A Sensitivity Analysis of the Social Vulnerability Index

Bibliographic Data

ID19598232
AuthorsMathew C Schmidtlein (University of South Carolina), Roland C Deutsch (0000-0002-5938-0330, University of North Carolina at Greensboro), Walter W Piegorsch (0000-0003-2725-5604, University of Arizona), Susan L Cutter (0000-0002-7005-8596, University of South Carolina, corresponding author)
Year2008
Volume28
Issue4
Pages1099-1114
Publication date2008-07-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueRisk Analysis (JOURNAL)
Journal identifiersISSN: 0272-4332 • E-ISSN: 1539-6924
PublisherWiley (PUBLISHER • GB)
DOI10.1111/j.1539-6924.2008.01072.x
PMID18627540
OpenAlexW2148278258
LanguageEN
Citations received94
References cited35

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003), examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty-four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability

Cartography · Econometrics · Factorial analysis · Geography · Multivariate statistics · Principal component analysis · Social vulnerability · Sociology · Statistics · Climate Change, Adaptation, Migration · Computer Science · Demography · Disaster Management and Resilience · Engineering · Flood Risk Assessment and Management · Mathematics · Psychology · Social Psychology