1. Introduction
Each year, disasters affect millions of people and cause billions of dollars in damages worldwide [
1]. Between 2008 and 2018, 663 events were classified as disasters by the Centre for Research on the Epidemiology of Disasters (CRED). Floods or storms caused 476 (72%), which accounted for over 65% of disaster-related economic losses. As the frequency and intensity of extreme weather events are expected to increase due to climate change [
2,
3], there is a growing need to understand the factors that affect resilience to these events, and to incorporate these factors into planning and decision-making tools for emergency management, recovery, and risk reduction.
One such tool is the Center for Disease Control and Prevention’s (CDC’s) Social Vulnerability Index (SVI) database [
4]. This tool uses socioeconomic and demographic data from the U.S. Census to identify areas at higher risk from natural hazards and is used in planning hazard mitigation efforts and coordinating emergency response operations [
5]. The SVI is intended as a tool for practitioners at all stages of the disaster management cycle, and, thus, should be useful to decision-makers coordinating limited resources in the immediate aftermath of a large disaster event. One such consideration is the deployment of emergency shelter and assistance for the repair of damaged housing. In theory, areas with higher levels of social vulnerability will experience greater levels of damage to housing, and practitioners may prioritize response efforts in these areas. Despite its widespread use, there is little empirical research validating the SVI with data from specific natural hazard events.
Social capital is another factor that has been shown to be important to resilience. Unlike social vulnerability, there has not been much work to develop appropriate indicator variables and combine them into a tool for resilience assessment. However, a recent study [
6] develops a social capital index (SoCI) intended for resilience assessment and disaster risk management, which uses publicly available data to calculate measures of social capital in U.S. counties.
More research is needed to assess the validity of the selected indicators at all phases of the disaster cycle, to ensure that tools such as the SVI and SoCI are accurate and reliable. This study uses housing damage assessments in Florida and Puerto Rico from Hurricanes Irma and Maria to assess the ability of the SVI and SoCI to predict hazard impacts at the county scale. Using aerial imagery, we also measure housing recovery in 36 census tracts in Puerto Rico and use this data to evaluate how well the SVI and SoCI explain variability in recovery outcomes.
2. Background
The natural hazards literature contains many definitions of vulnerability, which can be generalized as “the potential for a loss” [
7]. Throughout much of the 20th century, hazards research viewed vulnerability as a purely physical phenomenon. It was not until the later part of the century that researchers began to recognize that social and economic factors contribute to vulnerability, as well [
8,
9]. The hazards of place model, developed by Susan Cutter, groups community vulnerability into biophysical and social vulnerability. Biophysical vulnerability arises from a community’s geographic context and the probability that it will be exposed to a hazard. Social vulnerability arises from the community’s “social fabric”, which includes socioeconomic and demographic characteristics that affect community response, coping, and recovery from a hazard event [
7]. Several indices exist using census data to measure vulnerable populations and create consistent metrics of social vulnerability with which to compare communities across a broader geographic context. Emergency practitioners use these indices for disaster planning, response, and recovery operations. Hazards researchers also use them to improve the understanding of social vulnerability and how different communities experience disasters [
10].
Cutter et al. [
11] developed an index of social vulnerability, known as the SoVI, which uses 11 variables from the U.S. Census to rank the social vulnerability of U.S. counties. Validation of the SoVI compared the number of presidential disaster declarations by county in the 1990s and found no significant difference between the number of declarations in counties with high SoVI scores and those with low scores. This validation method may be misleading, however, as a presidential disaster declaration does not imply a specific minimum level of impact, and there may be political pressure for the president to make such a declaration [
12,
13]. Sherrieb et al. [
14] used the SoVI to develop a community resilience index to show that counties in Mississippi with higher levels of social vulnerability scored lower in measures of resilience, however, the community resilience index was not validated with disaster impact or recovery data. A study by Flannagan et al. [
9] developed a similar social vulnerability index, dubbed the SVI, which measures social vulnerability across four distinct themes. In this study, the authors used data from New Orleans following Hurricane Katrina to validate the index. This analysis found that drowning victims were disproportionately elderly, and areas with high levels of social vulnerability were less likely to have returned to pre-Katrina population levels than areas with low levels of social vulnerability. The SVI methodology developed in the Flannagan study is used by the CDC to develop its SVI database, which is now widely used by emergency managers [
5].
