Analysis of Factors Influencing Summer Thermal Environment in Seoul Using Geographically Weighted Regression (GWR)

doi:10.16879/jkca.2025.25.1.011

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2025 Vol.25, Issue 1 Preview Page Next Page

Research Article

Analysis of Factors Influencing Summer Thermal Environment in Seoul Using Geographically Weighted Regression (GWR) 지리가중회귀분석(GWR)을 활용한 서울시 여름철 열환경 영향요인 분석: 홍유진^1†, 홍인수²
Youjin Hong^1†, Insu Hong²; ¹서울대학교 지리교육과 강사
²CJ대한통운 TES물류기술연구소

¹Lecturer, Department of Geography Education, Seoul National University
²Head of Research Team, CJ Logistics

30 April 2025. pp. 11~21

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Abstract

This study aims to analyze the spatial factors affecting land surface temperature (LST) during the summer in Seoul and to identify the spatial heterogeneity of their influences. Using Landsat 8 imagery, average summer LST for 2024 was derived, and explanatory variables were constructed based on natural environment, urban structure, population activity, and land use at a 250-meter grid resolution. Both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were employed, and the results indicated that the GWR model showed superior explanatory power (R² = 0.878) and lower AIC model fit compared to the OLS. The spatial distribution of Local R² and variable-specific regression coefficients confirmed spatial non-stationarity and nonlinear patterns of influence. This study provides valuable insights into the development of region-specific heat mitigation strategies in Seoul and contributes to addressing thermal inequity and managing heat-vulnerable areas in urban environments.

Keywords

Urban heat island

Land surface temperature

Geographically weighted regression

Spatial non-stationarity

Seoul

본 연구는 서울시를 대상으로 여름철 지표온도(LST)에 영향을 미치는 공간적 요인을 분석하고, 지역별 영향력의 이질성을 파악하고자 하였다. 이를 위해 Landsat 8 위성영상을 활용하여 2024년 여름철 평균 지표온도를 산출하고, 자연환경, 도시구조, 인구활동, 토지이용 변수들을 250m 격자 단위로 구축하였다. 전역적 회귀분석(OLS)과 지리가중회귀분석(GWR)을 수행한 결과, GWR 모형이 더 높은 설명력(R² = 0.878)과 낮은 AIC 값을 보여 공간적 적합도가 우수함을 확인하였다. 또한 Local R² 분포를 통해 모형의 설명력이 지역별로 상이함을 확인하였고, 변수별 회귀계수의 공간 분포를 통해 열환경 형성 요인의 비선형성과 공간 비정상성을 실증적으로 확인하였다. 본 연구는 서울시의 열환경 대응을 위한 지역 맞춤형 공간정책 수립에 기초자료를 제공하며, 도시열섬의 불균형 해소 및 열취약지역 관리 전략의 수립에 기여하고자 하였다.

