인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study was conducted as a follow-up study to “Study on Characteristics of Area Types Based on Rainfall Damage Amounts” and developed a composite vulnerability typology by integrating temporal trends and spatial clustering characteristics of heavy rainfall damage. All municipalities (Si, Gun, and Gu) in South Korea were targeted and heavy rainfall damage data from 2014 to 2023 were utilized. For temporal analysis, the Mann-Kendall test, Sen’s slope, and Pettitt test were employed to examine damage trends, quantify their magnitude, and detect change points. For spatial analysis, Global Moran’s I and Local Indicators of Spatial Association examined spatial autocorrelation and local clustering patterns.
A total of 16 of the 229 municipalities exhibited a statistically significant increasing damage trends, while approximately 61% displayed worsening damage structures or new damage following detected change points. Global Moran’s I confirmed positive spatial autocorrelation, with high-high and low-low clusters concentrated in the central and southeastern inland regions, respectively. Integrating temporal and spatial dimensions with the existing magnitude-based classification yielded seven composite risk types. In particular, the Low-Damage Rising Trend Type identified emerging risk areas not captured by magnitude-based classification alone.
This study interprets heavy rainfall damage through temporal persistence and spatial clustering, providing a basis for prioritizing disaster management and establishing region-specific adaptation strategies.
A total of 16 of the 229 municipalities exhibited a statistically significant increasing damage trends, while approximately 61% displayed worsening damage structures or new damage following detected change points. Global Moran’s I confirmed positive spatial autocorrelation, with high-high and low-low clusters concentrated in the central and southeastern inland regions, respectively. Integrating temporal and spatial dimensions with the existing magnitude-based classification yielded seven composite risk types. In particular, the Low-Damage Rising Trend Type identified emerging risk areas not captured by magnitude-based classification alone.
This study interprets heavy rainfall damage through temporal persistence and spatial clustering, providing a basis for prioritizing disaster management and establishing region-specific adaptation strategies.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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