The constituent components and local indicator variables of social vulnerability index

Gainbi Park and Zengwang Xu ()
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Gainbi Park: University of Wisconsin Milwaukee
Zengwang Xu: University of Wisconsin Milwaukee

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 110, issue 1, No 5, 95-120

Abstract: Abstract Social vulnerability index (SoVI) has been widely used to measure the extent to which people or places are socially vulnerable. The SoVI is an aggregate composite index that linearly combines a few principal components resulted from the principal components analysis on a number of selected social vulnerability indicator variables, and it can quantify the relative level of overall social vulnerability but cannot inform the specific local social indicators that contribute to the vulnerability in various degrees. The specific social indicators that either attenuate or amplify local social vulnerability are of much need in policy making to reduce social vulnerability. This study explores the differential contributions of the constituent components of SoVI and investigates how the local indicator variables have evolved over time and across the Greater Houston metropolitan area in the USA using the geographically weighted principal components analysis. It found that the overall social vulnerability as measured by SoVI has exhibited persistent spatial patterns in the Greater Houston area since 1970; however, the spatial patterns of the SoVI are not equally constituted by the components of the SoVI. In particular, the high social vulnerability of suburban areas is mainly the result of one principal component that highly correlates with the percentage of mobile homes. It also found that the indicator variables of social vulnerability have exhibited great spatial heterogeneity and dependence at local scale, and they vary over time but persist on disadvantages in economic condition, mobility, and family structure.

Keywords: Social vulnerability; Principal components analysis; Geographically weighted principal components analysis; Spatial heterogeneity; Houston (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-04938-9

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