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For most of the scientific disciplines associated with coastal and estuarine research, workforce representation does not match the demographics of communities we serve, especially for Black, Hispanic or Latino, and Indigenous peoples. This essay provides an overview of this inequity and identifies how a scientific society can catalyze representational, structural, and interactional diversity to achieve greater inclusion. Needed changes go beyond representational diversity and require an intentional commitment to build capacity through inclusivity and community engagement by supporting anti-racist policies and actions. We want to realize a sense of belonging on the part of scientists in society at large and enable research pursuits through a lens of social justice in service of coastal communities. Minimally, this framework offers an avenue for increased recruitment of individuals from more diverse racial and ethnic identities. More broadly, the mechanisms described here aim to create a culture in scientific societies in which social justice, driven by anti-racist actions, produces systemic change in how members of scientific societies approach, discuss, and address issues of inequity. We have written this essay for members of the coastal and marine science community who are interested in change. We aim to call in new voices, allies, and champions to this work.

Human exposure to floods continues to increase, driven by changes in hydrology and land use. Adverse impacts amplify for socially vulnerable populations, who disproportionately inhabit flood-prone areas. This study explores the geography of flood exposure and social vulnerability in the conterminous United States based on spatial analysis of fluvial and pluvial flood extent, land cover, and social vulnerability. Using bivariate Local Indicators of Spatial Association, we map hotspots where high flood exposure and high social vulnerability converge and identify dominant indicators of social vulnerability within these places. The hotspots, home to approximately 19 million people, occur predominantly in rural areas and across the US South. Mobile homes and racial minorities are most overrepresented in hotspots compared to elsewhere. The results identify priority locations where interventions can mitigate both physical and social aspects of flood vulnerability. The variables that most distinguish the clusters are used to develop an indicator set of social vulnerability to flood exposure. Understanding who is most exposed to floods and where, can be used to tailor mitigation strategies to target those most in need.

The occurrence of elevated temperatures within landfills is a challenging issue for landfill operators to detect and correct. Little is known regarding the causes of elevated temperatures (ETs) or the number of landfills currently operating under such conditions. Therefore, the goal of this research was to determine which landfills within Florida have been impacted by ETs, and to develop a more complete understanding of the factors that may lead to these landfills becoming elevated temperature landfills (ETLFs). Historical landfill gas wellhead data, waste deposition reports, and landfill site geometry were collected for 27 landfill cells through the Florida Department of Environmental Protection electronic document management system, OCULUS database and from landfill operators and owners. These data were evaluated to quantify the characteristics that result in landfills having ‘elevated’ temperatures. Gas data included landfill gas temperatures and methane, carbon dioxide, and balance gas content. Furthermore, landfill maps were created in ArcGIS to observe spatial distribution of ETs in landfills over time. Upon analysis of the landfill gas wellhead data, it was discovered that 74% of studied landfill cells had ET readings; regulatory limits specify a maximum allowable gas temperature of 55 °C (131 °F). It was discovered that 37% of landfill cells contained MSW ash; of these cells, 90% of them are considered ETLFs. ETLF cells are on-average double the site area and approximately 6 m deeper than the average non-ETLF cell. Furthermore, results suggest that heat propagation in most landfills is limited; however, heat propagation is possible if gas wells are turned off for an extended time period.

This study investigates evacuation behaviors associated with Hurricane Matthew in October of 2016. It assesses factors influencing evacuation decisions and evacuation departure times for Florida, Georgia, and South Carolina from an online survey of respondents. Approximately 62% of the Florida sample, 77% of the Georgia sample, and 67% of the South Carolina sample evacuated. Logistic regression analysis of the departures in the overall time period identified variability in evacuation timing, primarily dependent on prior experience, receipt of an evacuation order, and talking with others about the evacuation order. However, using four logistic regressions to analyze differences in departure times by day shows that the only significant variable across the three main days of evacuation was our proxy variable for evacuation-order times. Depending on the day, other variables of interest include number of household vehicles, previous hurricane experience, and receipt of an evacuation order. Descriptive results show that many variables are considered in the decision to evacuate, but results from subsequent analyses, and respondents’ comments about their experiences, highlight that evacuation orders are the primary triggering variable for when residents left.

Deconstructing causal linkages between place attributes and disaster outcomes at coarse scales like zip codes and counties is difficult because heterogeneous socio-economic characteristics ...

