期刊
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
卷 71, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ijdrr.2022.102817
关键词
Disaster resilience; Community building portfolios; Clustering; GIS data; Census data; Vulnerability; Recoverability
资金
- Korea Institute of Energy Technology Evaluation and Planning (KETEP) [20181510102410]
- Ministry of Trade, Industry & Energy (MOTIE) of Republic of Korea
- Institute of Construction and Environmental Engineering at Seoul National University
- Korea Evaluation Institute of Industrial Technology (KEIT) [20181510102410] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This study proposes a community disaster resilience clustering method that evaluates the disaster resilience of communities by integrating physical vulnerability and socioeconomic recoverability. The results indicate that the method successfully categorizes communities in terms of disaster resilience and provides useful insights for resilience planning.
Disaster resilience is one of the essential capabilities of communities to minimize the negative impact of disasters. Because of its importance and multidimensional characteristics, disaster resilience has been investigated in various research fields including natural science, engineering, and social science. However, despite the extensive studies on community disaster resilience, the gap between the developed methodologies and the available databases still makes it challenging to assess the disaster resilience of communities. To overcome this issue and make the most of the available open databases, this paper proposes a community disaster resilience clustering (CDRC) method. To assess the disaster resilience of community building portfolios, The CDRC separately evaluates the physical vulnerability using open GIS building databases and socioeconomic recoverability using census database. The method then integrates the two measures through clustering to characterize the disaster resilience of the community in terms of physical vulnerability and socioeconomic recoverability. To demonstrate the concept and advantages of CDRC, the proposed method was compared with other existing approaches using a virtual community example. Next, the CDRC method was applied to a real community example using the GIS database and the census data of South Korea. The results indicate that the CDRC method successfully categorizes communities in terms of disaster resilience and provides useful insights for resilience planning.
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