Identifying priority areas for urban heat stress management using high-density sensor networks based on apparent temperature for enhancing urban environments

Abstract

In this study, a heat stress analysis and vulnerability assessment were conducted in the Seoul Metropolitan Area using high-resolution urban sensor data from the Smart Seoul Data of Things (S·DoT), a high-density urban sensor network characterized by high spatial and temporal resolution. The analysis focused on summer 2021 and used apparent temperature (AT) — a more human-centric metric incorporating humidity — to define heat stress occurrence. Unlike conventional studies that use air temperature (Ta) to define heatwaves, this study uses the term ‘heat stress’ to refer to days when AT exceeds 33 °C. The heat stress severity measured by frequency, intensity, and duration was compared across 25 residential areas to minimize variations caused by land use differences. Space-time pattern mining was then used to cluster relatively high- and low-temperature zones in Seoul based on AT. These findings were integrated with residential and pedestrian heat-stress vulnerability factors to comprehensively assess heat stress vulnerability in the city. The results revealed significant disparities in heat stress severity among Seoul's residential areas. Additionally, the space-time pattern mining identified areas with relatively higher temperatures that coincided with heat-stress vulnerability factors related to population density. The outcomes of this study can inform urban planning and policy decisions by facilitating efficient resource and financial allocation, addressing health inequalities, and the prioritization of vulnerable populations. The significance of this research lies in its pioneering approach to analyzing heat stress based on AT using S·DoT data.