A Sensitivity Analysis of the Social Vulnerability Index

Abstract

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.

Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index

“…Schmidtlein et al [13] determined that scale and minor changes in variable selection had little impact on the efficacy of the SoVI, a concern noted in Cutter's work. However, Tate [14] challenged that statistical bias, precision, and uncertainty are intrinsically part of the general hierarchical SoVI, thereby insisting that sensitivity analysis be included in the creation of a vulnerability index.…”

“…The following sections describe in greater detail the procedure used to explore this application of ANN to expanding SoVI. We chose the SoVI method to test an ANN as existing literature on SoVI [3,4,6,8,[13][14][15] appears to provide a clear avenue for exploration.…”

“…These data provide a basic skeleton by which to test the ability of an ANN to perform as a classification method, rather than as a complete and accurate vulnerability assessment. Yes Proportion of population that is non-white PC_POP_UNDER_5 (6) Yes Proportion of population under the age of 5 PC_POP_OVER_65 (7) Yes Proportion of the population over the age of 65 PC_CIVIL_LABOR_UNEMPLOYED (8) Yes Proportion of the civil labor force that is unemployed AVG_PEOPLE_HOUSE (9) No Average number of housing occupants PC_HOUSE_EARN_MORE_75K (10) Yes Proportion of population earning more than $75,000 per year PC_POVERTY (11) Yes Proportion of population living below the poverty level PC_RENT_OCC_HOUSING (12) Yes Proportion of housing occupied by renters PC_MOBILE_HOME (13) Yes Proportion of occupied housing as mobile homes PC_POP_OVER_25_NO_DIPLOMA (14) Yes Proportion of population over 25 with no high school diploma These data were joined to the census block groups polygons in a Geographic Information System (GIS) and the final variables were calculated within the database. The joined data were exported from the GIS to the statistical software package R to perform further analysis.…”

“…Further work involving the SoVI has gone on to explore how robust the SoVI is in terms of sensitivity and variance at different scales [3,13]. Schmidtlein et al [13] determined that scale and minor changes in variable selection had little impact on the efficacy of the SoVI, a concern noted in Cutter's work.…”

“…With the adaptive element identified by Holand and Lujala [8] coupled with index sensitivity analysis by Schmidtlein et al and Tate [13,14], new possibilities are opened for advancing the power of the SoVI. One such opportunity becomes apparent when considering Stephen and Downing's [15] assessment of vulnerability to famine and food insecurity in Ethiopia in a comparison between three methods.…”