To date, there has been limited research conducted on disaster aid allocation across multiple regions and disasters within the United States. In addition, there is a paucity of research specifically connecting social indicators of vulnerability to public assistance grants aimed at restoring, rebuilding, and mitigating against future damages in disasters. Given these gaps, this article inquires as to whether the Federal Emergency Management Agency’s (FEMA’s) public assistance program is characterized by procedural inequities, or disparate outcomes for counties with more socially vulnerable populations. Specifically, this article analyzes county-level FEMA’s Public Assistance distribution following major disaster declarations, while controlling for damages sustained, population, household counts, and FEMA Region. Results indicate that FEMA’s Public Assistance program operates well when accounting only for disaster losses across the years, however, findings also show that county social conditions influence funding receipt. Although socioeconomic characteristics were significant drivers of assistance spending, additional vulnerability indicators related to county demographic and built environment characteristics were also important drivers of receipt. Cases of both procedural inequity and equity are highlighted, and implications for equitable disaster recovery are discussed along with recommendations.

Natural hazard impacts and resources allocated for risk reduction and disaster recovery are often inequitably distributed. New research is developing and applying methods to measure these inequities.

Background This study aims to explore the relationship between social vulnerability (SoVI)indicators (race/ethnicity, population structure, socioeconomic status, housing structure, and access/functional needs) with low birth weight (LBW) and preterm delivery (PTD) rates across the Southeastern United States. Methods Annual low birth weight and premature birth rates for all counties were collected between 2000 and 2015. LBW and PTD were recoded into two categories below (0) and above (1) the annual national average for each year. Multinomial logistic regression (MLR) was employed to conduct regression analysis to investigate the relationship. Results Twenty-six and twenty-four different social vulnerability indicators were influential in predicting low birth weight rates and preterm delivery across the SE United States from 2005–2015, respectively. Racial and ethnic variables were among the most frequent influential social vulnerability indicators of low birth weights. Like race and ethnicity, counties with low and medium house values have a higher likelihood of low LBW compared to counties with higher house values. Unlike LBW, race and ethnic characteristics influence PTD rates across the study area in different ways. Whereas LBW rates are driven up in counties with low/medium Hispanic populations compared to high percentage counties, PTD is more strongly associated with Black communities. Further, population structure and socioeconomic status indicators provide the most robust indication of counties more likely to have higher PTD than the national average. Conclusion Influential variables point toward a dire need to comprehensively understand the links between social vulnerability and LBW and PTD. Moving toward a comprehensive view of social vulnerability borne out of the hazards literature provides a more robust understanding of the drivers of adverse birth outcomes that has rarely been addressed in the literature.

Purpose To assess veteran-specific prostate cancer (PrCA) mortality-to-incidence ratios (MIR) in South Carolina's (SC) veteran population. Methods U.S. Veterans Health Administration electronic medical records from January 1999 to December 2015 identified 3,073 PrCA patients residing in 345 ZIP code tabulation areas (ZCTA) within SC. MIRs were calculated for all SC ZCTAs and by key patient- and neighborhood-level risk factors for PrCA. Comparisons between ZCTAs identified as part of a spatial cluster were compared with non-significant ZCTAs using t tests. Results The MIR was 0.17 overall, ranging from a low of 0.15 among Black men to 0.20 among White men. Among metropolitan ZCTAs, the MIR was 0.18 compared to 0.16 in non-metropolitan ZCTAs. Two clusters of higher-than-expected MIRs were found in the Upstate region. Conclusions Identification of spatial clusters of higher- or lower-than-expected MIRs allows for further testing of possible explanatory factors, and the capacity to target resources and policies according to greatest need.

Background: Individuals affected by disasters are at risk for adverse mental health sequelae. Individuals living in the US Gulf Coast have experienced many recent major disasters, but few studies have explored the cumulative burden of experiencing multiple disasters on mental health. Objective: The objective of this study was to evaluate the relationship between disaster burden on mental health. Methods: We used data from 9,278 Gulf Long-term Follow-up Study participants who completed questionnaires on perceived stress, anxiety, depression, and post-traumatic stress disorder (PTSD) in 2011-2013. We linked 2005-2010 county-level data from the Spatial Hazard Events and Losses Database for the United States, a database of loss-causing events, to participant’s home address. Exposure measures included total count of loss events as well as severity quantified as property/crop losses per capita from all hazards. We used multilevel modeling to estimate odds ratios (OR) and 95% confidence intervals (CI) for each exposure-outcome relationship. Results: Total count of loss events was positively associated with perceived stress (ORQ4:1.40, 95% CI:1.21-1.61) and was inversely associated with PTSD (ORQ4:0.66, 95% CI 0.45-0.96). Total duration of exposure was also associated with stress (ORQ4:1.16, 95% CI 1.01-1.33) but not with other outcomes. Severity based on cumulative fatalities/injuries was associated with anxiety (ORQ4:1.31, 95% CI 1.05-1.63) and stress (ORQ4:1.34, 95% CI 1.15-1.57), and severity based on cumulative property/crop losses was associated with anxiety (ORQ4:1.42, 95% CI:1.12-1.81), depression (ORQ4:1.22, 95% CI:0.95-1.57) and PTSD (ORQ4:1.99, 95% CI:1.44-2.76). Significance: Higher natural hazard burden above and beyond individual disasters may increase risk for multiple adverse mental health outcomes.