The SVI is calculated using 15 indicators from the U.S. Census Bureau’s American Community Survey. These indicators are classified into four themes representing different aspects of social vulnerability: (1) socioeconomic status, (2) household composition and disability, (3) minority status and language, and (4) housing and transportation. The socioeconomic status theme measures per-capita income, poverty, unemployment, and the percentage of the population without a high school diploma. Communities that rank high in this theme are less likely to have resources available to prepare for and recover from a disaster, and any losses experienced from a hazard event are more likely to represent a greater proportion of overall household assets [
9]. The household composition and disability theme measures the percentage of the population age 65 and older, percentage of the population age 17 and younger, percentage of the population age 5 and older with a disability, and the percentage of single-parent households. More vulnerable communities, as ranked by this theme, are more likely to require financial support or other assistance during a disaster [
9]. Minority status and language measures the percentage of the population that is not non-Hispanic-white, and the percentage of the population that does not speak English at least well. Discrimination against racial and ethnic minorities has resulted in less economic development in these communities, and hazard mitigation projects which are selected based on economic benefit are more likely to go to more developed areas, leaving minority communities more vulnerable. Additionally, people with limited English proficiency may find it difficult to receive hazard alerts or to communicate with government agencies and aid organizations for assistance [
9]. The housing and transportation theme measures the percentage of housing units classified as multi-unit, percentage of housing units that are mobile homes, the percentage of housing units classified as crowded, percentage of the population living in group quarters, and the percentage of households with no vehicle available. These factors may affect evacuation capabilities, and structures in these categories are less likely to be able to withstand the impacts of severe hazards such as hurricanes or earthquakes [
9].
Social capital is another factor that has been shown to be important in community disaster recovery [
15,
16,
17]. Social capital is defined as “social networks and the norms of reciprocity and trustworthiness that arise from them” [
18]. Communities with strong social networks are more likely to work together during the recovery process and are more likely to have connections with government agencies and aid organizations that can provide resources for recovery [
15]. Social capital can also reduce vulnerability as strong social networks can facilitate the sharing of hazard warnings allowing people more time to prepare or make the decision to evacuate [
19]. Communities with higher levels of social capital may also be more successful in coordinating and sharing resources in preparing for disasters (e.g., labor, emergency supplies, boarding up windows and doors) [
19]. Although there is not yet widespread agreement on how best to operationalize social capital, researchers have recognized the need to develop quantitative measures that can be used by emergency managers and disaster researchers [
10,
15,
17,
20].
There have been a few attempts to develop indices of social capital. In,
Bowling Alone: The Collapse and Revival of the American Community [
18], Robert Putnam develops a state-level index of social capital comprised of measures of civic and political participation. Using this index, Putnam found that high levels of social capital were associated with several positive community characteristics, including better child welfare and educational performance, lower murder and mortality rates, and greater tolerance for gender/racial equality and civil liberties [
18]. Rupasingha et al. [
21] developed a county-level index of social capital and used this to identify socioeconomic and demographic indicators of social capital production. These indicators can be easily calculated using publicly available data from the U.S. Census Bureau, allowing for a consistent approach to measuring social capital across U.S. counties. Building on this work, Kyne and Aldrich [
6] developed an index of social capital (SoCI) to assess community disaster resilience. This index was validated using data from historical disaster impacts in the United States and results showed that higher levels of social capital were associated with fewer fatalities, but greater levels of damage. This damage assessment included commercial property losses, which may have affected this finding.
The SoCI measures social capital using 19 indicators calculated with data from the U.S. Census Bureau and the Environmental Systems Research Institute (ESRI). These variables represent three types of social capital: bonding, bridging, and linking. Bonding social capital is formed through strong connections between family members, close friends, and neighbors [
18]. These types of relationships tend to exhibit a high level of “homophily”, or similarity in terms of race, ethnicity, or language. The indicators of bonding social capital used in the SoCI are direct measures of homophily within the study area, which are used as a proxy for bonding social capital. Weaker ties between individuals from different groups serve to connect groups that may not otherwise interact [
18]. These types of relationships are more likely to be formed in communities where there are more opportunities for people from different groups to interact [
19]. Bridging indicators in the SoCI measure the number of organizations within a county, such as churches or charitable organizations, that may facilitate these types of interactions. Linking social capital is characterized by vertical relationships across a social hierarchy, such as between residents and government officials [
15]. The SoCI captures linking social capital based on the percentage of the labor force working for government agencies, the percentage of the population who are eligible to vote, and the percentage of the population that has participated in political activities.
