Rongwei Wu, Jiali Zhang, Ziyi Luo, Liang Zhou
Global climate change and rapid urbanization have intensified the urban heat island effects, leading to more frequent extreme heat events. In densely populated megacities, accelerated population aging and compact urban forms further amplify heat health risks. This study constructed a heat health risk indicator system covering heat hazard, exposure, and vulnerability based on the Intergovernmental Panel on Climate Change risk triangle framework. It integrated Landsat/MODIS data with Chongqing's sixth and seventh population censuses to assess heat health risk at the township/subdistrict scale in Chongqing's central urban area. Exploratory Spatial Data Analysis was employed to examine spatiotemporal evolution, and a layered overlay approach was used to identify dominant risk types. Results showed that for the periods 2001– 2010 and 2011–2020, heat hazard exhibited a multi-center-periphery structure, with high-value zones expanding outward. Heat exposure decreased outward from the Yuzhong Peninsula. Heat vulnerability was higher in peripheral areas than in core areas. Correspondingly, heat health risk exhibited a pronounced center-periphery pattern, with high-risk subdistricts concentrated in Yuzhong District and adjacent core areas and showing outward expansion with strengthened spatial clustering over time. Spatial autocorrelation was observed, with High–High clusters in core areas and Low–Low clusters in peripheral areas. High–High clusters expanded over time. Dominant risk types shifted from single-component to multi-component patterns with clear spatial differentiation. During 2001–2010, vulnerability-dominant risk prevailed in outer districts, while hazard-dominant risk was concentrated in core areas. Overall, high-risk areas remained concentrated in the core districts, although some areas with low hazard, exposure, and vulnerability were still classified as high-risk, indicating an edge-overlay effect from the combined influence of the three components. These findings support fine-scale heat health risk identification, zoned management strategies, and targeted intervention policies.