Using Spatial Pattern Analysis to Explore the Relationship between Vulnerability and Resilience to Natural Hazards

Chien-Hao Sung and Shyue-Cherng Liaw
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Chien-Hao Sung: Department of Geography, National Taiwan Normal University, Taipei 10610, Taiwan
Shyue-Cherng Liaw: Department of Geography, National Taiwan Normal University, Taipei 10610, Taiwan

IJERPH, 2021, vol. 18, issue 11, 1-16

Abstract: 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 autocorrelation 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 resilience. 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.

Keywords: vulnerability; resilience; spatially explicit resilience-vulnerability model (SERV); spatial autocorrelation analysis; geographically weighted regression (GWR); spatial difference (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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