Earticle

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 유형11개소, 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.

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.

The object of this analysis was to analyze the spatial relation between one-person households and small-housing supplies in the Seoul Metropolitan City area. By analyzing the spatial relationship between one-person households and small housing supply, it is supported to promote a housing supply policy that meets the demand of one-person households. The analysis methods are spatial autocorrelation analysis and nearest neighbor analysis. The Global Moran's I index could not identify any statistically significant autocorrelation in the year 2000, but it verified its existence in the year 2021. A hot-spot and cold-spot analysis identified the spatial concentration patterns for both one-person households and small housings. It was confirmed that one-person households and small-housing supplies were concentrated in a specific area. The one-person households are flocking to areas with a high concentration of small-housing supplies with convenient transportation to the city center. The nearest neighbor analysis of the hot-spot administrative districts(dongs) also reveals that, apart from the year 2010 compared to the year 2000 for one-person households, but from the year 2021 compared to the year 2010 for one-person households and both the hot-spot administrative districts(dongs) of small-housing supplies form clusters that are statistically significant. Although there were no statistically significant differences, a spatial concentration was observed between one-person households and small-housing supplies, as the mean shortest distance in the hot-spot administrative districts(dongs) in the year 2021 compared to the year 2010 is lower than that in the year 2010 compared to the year 2000. Collectively, there exists a spatial autocorrelation between one-person households and small-housing supplies, meaning they tend to be clustered together in specific areas. Based on these results, high-quality small-housing supplies should be provided to areas where oneperson households are expected to be concentrated. In addition, it is necessary to improve the quality of life of one-person households by improving the residential environment of small-housing supplies.