Validation using empirical research is needed to ensure indicators of social vulnerability and social capital are appropriately and effectively put into practice. Using case studies in Florida and Puerto Rico from the 2017 U.S. hurricane season, this study assesses the ability of indicators of social vulnerability and social capital to predict impacts to and recovery outcomes of housing infrastructure.
Research questions:
6. Discussion
The SVI is often regarded as a valuable tool for assessing disaster resilience and guiding emergency management decisions; however, there has been little empirical research evaluating the use of the SVI as a predictive tool. The results of this study support the use of the SVI as a planning tool and highlight how the various themes impact its effectiveness. In both case studies, the overall SVI significantly improved the prediction of housing impacts in the study area over that of the peak wind gust bivariate model and was positively associated with housing damage. This means that counties with higher levels of social vulnerability experienced more per capita damage than less socially vulnerable communities exposed to a hazard of similar magnitude.
The SVI measures social vulnerability across four themes that capture different aspects of vulnerability that may be relevant in different phases of the disaster recovery process. Using the housing impact data, we also assessed the ability of each SVI theme to predict housing impacts. As expected, SVI theme 1 (socioeconomic status) significantly improved the prediction of housing damages over the gust-only model and was positively associated with housing damage. Communities that rank high in SVI theme 1 have higher rates of poverty and unemployment and lower levels of income and educational attainment. In both case studies, counties with higher SVI theme 1 scores had more per capita housing damage than similarly exposed counties with lower scores. Additionally, SVI theme 1 performed better than the overall SVI in predicting housing damages in both case studies.
SVI theme 2 captures vulnerability based on household composition. While some of the variables in this indicator, such as the percentage of the population that is elderly or disabled, may be related to an inability to make disaster preparations, we did not identify an expected relationship between this SVI theme and housing damage. This SVI theme was a statistically significant improvement to the gust-only model in the Florida case study, performing nearly as well as the overall SVI. However, when SVI theme 2 was added to the gust-only model in the Puerto Rico case study, there was not a statistically significant improvement in the amount of explained variance in housing damage.
We expected SVI theme 3 (minority status and language) to be positively correlated with housing impacts. This outcome did not occur in the Florida case study, where adding theme 3 to the gust-only model did not significantly increase the explained variance in the model. Although the sign on the coefficient was positive, its t-statistic showed that it was not significantly different from zero. Adding theme 3 to the gust-only model in the Puerto Rico case study, though, did significantly improve the prediction of housing damage. SVI theme 3 performed better in the Puerto Rico case study than the overall SVI.
Theme 4 includes mobile homes, and we expected a higher prevalence of mobile homes to be associated with greater levels of damage. However, the impact analysis in the study calculated damages in terms of real property losses, and mobile homes are often not considered to be real property but personal property. Because the focus of this paper was housing resilience, we did not consider personal property losses in our analysis. Theme 4 also includes the prevalence of multi-unit housing, but our data only include applicant information where the applicant was the property owner. These factors likely affected how SVI theme 4 contributed to the analysis. Despite these caveats, adding theme 4 to the gust-only model in the Florida case study significantly improved the prediction of impacts, and the coefficient was positive as expected. In the Puerto Rico case study, though, theme 4 did not significantly improve the gust-only model, and the coefficient for theme 4 was negative (although not statistically significant).
We also assessed the ability of the SoCI to predict housing impacts. Although studies have shown social capital to be an important factor of disaster resilience, the social capital index developed by Kyne and Aldrich is the first attempt to develop a set of quantitative indicators of social capital for resilience assessment and planning. Our study empirically evaluates the SoCI’s explanatory capability for variation in housing impacts. We expected counties with higher levels of social capital to report less per-capita damage than counties with less social capital exposed to similar wind gusts. The results of this study were mixed, however. In Puerto Rico, the overall SoCI, as well as the bonding and bridging SoCIs, were negatively associated with damages. The linking SoCI was positively correlated with damages; however, we do not believe that there is any logical relationship between the percentage of the labor force working for government agencies and housing damage. Rather, there are probably other underlying relationships not explored in this analysis that explain this correlation.
