Interaction of Urban Configuration, Temperature, and De Facto Population in Seoul, Republic of Korea: Insights from Two-Stage Least-Squares Regression Using S-DoT Data

Minkyung Park and Heechul Kim ()
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Minkyung Park: Department of Urban Planning, Gachon University, Seongnam 13120, Republic of Korea
Heechul Kim: Division of Urban Planning & Landscape Architecture, Gachon University, Seongnam 13120, Republic of Korea

Land, 2023, vol. 12, issue 12, 1-22

Abstract: Climate change exacerbates thermal experiences in urban environments, affecting the frequency of social activities in public spaces. As climate change is expected to have a greater influence on thermal comfort, effective integration of climatic knowledge and urban design is required. However, there is a lack of knowledge regarding urban configurations that are resistant to temperature and promote urban vitality. This study aimed to explore the correlation between urban configuration, thermal environment, and urban vitality. We categorized the urban configuration of Seoul and analyzed the urban configuration type that impacts urban vitality and temperature. We used the number of the de facto population to measure urban vitality. The two-stage least-squares (2-SLS) model was used to address endogeneity concerns related to configuration, temperature, and de facto population. This study shows that de facto population is influenced by both urban configuration type and temperature. Effective design strategies for maintaining de facto population while responding to climate change include a combination of small blocks and high height-to-width ratios (H/W). In contrast, open-space urban configurations negatively impact de facto population. In high-density and high-H/W areas, de facto population increased due to shading effects but decreased when the critical value was exceeded. Urban configurations with high density and deep urban canyons have greater de facto population in winter than in summer.

Keywords: urban vitality; de facto population; urban configuration; temperature; empirical study; K-means clustering; two-stage least squares (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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