Spatial Distribution of Cold-Related Illness and Identification of Vulnerable Areas

doi:10.15683/kosdi.2026.6.30.675

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2026 Vol.22, Issue 2 Preview Page Next Page

Original Article

Spatial Distribution of Cold-Related Illness and Identification of Vulnerable Areas 한파 인명피해의 공간적 분포와 취약지역 식별: 신은혜¹ㆍ김이레²*
Eunhye Shin¹, Yire Kim²*; ¹Principal Researcher, National Disaster Management Research Institute, Ulsan, Republic of Korea
²Senior Researcher, National Disaster Management Research Institute, Ulsan, Republic of Korea

30 June 2026. pp. 675-683

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Abstract

Purpose: This study aims to analyze the spatial distribution and clustering patterns of cold wave-related health damage across 250 si/gun/gu administrative districts in South Korea and to identify cold wave-vulnerable areas. Method: Using data from the Korea Disease Control and Prevention Agency’s 2024-2025 Cold-Related Illness Emergency Room Surveillance System, Global Moran’s I and Local Moran’s I (LISA) were calculated with a K-nearest neighbors (K = 6) spatial weight matrix. Result: Global Moran’s I was 0.102 (p < 0.01), indicating statistically significant positive spatial auto-correlation. LISA analysis identified 34 significant spatial clusters: 11 HH (hotspots), 8 LL (coldspots), 8 HL, and 7 LH areas. Two major hotspot clusters were found in the Seoul-Gyeonggi northern region and inland areas spanning Chungbuk, Gyeongbuk, and Gangwon provinces. Conclusion: Cold wave damage exhibits significant spatial clustering, suggesting the need for regional cooperative response strategies beyond individual administrative boundaries and customized support systems reflecting local vulnerability characteristics.

Keywords

Cold Wave

Cold-related Illnesses

Spatial Auto-correlation

Global Moran’s I

Local Moran’s I

연구목적: 본 연구는 전국 250개 시·군·구를 대상으로 한파 인명피해의 공간적 분포와 군집 패턴을 분석하고, 한파 취약지역을 식별하는 것을 목적으로 한다. 연구방법: 질병관리청 ｢2024-2025 한랭질환 응급실 감시체계｣ 자료를 활용하여 K-최근접 이웃(K = 6) 공간가중치행렬을 기반으로 전역적 모란지수와 국지적 모란지수(LISA)를 산출하였다. 연구결과: 전역적 모란지수는 0.102(p < 0.01)로 정(+)의 공간자기상관이 확인되었다. LISA 분석 결과, 34개 지역이 유의미한 공간 군집으로 식별되었으며, HH 유형 11개소, LL 유형 8개소, HL 유형 8개소, LH 유형 7개소로 나타났다. 핫스팟은 서울·경기 북부 권역과 충북·경북·강원 내륙 권역에서 형성되었다. 결론: 한파 피해는 유의미한 공간적 군집 특성을 보이므로, 개별 행정구역을 넘어 인접 지역 간 연계를 고려한 권역 단위 대응 전략과 지역 특성을 반영한 맞춤형 지원 체계가 필요하다.

키워드

한파

한랭질환

공간자기상관

전역적 모란지수

국지적 모란지수

References

Anselin, L. (1995). “Local indicators of spatial association-LISA.” Geographical Analysis, Vol. 27, pp. 93-115. 10.1111/j.1538-4632.1995.tb00338.x
Anselin, L. (2019). “A local indicator of multivariate spatial association: extending Geary’s c.” Geographical Analysis, Vol. 51, pp. 133-150. 10.1111/gean.12164
Dai, Y.L., Sun, B., Zhou, B.T., Li, H.X., He, S., Li, F., Huang, Y.T., Tang, W.C. (2025). “Intensified extreme cold surges in northern East Asia and the associated changes in atmospheric circulation under climate change.” Advances in Climate Change Research, Vol. 16, No. 5, pp. 960-973. 10.1016/j.accre.2025.07.003
Joongboo Ilbo (2024.10.24.). Only 4 Local Governments Have Cold Wave Support Ordinances Despite Growing Heating Cost Burden for Vulnerable Groups. https://www.joongboo.com/news/articleView.html?idxno=363675719.
KBS News (2026.1.23.). Samhansaon Is Now Outdated... What Causes Prolonged Cold Waves?. https://news.kbs.co.kr/news/pc/view/view.do?ncd=8467555.
Kil, H.-J., Kim. S.-Y., Shin, N.-R., Lee, Y.-S. (2015). Analysis of Supply and Demand for Employment and Welfare Social Services: Focusing on Regions. Korea Labor Institute, KLI 2015-04, Sejong.
Kim, K.-S., Hong, B.-B. (2024). Climate Change and Health Equity. CEO Report, Korea Insurance Research Institute, CEO Report 2024-02, Seoul.
Kim, Y.-J., Shin, J.-Y. (2023). “Necessity and direction of social vulnerability survey for climate crisis vulnerable groups.” Korea Environment Institute, Environmental Forum, Vol. 27, No. 6, pp. 1-24.
Koo, J.-Y. (2021). “The effect of local administrative agencies’ disaster management capabilities on the mitigation of natural disasters: A comparison of metropolitan and provincial cities.” Journal of Local Government Studies, Vol. 33, No. 3, pp. 255-282. 10.21026/JLGS.2021.33.3.255
Korea Disease Control and Prevention Agency (2025.3.11). 2024-2025 Winter Cold-Related Illness Patients Decreased 16.5% Compared to Previous Year, Deaths Decreased 33.0%. Press Release.
Korean Statistical Information Service (KOSIS). Elderly Population Ratio (City/Province/District). https://kosis.kr/index/index.do.
Lim, S.-H. (2020). “Spatial autocorrelation and the turnout of the early voting and regular voting: Analysis of the 21st general election at Dong in Seoul.” Korean Journal of Legislative Studies, Vol. 26, No. 2, pp. 113-140.
Munhwa Ilbo (2025.10.17.). Heating Cost Support for Vulnerable Groups... Varies Greatly by Region. https://www.munhwa.com/article/11540041.
Sohn, J.-Y. (2011). “Analysis on urban water use using moran and local moran indices for spatial pattern and its change - A case study of the counties in the U.S. southeast.” Journal of the Korean Regional Science Association, Vol. 27, No. 2, pp. 77-97.
Yeom, J.-W., Kang, S.-W., Jung, P.-S., Jung, J.-C. (2020). “Spatial scope of the regional hazard mitigation plan.” Journal of The Korean Society of Hazard Mitigation, Vol. 20, No. 1, pp. 61-70. 10.9798/KOSHAM.2020.20.1.61

