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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Short summary
Large uncertainties remain on how to quantify social vulnerability in a comprehensive way. Standard approaches combine many social indicators into a single vulnerability index. However, we observe no correlation between disaster damages and a vulnerability index based on literature-derived assumptions about the influence of each indicator. A regression analysis of past damages can improve the interpretation of social indicators by representing the local and domain-specific circumstances.
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https://doi.org/10.5194/nhess-2017-74
https://doi.org/10.5194/nhess-2017-74

  16 Feb 2017

16 Feb 2017

Review status: this preprint was under review for the journal NHESS but the revision was not accepted.

Weigh(t)ing the dimensions of social vulnerability based on a regression analysis of disaster damages

Vincent David Corvin Heß Vincent David Corvin Heß
  • Wegener Center for Climate and Global Change, University of Graz, Graz, Austria

Abstract. Social vulnerability determines how natural hazards can turn into social disasters. However, large uncertainties remain on how to quantify social vulnerability in a compact and comprehensive way. A principle component analysis (PCA) combines many social indicators, such as population density or the level of education, into a single vulnerability index. Whether the individual indicators increase or decrease vulnerability is usually based on consequential reasoning or derived from previous studies. These assumptions are rarely tested for their applicability to the study area. In a case study for the Austrian federal state of Styria we observe no correlation between disaster damages and an initial vulnerability index which was based on literature-derived assumptions about the influence of each indicator and following best practices for the PCA. We show that a regression analysis of past damages can improve the interpretation of social indicators by better representing the local situation. With the results from the regression-based analysis we can improve the PCA approach and only the updated vulnerability index correlates with observed damages. This indicates that the use of a PCA-based vulnerability index requires a thorough understanding about the regionally specific influence of each indicator on social vulnerability to explain differences in disaster damages.

Vincent David Corvin Heß

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Vincent David Corvin Heß

Vincent David Corvin Heß

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Latest update: 22 Jan 2021
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Short summary
Large uncertainties remain on how to quantify social vulnerability in a comprehensive way. Standard approaches combine many social indicators into a single vulnerability index. However, we observe no correlation between disaster damages and a vulnerability index based on literature-derived assumptions about the influence of each indicator. A regression analysis of past damages can improve the interpretation of social indicators by representing the local and domain-specific circumstances.
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