Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul

Cheolhee Yoo; Jungho Im; Qihao Weng; Dongjin Cho; Eunjin Kang; Yeji Shin

doi:10.1016/j.isci.2023.108123

Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul

iScience. 2023 Oct 4;26(11):108123. doi: 10.1016/j.isci.2023.108123. eCollection 2023 Nov 17.

Authors

Cheolhee Yoo^{1

2}, Jungho Im³, Qihao Weng^{1

2}, Dongjin Cho³, Eunjin Kang³, Yeji Shin^{3

4}

Affiliations

¹ JC STEM Lab of Earth Observations, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
² Research Centre for Artificial Intelligence in Geomatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
³ Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
⁴ Market Intelligence Team, Purchasing Strategy Unit, CJ CheilJedang Corporation, Market Intelligence Team, Seoul, South Korea.

Abstract

Previous heat risk assessments have limitations in obtaining accurate heat hazard sources and capturing population distributions, which change over time. This study proposes a diurnal heat risk assessment framework incorporating spatiotemporal air temperature and real-time population data. Daytime and nighttime heat risk maps were generated using hazard, exposure, and vulnerability components in Seoul during the summer of 2018. The hazard was derived from the daily extreme air temperatures obtained using the stacking machine learning model. Exposure was calculated using de facto population density, and vulnerability was assessed using demographic and socioeconomic indicators. The resulting maps revealed distinct diurnal spatial patterns, with high-risk areas in the urban core during the day and dispersed at night. Daytime heat risk was strongly correlated with heat-related illness ratios (R = 0.8) and accurately captured temporal fluctuations in heat-related illness incidence. The proposed framework can guide site-specific adaptation and response plans for dynamic urban heat events.

Keywords: Artificial intelligence; Earth sciences; Engineering; Urban planning.