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 22
No :2
Pages :675-683
DOI :https://doi.org/10.15683/kosdi.2026.6.30.675

[1] Anselin, L. (1995). “Local indicators of spatial association-LISA.” Geographical Analysis, Vol. 27, pp. 93-115. 10.1111/j.1538-4632.1995.tb00338.x

[2] Anselin, L. (2019). “A local indicator of multivariate spatial association: extending Geary’s c.” Geographical Analysis, Vol. 51, pp. 133-150. 10.1111/gean.12164

[3] Dai, Y.L., Sun, B., Zhou, B.T., Li, H.X., He, S., Li, F., Huang, Y.T., Tang, W.C. (2025). “Intensified extreme cold surges in northern East Asia and the associated changes in atmospheric circulation under climate change.” Advances in Climate Change Research, Vol. 16, No. 5, pp. 960-973. 10.1016/j.accre.2025.07.003

[4] Joongboo Ilbo (2024.10.24.). Only 4 Local Governments Have Cold Wave Support Ordinances Despite Growing Heating Cost Burden for Vulnerable Groups. https://www.joongboo.com/news/articleView.html?idxno=363675719.

[5] KBS News (2026.1.23.). Samhansaon Is Now Outdated... What Causes Prolonged Cold Waves?. https://news.kbs.co.kr/news/pc/view/view.do?ncd=8467555.

[6] Kil, H.-J., Kim. S.-Y., Shin, N.-R., Lee, Y.-S. (2015). Analysis of Supply and Demand for Employment and Welfare Social Services: Focusing on Regions. Korea Labor Institute, KLI 2015-04, Sejong.

[7] Kim, K.-S., Hong, B.-B. (2024). Climate Change and Health Equity. CEO Report, Korea Insurance Research Institute, CEO Report 2024-02, Seoul.

[8] Kim, Y.-J., Shin, J.-Y. (2023). “Necessity and direction of social vulnerability survey for climate crisis vulnerable groups.” Korea Environment Institute, Environmental Forum, Vol. 27, No. 6, pp. 1-24.

[9] Koo, J.-Y. (2021). “The effect of local administrative agencies’ disaster management capabilities on the mitigation of natural disasters: A comparison of metropolitan and provincial cities.” Journal of Local Government Studies, Vol. 33, No. 3, pp. 255-282. 10.21026/JLGS.2021.33.3.255

[10] Korea Disease Control and Prevention Agency (2025.3.11). 2024-2025 Winter Cold-Related Illness Patients Decreased 16.5% Compared to Previous Year, Deaths Decreased 33.0%. Press Release.

[11] Korean Statistical Information Service (KOSIS). Elderly Population Ratio (City/Province/District). https://kosis.kr/index/index.do.

[12] Lim, S.-H. (2020). “Spatial autocorrelation and the turnout of the early voting and regular voting: Analysis of the 21st general election at Dong in Seoul.” Korean Journal of Legislative Studies, Vol. 26, No. 2, pp. 113-140.

[13] Munhwa Ilbo (2025.10.17.). Heating Cost Support for Vulnerable Groups... Varies Greatly by Region. https://www.munhwa.com/article/11540041.

[14] Sohn, J.-Y. (2011). “Analysis on urban water use using moran and local moran indices for spatial pattern and its change - A case study of the counties in the U.S. southeast.” Journal of the Korean Regional Science Association, Vol. 27, No. 2, pp. 77-97.

[15] Yeom, J.-W., Kang, S.-W., Jung, P.-S., Jung, J.-C. (2020). “Spatial scope of the regional hazard mitigation plan.” Journal of The Korean Society of Hazard Mitigation, Vol. 20, No. 1, pp. 61-70. 10.9798/KOSHAM.2020.20.1.61

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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