Social vulnerability score: a scalable index for representing social vulnerability in virtual community resilience testbeds

Incorporating social systems and phenomena into virtual community resilience testbeds is uncommon but becoming increasingly important. Social vulnerability indices are a convenient way to account for differential experiences and starting conditions of the population in resilience assessments. This paper proposes a scalable index, termed Social Vulnerability Score (SVS), to serve the purpose of testbed development. The SVS overcomes two important limitations of existing indices: it is constructed using an approach that does not decrease in validity with changing spatial resolution, and it only needs to be calculated for the geographic area of interest, instead of for the entire county thereby significantly reducing computational effort for testbed developers and users. The proposed SVS aggregates the ratio of a set of demographics from U.S. Census datasets at the desired location against their national average values. The resulting scores are mapped into five levels, called zones, ranging from very low vulnerability (zone 1) to very high (zone 5). The validity of the SVS was investigated through a regression analysis of flood outcomes in Lumberton, North Carolina caused by Hurricane Matthew in 2016. The resulting correlations between the SVS zones and post-disaster outcomes of household dislocation and home repair times match the social vulnerability theory. The paper concludes with a discussion of the advantages and limitations of using the SVS.