SoVI | Guidance on Risk, Resilience, and Vulnerability Indices

Social Vulnerability Index for the United States (SoVI®)

Description

The Social Vulnerability Index for the United States (SoVI®) provides a comparative social vulnerability score, comprised of 29 socioeconomic variables that contribute to a reduction in a community’s ability to prepare for, respond to, and recover from hazards. SoVI® allows users to visualize (compare) the differences in social vulnerability among counties within their state and across the nation. The social vulnerability index was originally released in 2010 and last updated in 2019.

Analysis

The index combines 29 socioeconomic variables that research indicates affect a community’s ability to prepare for, respond to, and recover from hazards. The data, primarily from the U.S. Census Bureau, are processed by the Hazards Vulnerability and Resilience Institute at the University of South Carolina. The variables are standardized and analyzed using principal components analysis to create a set of optimized components. These components are adjusted so that positive values indicate higher vulnerability, and negative values indicate lower vulnerability. The components are then summed to produce a social vulnerability score for each county.

The SoVI® 2010-14 introduced updates based on advances in vulnerability science, including factors like family structure, language barriers, vehicle availability, medical disabilities, and healthcare access. These changes, first implemented in SoVI® 2006-2010, are continued in SoVI® 2010-14 and SoVI® 2019, incorporating data from the 2010 U.S. Decennial Census and the five-year American Community Survey estimates (2010-14 and 2015-19).

What does this index provide?

SoVI® provides a static view of vulnerability at a particular moment in time; this enables users to:

Compare the social vulnerability among counties within their state and within the nation.
Compare or track social vulnerability changes over time.
View a census tract or county’s ability to absorb loss and damages during a disaster.

For what level of government would this index be most useful?

Due to its level of granularity (census tract), this data should be useful at all levels of government:

Federal
Tribal (usability is dependent on the size of the tribal geography)
State
Regional (intrastate region)
County (for comparisons)

Context

Why was the index developed?

To measure social vulnerability to environmental hazards in the US.

Who is the data steward's intended audience?

Public Health Officials and Emergency Response Planners

How does the data steward envision that data be used?

SoVI® is a valuable tool for policymakers and practitioners because it graphically illustrates the geographic variation in social vulnerability.
It shows where there is an uneven capacity for preparedness and response and where resources might be used most effectively to reduce the pre-existing vulnerability.
SoVI® also is useful as an indicator in determining the differential recovery from disasters using empirically-based information.

What are the known limitations of this index?

Utilizes uniform formulas and variables across the coverage area; it does consider community-specific variables.
The interpretation is limited to the variable included in the analysis.
Does not include the prevalence or existence of hazards in the vulnerability equation, only social factors. Therefore, this index should be used in conjunction with hazard information.
Provides a single snapshot of a community. Multiple events and duration of events may further impact a community’s vulnerability at a given time.
The resulting index is only as relevant as the underlying datasets and the date at which the index was compiled. Further, the relevancy dates of the underlying datasets could be a limitation.

Variables

The index synthesizes 29 socioeconomic variables, which the research literature suggests contributes to a reduction in a community’s ability to prepare for, respond to, and recover from hazards. SoVI® data sources include primarily those from the United States Census Bureau. http://artsandsciences.sc.edu/geog/hvri/faq

In SoVI® 2019, seven significant components explain 73% of the variance in the data. These components include wealth; race and social status; age dependency (elderly); Hispanic ethnicity and people without health insurance; special needs populations; Native American populations; and service industry employment. Detailed information on these components can be found here in PDF format: https://www.sc.edu/study/colleges_schools/artsandsciences/centers_and_institutes/hvri/documents/sovi/sovi2019.pdf