Social Vulnerability Index (SoVI) for Gulf Coast counties
Synopsis of Social Vulnerability
The concept of social vulnerability is theoretically framed by a multi-disciplinary litany of case studies and research on specific hazard events, their impacts,
and outcomes. Social vulnerability to hazards refers specifically to a lack of ability for individuals and communities to adequately prepare for, respond to, and
rebound from environmental hazards. The science of vulnerability is relatively recent, but theoretical links between pre-event socio-economics and general adverse
outcomes date back decades. One of the first operational measures of social vulnerability, Maloney’s (1973) Social Vulnerability of Indianapolis linked underlying
social characteristics with adverse health outcomes. Subsequently, scholars at the University of South Carolina and the University of Central Florida have driven
development of vulnerability science, empirical measurement of social vulnerability, and use of social vulnerability metrics for decision making, planning, and all
phases of emergency management practice.
Current SoVI Model
The Social Vulnerability Index (SoVI®) measures the social vulnerability of U.S. counties to environmental hazards. The index is a comparative metric that facilitates
the examination of the differences in social vulnerability among counties. SoVI® is a valuable tool for policy makers and practitioners because it graphically illustrates
the geographic variation in social vulnerability. It shows where there is 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.
The index synthesizes 29 socioeconomic variables, which the research literature suggests contribute to 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.
Raw Input Variables Used to Construct SoVI
| QASIAN |
Percent Asian |
| QBLACK |
Percent Black |
| QHISP |
Percent Hispanic |
| QNATAM |
Percent Native American |
| QAGEDEP |
Percent Population under 5 Years or 65 and Over |
| QFAM |
Percent Children Living in 2-Parent Families |
| MEDAGE |
Median Age |
| QSSBEN |
Percent Households Receiving Social Security Benefits |
| QPOVTY |
Percent Poverty |
| QRICH |
Percent Households Earning over $200,000 Annually |
| PERCAP |
Per Capita Income |
| QESL |
Percent Speaking English as a Second Language with Limited English Proficiency |
| QFEMALE |
Percent Female |
| QFHH |
Percent Female Headed Households |
| QNRRES |
Nursing Home Residents Per Capita |
| QNOHLTH |
Percent of Population without Health Insurance |
| QED12LES |
Percent with Less than 12th Grade Education |
| QCVLUN |
Percent Civilian Unemployment |
| PPUNIT |
People per Unit |
| QRENTER |
Percent Renters |
| MDHSEVAL |
Median Housing Value |
| MDGRENT |
Median Gross Rent |
| QMOHO |
Percent Mobile Homes |
| QEXTRCT |
Percent Employment in Extractive Industries |
| QSERV |
Percent Employment in Service Industry |
| QFEMLBR |
Percent Female Participation in Labor Force |
| QNOAUTO |
Percent of Housing Units with No Car |
| QUNOCCHU |
Percent Unoccupied Housing Units |
| QHSEBRDN |
Percent of All Households Spending More than 40% of Their Income on Housing Expenses |