The Social Vulnerability Index (SoVI®)
SoVI® is a statistically derived comparative metric to illustrate the variability in capacity for preparedness, response, and recovery at county and subcounty levels of geography. Using census data, SoVI® synthesizes 32 different variables, using a principal components analysis and expert judgment, into a single composite value, which is then mapped to illustrate differences between places. Several factors consistently appear in the results of these analyses, including socioeconomic status, elderly, and gender; however, the relative importance of these factors is observed to be place specific. Since its inception, SoVI® has been used by emergency planners as part of their state hazard mitigation planning (South Carolina, California, and Colorado) and has been incorporated into a number of digital products including the National Oceanic and Atmospheric Administration’s Coastal Services Digital Coast.
http://sovius.org for more details and applications.
Baseline Resilience Indicator for Communities
A new composite indicator called the Baseline Resilience Indicator for Communities (BRIC) was introduced to measure community resiliency (Cutter et al., 2010). BRIC acknowledges that resilience is a multifaceted concept with social, economic, institutional, infrastructural, ecological, and community components. The composite indicator is calculated as the arithmetic mean of five subindexes related to social, economic, institutional, infrastructural, and community resilience; ecological resilience is not included in the 2010 formulation. Each subindex is normalized so that the final indicator varies between 0 and 1.
Cutter et al. (2010) proposed several applications of the proposed method to communities at different scales. An interesting case study relates to the spatial distribution of disaster resilience over 736 counties within FEMA Region IV (Figure 4.3). A second example deals with determining the resilience score of three metropolitan areas: Gulfport-Biloxi, Charleston, and Memphis. Both case studies show a clear ability to identify least-resilient areas at different geographic scales using an empirically based descriptive approach.