th and 5th ring roads due to commuter influx, with elevated nighttime risks persisting in densely populated regions; (3) Principal component analysis identified four key vulnerability factors of urban built-up, economic development, education level, and disadvantaged groups. High risks in northeast Fengtai and correlation analysis emphasized the critical role of socioeconomic and environmental factors in mitigating heat risks. This study offers a fine-scale approach to assess heat health risks, providing key insights for urban planning and climate adaptation to mitigate heat-related health impacts.
关键字：
Heat health risk index; Urban heat; ECOSTRESS; Street view image; Beijing
