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Analyzing the relationships between ...

Alexander, David Jean-Paul.

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Analyzing the relationships between hazard vulnerability science and disaster management policy and practice: A case study of Atlantic hurricanes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analyzing the relationships between hazard vulnerability science and disaster management policy and practice: A case study of Atlantic hurricanes./
Author:	Alexander, David Jean-Paul.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	297 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.
Contained By:	Dissertation Abstracts International78-06B(E).
Subject:	Geographic information science and geodesy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10191389
ISBN:	9781369527278

Analyzing the relationships between hazard vulnerability science and disaster management policy and practice: A case study of Atlantic hurricanes.
Alexander, David Jean-Paul.

Analyzing the relationships between hazard vulnerability science and disaster management policy and practice: A case study of Atlantic hurricanes. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 297 p.

Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.

Thesis (Ph.D.)--George Mason University, 2016.

Vulnerability indices have been used extensively in disaster management, and the social vulnerability index (SoVI) has been regarded as the most popular despite its appropriateness and performance not being validated conceptually and empirically. A pedigree matrix and variable crosswalk were used to examine the conceptual relationships between hazard vulnerability science (three selected vulnerability indices, including SoVI) and disaster management (using disaster operations impact model data). The research indicates there are theoretical linkages between hazard vulnerability indicators and disaster management essential elements of information. The analysis also show that SoVI is conceptually the most appropriate among the three vulnerability index. Subsequently, I conducted an empirical study to assess the capability of SoVI to predict damages caused by natural disaster events. SoVI index scores were related to nine Atlantic hurricanes and their associated federal disaster costs and estimated damages at the county level. Ordinary least squares regression, spatial econometrics, and geographically weighted regression are used to evaluate their empirical relationships. The study demonstrates that SoVI has little explanatory power in explaining federal disaster costs per capita and that the disaster impact model variables are more effective in explaining the variation in federal disaster costs per capital rather than the SoVI. The results also show that these relationships varied tremendously across the nine hurricane events. Although using logarithmic transformation to reduce skewness in variables improved model performance marginally, no model involving SoVI performs reasonably well. The research recommends using the disaster impact model outputs for constructing a more reliable predictive model to support disaster operations.

ISBN: 9781369527278Subjects--Topical Terms:

2122917
Geographic information science and geodesy.

Analyzing the relationships between hazard vulnerability science and disaster management policy and practice: A case study of Atlantic hurricanes.
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520 $a Vulnerability indices have been used extensively in disaster management, and the social vulnerability index (SoVI) has been regarded as the most popular despite its appropriateness and performance not being validated conceptually and empirically. A pedigree matrix and variable crosswalk were used to examine the conceptual relationships between hazard vulnerability science (three selected vulnerability indices, including SoVI) and disaster management (using disaster operations impact model data). The research indicates there are theoretical linkages between hazard vulnerability indicators and disaster management essential elements of information. The analysis also show that SoVI is conceptually the most appropriate among the three vulnerability index. Subsequently, I conducted an empirical study to assess the capability of SoVI to predict damages caused by natural disaster events. SoVI index scores were related to nine Atlantic hurricanes and their associated federal disaster costs and estimated damages at the county level. Ordinary least squares regression, spatial econometrics, and geographically weighted regression are used to evaluate their empirical relationships. The study demonstrates that SoVI has little explanatory power in explaining federal disaster costs per capita and that the disaster impact model variables are more effective in explaining the variation in federal disaster costs per capital rather than the SoVI. The results also show that these relationships varied tremendously across the nine hurricane events. Although using logarithmic transformation to reduce skewness in variables improved model performance marginally, no model involving SoVI performs reasonably well. The research recommends using the disaster impact model outputs for constructing a more reliable predictive model to support disaster operations.
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