In Florida, each of the SoCI variables, except for the bonding SoCI, was positively associated with housing damage. These mixed results indicate that the SoCI is not a useful tool in predicting areas that are likely to experience greater levels of damage in a disaster. Future work should explore the relationship between social capital and disaster impacts in more detail. As discussed above, we modified some of the indicators that comprise the SoCI for this study. In their paper, Kyne and Aldrich explain the theory behind each of the indicators included the SoCI. Our modifications were not based on differences in theory; rather, we modified the indicators to align with the theories presented in the original paper more closely. Therefore, we do not believe that our variable modifications confound these results.
None of the indices assessed in this study were statistically significant in the recovery analysis. Because there are no publicly available datasets for tracking the disaster recovery process, resilience indices such as the SVI and SoCI have typically been developed and validated using only disaster impact data. While the variables selected for these indices are justified based on reviews of the literature and the underlying theory, empirical validation is necessary to ensure the resulting indices are useful for emergency managers and other practitioners who may rely on them. The results of this study suggest that the SVI and SoCI may need further development to ensure that they are applicable in resilience assessments focused on the recovery phase of the disaster cycle. Limitations of the methods used in this study may have also affected these results and are discussed below.
Limitations and Future Work
The impact assessment portion of this study looked at real property losses for owner-occupied households. It is possible that excluding renters and personal property losses from this study may have affected the results of the SVI assessment. Low-income households are more likely to rent rather than own their home. Therefore, the data used to calculate housing damage may have been biased toward higher-income households. Additionally, SVI theme 4 includes measures of multi-unit housing, which are often renter-occupied, and mobile homes, which are often categorized as personal property instead of real property. These categorizations may have also further biased the analysis towards less vulnerable populations.
There are also limitations to the recovery analysis presented in this study. We relied on historic aerial imagery from Google Earth to count blue roofs and develop our measures of housing recovery. The primary limitation is that focusing only on blue roofs does not consider other types of damage that may be more difficult to detect without more advanced image analysis techniques. There were also technical limitations to this method. The availability of historic imagery varies greatly depending on location, and some areas may be obscured by cloud cover. For these reasons, we were not always able to view the census tracts in the sample on the exact dates we chose for the study (20 March 2018 and 20 September 2019), and in some cases, we had to rely on imagery up to three months before or after the target date. Additionally, because Google Earth provides high-resolution imagery from a variety of sources captured from a variety of instruments, there are differences in the resolution and color spectrum in the imagery analyzed for this study, which increased the possibility of blue roof counting errors. To address these issues, future work should include funding to purchase aerial imagery of the study area with more consistent availability and visibility. More robust methodologies for recovery tracking in aerial imagery should also be incorporated.
As mentioned above, the mixed results from the analyses of the SoCI and SoCI sub-indices point to a need for more work in developing appropriate indicators of social capital. The variables in the SoCI, and other social capital indices that came before, have been selected based on theory and assumptions about how people form connections with one another. They have been validated with indicators that represent the expected outcomes of different levels of social capital. Future work should incorporate social network analysis, a standard method for studying social networks, to directly study the relationship between potential social capital indicators, and the structure of social networks in communities.
7. Conclusions
The purpose of this study was to evaluate the effectiveness of the SVI and SoCI as tools for resilience assessment. Using data from Florida and Puerto Rico from the 2017 hurricane season, we assessed each index’s ability to explain variation in levels of housing damage within the study area. We also assessed the ability of the indices to explain variation in levels of housing recovery in a sample of 36 census tracts two years after the landfall of Hurricane Maria.
Our results show that the SVI is an effective tool to explain variation in disaster impacts. We also show, by assessing the individual theme indices of the SVI, that the socioeconomic theme of the SVI is more effective at predicting variation in housing damage than any other theme, including the overall SVI. The impact assessment showed mixed results for the SoCI, and we believe that more work to validate the SoCI as a tool for resilience assessment should be done before it is adopted as a tool by practitioners. Neither the SVI, the SoCI, nor any of the sub-indices, were significant in the recovery assessment portion of this study. There were several limitations with this method as described above, which should be addressed in future work. However, these results highlight certain usefulness and limitations to these indices of which practitioners who rely on them should be aware.