Using spatial pattern analysis to explore the relationship between vulnerability and resilience to natural hazards

Chien Hao Sung, Shyue Cherng Liaw*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural hazards, including flood and debris flow events, which are the most common natural hazards in our case study area due to the topography and precipitation features. In order to provide a concise result, we apply the principal component analysis (PCA) to aggregate the correlated variables. Moreover, we use the spatial autocor-relation analysis to analyze the spatial pattern and spatial difference. We also adopt the geographically weighted regression (GWR) to validate the effectiveness of SERV. The result of GWR shows that SERV is valid and unbiased. Moreover, the result of spatial autocorrelation analysis shows that the mountain areas are extremely vulnerable and lack enough resilience. In contrast, the urban regions in plain areas show low vulnerability and high resilience. The spatial difference between the mountain and plain areas is significant. The topography is the most significant factor for the spatial difference. The high elevation and steep slopes in mountain areas are significant obstacles for socioeconomic development. This situation causes consequences of high vulnerability and low resili-ence. The other regions, the urban regions in the plain areas, have favorable topography for socioeconomic development. Eventually, it forms a scenario of low vulnerability and high resilience.

原文英語
文章編號5634
期刊International journal of environmental research and public health
18
發行號11
DOIs
出版狀態已發佈 - 2021 六月 1

ASJC Scopus subject areas

  • 污染
  • 公共衛生、環境和職業健康
  • 健康、毒理學和誘變

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