Park, M.Y., Kang, J.E. (2026). Do Community Characteristics Explain HeatRelated Illness in Seoul, Korea?. GeoHealth, 10(3), e2025GH001580.
Heatwaves intensified by climate change have increasingly threatened public health, highlighting the need for proactive and spatially targeted interventions. This study aimed to provide scientific evidence for managing the risk of heat-related illness (HRI) by integrating communitylevel physical environments and sociodemographic characteristics and applying explainable artificial intelligence techniques. Based on the Hazard?Exposure?Vulnerability?Response framework presented in the IPCC Sixth Assessment Report, we evaluated 20 regression models using Seoul, South Korea, as a case study. Nonlinear models demonstrated superior predictive performance compared to linear models, and Shapley additive explanations analysis revealed that apparent temperature, urbanized area ratio, olderadult population ratio, outdoor worker count, and accessibility to water areas were the most influential variables. Apparent temperature exhibited a distinct threshold with a sharp increase in HRI risk above 36°C, while lessurbanized areas were associated with higher incidence rates. Communities with higher proportions of older adults and outdoor workers consistently demonstrated greater vulnerability, and the effect of accessibility to water areas was spatially limited within daily activity spaces. Given the weak linear associations between HRI incidence and all explanatory variables (r <0.2), nonlinear dynamics and interactions play a critical role in understanding HRI risk. This study provides actionable insights for designing targeted heathealth policies that consider diverse community characteristics.
Keywords : Heat-related illness, Machine learning, Community characteristics, Climate risk assessment, Apparent temperature
연구과제 : 연구재단(폭염대응), 기특대, 환경보건센터
DOI 링크 : https://doi.org/10.1029/2025GH001580