키워드

도시열섬

지표온도

지리가중회귀분석

공간이질성

서울시

References

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이훈종, 2023, “도시열섬현상의 공간적 특성 및 도시지리학에서의 함의: 미국 샌안토니오 대도시 지역을 중심으로,” 국토지리학회지, 57(1), 79-100.10.22905/kaopqj.2023.57.1.7
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He, B. J., Ding, L., and Prasad, D., 2020, Relationships among local-scale urban morphology, urban ventilation, urban heat island and outdoor thermal comfort under sea breeze influence, Sustainable Cities and Society, 60(April), 102289. 10.1016/j.scs.2020.102289
Hsu, A., Sheriff, G., Chakraborty, T., and Manya, D., 2021, Disproportionate exposure to urban heat island intensity across major US cities, Nature Communications, 12(1), 2721.10.1038/s41467-021-22799-534035248PMC8149665
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Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., and Wegner, J. D., 2022, Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles, Remote sensing of environment, 268, 112760.10.1016/j.rse.2021.112760
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Li, J., Song, C., Cao, L., Zhu, F., Meng, X., and Wu, J., 2011, Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China, Remote Sensing of Environment, 115(12), 3249-3263.10.1016/j.rse.2011.07.008
Mitchell, B. C. and Chakraborty, J., 2015, Landscapes of thermal inequity: disproportionate exposure to urban heat in the three largest US cities, Environmental Research Letters, 10(11), 115005.10.1088/1748-9326/10/11/115005
Shandas, V., Voelkel, J., Williams, J., and Hoffman, J., 2019, Integrating satellite and ground measurements for predicting locations of urban heat vulnerability, Climate, 7(1), 5.10.3390/cli7010005
Shrestha, N., 2020, Detecting multicollinearity in regression analysis, American Journal of Applied Mathematics and Statistics, 8(2), 39-42.10.12691/ajams-8-2-1
Tomlinson, C. J., Chapman, L., Thornes, J. E., and Baker, C. J., 2011, Including the urban heat island in spatial heat health risk assessment strategies: A case study for Birmingham, UK, International Journal of Health Geographics, 10(1), 42. 10.1186/1476-072X-10-4221682872 PMC3141360
Voogt, J. A. and Oke, T. R., 2003, Thermal remote sensing of urban climates, Remote Sensing of Environment, 86(3), 370-384.10.1016/S0034-4257(03)00079-8
Weng, Q., Lu, D. and Schubring, J., 2004, Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies, Remote sensing of Environment, 89(4), 467-483.10.1016/j.rse.2003.11.005
Xu, H., 2006, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, International journal of remote sensing, 27(14), 3025-3033.10.1080/01431160600589179
Zhao, L., Lee, X., Smith, R. B., and Oleson, K., 2014, Strong contributions of local background climate to urban heat islands, Nature, 511(7508), 216-219.10.1038/nature1346225008529
Zhang, Y., Li, T., Wang, J., and Zhang, H., 2023, Combined effect of prolonged high temperatures and humidity on mortality in China: A nationwide study, The Lancet Planetary Health, 7(4), 276-284.
Zhou, W., Huang, G., and Cadenasso, M. L., 2011, Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes, Landscape and Urban Planning, 102(1), 54-63.10.1016/j.landurbplan.2011.03.009
경향신문, 2024년 6월 28일, “폭염 증가 1위 도시 서울” https://www.khan.co.kr/article/202406280900011/?kref=rta

Information

Publisher :The Korean Cartographic Association
Publisher(Ko) :한국지도학회
Journal Title :Journal of the Korean Cartographic Association
Journal Title(Ko) :한국지도학회지
Volume : 25
No :1
Pages :11~21
DOI :https://doi.org/10.16879/jkca.2025.25.1.011

[1] 김병우·유정훈·송석현, 2020, “서울시의 토지피복 및 공간특성이 지표온도에 미치는 영향 분석,” 국토지리학회지, 54(2), 145-160.

[2] 김종성·강정은, 2018, “압축형 공간구조 특성이 도시 열환경에 미치는 영향,” 국토계획, 53(5), 39-52.

[3] 박성욱·김민식, 2021, “Landsat 8 OLI/TIRS Science Product를 활용한 지표면 온도 유용성 평가,” 대한원격탐사학회지, 37(3), 463-473.

[4] 박지영·윤종열, 2017, “도시 열섬 영향 요인 분석을 위한 지표온도와 공간특성 간의 관계: 서울시를 사례로,” 한국GIS학회지, 25(1), 1-14.10.29049/rjcc.2017.25.1.88

[5] 배민기·이호영·송규진, 2020, “폭염 시 위험지역과 취약계층 거주지역 간의 공간관계 분석,” 지역과 도시계획, 31(3), 253-265.

[6] 엄정희, 2016, “공간계획 활용을 위한 도시 열환경 취약성 평가 연구,” 한국지역지리학회지, 22(4), 558-573.

[7] 이수미·최지용·박은아, 2019, “도시 환경이 온열질환 발생에 미치는 영향에 관한 연구,” 보건사회연구, 39(2), 484-510.

[8] 이진혁·김현정, 2023, “고령인구를 고려한 폭염취약지 공간분석: 대구광역시를 중심으로,” 대한지리학회지, 58(2), 229-243.

[9] 이훈종, 2023, “도시열섬현상의 공간적 특성 및 도시지리학에서의 함의: 미국 샌안토니오 대도시 지역을 중심으로,” 국토지리학회지, 57(1), 79-100.10.22905/kaopqj.2023.57.1.7

[10] 조혜민·김용창·김현정, 2019, “서울시 도시열섬현상 지역의 물리적 환경과 인구 및 사회경제적 특성 탐색,” 국토계획, 54(6), 101-116.

[11] 한아름·이태훈·이선준·곽한솔, 2024, “머신러닝을 이용한 인구사회학적 요인과 기상요인이 온열질환자 발생에 미치는 영향 분석,” Ecology and Resilient Infrastructure, 11(4), 186-202.

