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

Chien-Hao Sung1, Shyue-Cherng Liaw1

  • 1Department of Geography, National Taiwan Normal University, Taipei 10610, Taiwan.

International Journal of Environmental Research and Public Health
|June 2, 2021
PubMed
Summary

Northeastern Taiwan

Keywords:
geographically weighted regression (GWR)resiliencespatial autocorrelation analysisspatial differencespatially explicit resilience-vulnerability model (SERV)vulnerability

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Risk Management

Background:

  • Natural hazards like floods and debris flows pose significant risks in northeastern Taiwan.
  • Understanding spatial patterns of vulnerability and resilience is crucial for effective disaster management.

Purpose of the Study:

  • To explore the spatial patterns of vulnerability and resilience to natural hazards in northeastern Taiwan.
  • To quantify and analyze these patterns using a spatially explicit model.

Main Methods:

  • Spatially Explicit Resilience-Vulnerability model (SERV)
  • Principal Component Analysis (PCA) for data aggregation
  • Spatial Autocorrelation Analysis
  • Geographically Weighted Regression (GWR) for validation

Main Results:

  • Mountainous areas exhibit high vulnerability and low resilience to natural hazards.
  • Urban regions in plains show low vulnerability and high resilience.
  • Topography is identified as the primary driver of spatial differences in vulnerability and resilience.

Conclusions:

  • The SERV model effectively quantifies vulnerability and resilience.
  • Significant spatial disparities exist between mountain and plain areas.
  • Favorable topography in plains supports socioeconomic development, enhancing resilience.