[12] Chakraborty, T., Hsu, A., Manya, D., and Sheriff, G., 2019, Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective, Environmental Research Letters, 14(10), 105003.10.1088/1748-9326/ab3b99

[13] Gao, Y., Zhao, J., and Han, L., 2022, Exploring the spatial heterogeneity of urban heat island effect and its relationship to block morphology with the geographically weighted regression model, Sustainable Cities and Society, 76, 103431.10.1016/j.scs.2021.103431

[14] Harlan, S. L., Brazel, A. J., Prashad, L., Stefanov, W. L., and Larsen, L., 2006, Neighborhood microclimates and vulnerability to heat stress, Social Science and Medicine, 63(11), 2847-2863. 10.1016/j.socscimed.2006.07.03016996668

[15] He, B. J., Ding, L., and Prasad, D., 2020, Relationships among local-scale urban morphology, urban ventilation, urban heat island and outdoor thermal comfort under sea breeze influence, Sustainable Cities and Society, 60(April), 102289. 10.1016/j.scs.2020.102289

[16] Hsu, A., Sheriff, G., Chakraborty, T., and Manya, D., 2021, Disproportionate exposure to urban heat island intensity across major US cities, Nature Communications, 12(1), 2721.10.1038/s41467-021-22799-534035248PMC8149665

[17] Jabbar, H. K., Hamoodi, M. N., and Al-Hameedawi, A. N., 2023, Urban heat islands: A review of contributing factors, effects and data, Earth and Environmental Science, 1129(1), 012038.10.1088/1755-1315/1129/1/012038

[18] Karnieli, A., Dall’Olmo, G., Tzanis, C. G., Soudani, K., and Makowski, D., 2010, Satellite remote sensing of vegetation indices and their use for land surface phenology, Sensors, 10(3), 2066-2101.

[19] Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., and Wegner, J. D., 2022, Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles, Remote sensing of environment, 268, 112760.10.1016/j.rse.2021.112760

[20] Lee, K., Kim, Y., Sung, H. C., Ryu, J., and Jeon, S. W., 2019, Trend analysis of urban heat island intensity according to urban area change in Asian mega cities, Sustainability, 12(1), 112.10.3390/su12010112

[21] Li, J., Song, C., Cao, L., Zhu, F., Meng, X., and Wu, J., 2011, Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China, Remote Sensing of Environment, 115(12), 3249-3263.10.1016/j.rse.2011.07.008

[22] Mitchell, B. C. and Chakraborty, J., 2015, Landscapes of thermal inequity: disproportionate exposure to urban heat in the three largest US cities, Environmental Research Letters, 10(11), 115005.10.1088/1748-9326/10/11/115005

[23] Shandas, V., Voelkel, J., Williams, J., and Hoffman, J., 2019, Integrating satellite and ground measurements for predicting locations of urban heat vulnerability, Climate, 7(1), 5.10.3390/cli7010005

[24] Shrestha, N., 2020, Detecting multicollinearity in regression analysis, American Journal of Applied Mathematics and Statistics, 8(2), 39-42.10.12691/ajams-8-2-1

[25] Tomlinson, C. J., Chapman, L., Thornes, J. E., and Baker, C. J., 2011, Including the urban heat island in spatial heat health risk assessment strategies: A case study for Birmingham, UK, International Journal of Health Geographics, 10(1), 42. 10.1186/1476-072X-10-4221682872 PMC3141360

[26] Voogt, J. A. and Oke, T. R., 2003, Thermal remote sensing of urban climates, Remote Sensing of Environment, 86(3), 370-384.10.1016/S0034-4257(03)00079-8

[27] Weng, Q., Lu, D. and Schubring, J., 2004, Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies, Remote sensing of Environment, 89(4), 467-483.10.1016/j.rse.2003.11.005

[28] Xu, H., 2006, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, International journal of remote sensing, 27(14), 3025-3033.10.1080/01431160600589179

[29] Zhao, L., Lee, X., Smith, R. B., and Oleson, K., 2014, Strong contributions of local background climate to urban heat islands, Nature, 511(7508), 216-219.10.1038/nature1346225008529

[30] Zhang, Y., Li, T., Wang, J., and Zhang, H., 2023, Combined effect of prolonged high temperatures and humidity on mortality in China: A nationwide study, The Lancet Planetary Health, 7(4), 276-284.

[31] Zhou, W., Huang, G., and Cadenasso, M. L., 2011, Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes, Landscape and Urban Planning, 102(1), 54-63.10.1016/j.landurbplan.2011.03.009

[32] 경향신문, 2024년 6월 28일, “폭염 증가 1위 도시 서울” https://www.khan.co.kr/article/202406280900011/?kref=rta

Journal of the Korean Cartographic Association ISSN:1598-6160(Print) 한국지도학회지

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