Socially vulnerable communities experience disproportionately negative outcomes following natural disasters and underscoring a need for well-validated measures to identify those at risk. However, questions have surfaced regarding the factor structure, internal consistency, and generalizability of social vulnerability measures. A reliance on data-driven techniques, which are susceptible to sample-specific characteristics, has likely exacerbated the difficulty generalizing social vulnerability measures across contexts. This study sought to validate previously published structures of SoVI using confirmatory factor analysis (CFA). We fit CFA models of 28 sociodemographic variables frequently used to calculate a commonly used measure, the social vulnerability index (SoVI), drawn from the American Community Survey across 4162 census tracts in Florida. Confirmatory models generally did not support theory-driven pillars of SoVI that were previously used to study vulnerability in the New York metropolitan area. Modified models and alternative SoVI factor structures also failed to fit the data. Many of the input variables displayed little to no variability, limiting their utility and explanatory power. Taken together, our results highlight the poor generalizability of SoVI across contexts, but raise several important considerations for reliability and validity, as well as issues related to source data and scale. We discuss the implications of these findings for improved theory-driven measurement of social vulnerability.

Building disaster resilience is a stated goal of disaster risk reduction programs. Recent research emphasizes a need for a greater understanding of community disaster response and recovery capacity so that communities can absorb shocks and withstand severe conditions and progress through the recovery period more efficiently. Nepal, which is prone to a multitude of hazards and having recently experienced a large earthquake in 2015, provides a unique opportunity for exploring disaster resilience in the developing world context. To date, no study investigating community disaster resilience across the entire country of Nepal exists. This study quantifies disaster resilience at Nepal&rsquo, s village level, primarily using census data. Guided by the Disaster Resilience of Place (DROP) model, 22 variables were selected as indicators of social, economic, community, infrastructure, and environmental resilience. Community resilience was assessed for 3971 village development communities (VDCs) and municipalities while using a principal component analysis. Additionally, a cluster analysis was performed to distinguish spatial patterns of resilience. Analyses reveal differential community disaster resilience across the country. Communities in the capital city Kathmandu and in the western and far western Hill are relatively resilient. While the entire Tarai region, which holds the greatest proportion of Nepal&rsquo, s population, exhibits relatively low levels of resilience when compared to the rest of the county. The results from this analysis provide empirical evidence with the potential to help decision-makers in the allocation of scarce resources to increase resilience at the local level.

The US is exposed to myriad natural hazards causing USD billions in damages and thousands of fatalities each year. Significant population and economic growth during the last several decades have resulted in more people residing in hazardous places. However, consistent national-scale hazard threat assessment techniques reflecting the state of hazard knowledge are not readily available for application in risk and vulnerability assessments. Mapping natural hazard threats is the crucial first step in identifying and implementing threat reduction or mitigation strategies. In this study, we demonstrate applied GIS approaches for creating and synthesizing US hazard threat extents using publicly available data for 15 natural hazards. Individually mapping each threat enables empirically supported intervention development and the building of a Composite Hazard Index (CHI). Summarizing the hazard frequencies provides a novel representation of US hazardousness. Implementing cluster analysis to regionalize US counties based on their underlying hazard characteristics offers insight into hazard threats’ spatial (non-political) natures. The results indicate that the southeast, central plains, and coastal regions of the northeast had high hazard occurrence scores, whereas more moderate hazard scores were observed west of the continental divide. Furthermore, while no place is safe from hazard occurrence, identifying each region’s distinct “hazardousness” can support individualized risk assessments and mitigation intervention development.

International audience; We would like to thank the CDC colleagues for their Commentary in what constitutes the first comment on a paper published in the Annals in decades. We view critique as a strength of scientific exploration. The Commentary concludes with as statement agreeing with the findings and conclusions of our paper: “we recognize CDC SVI may not identify the most vulnerable populations in all applications and has not done so in the Rufat et al. study (…) we agree with the authors.”

Hurricane Irma made landfall in the Florida Keys as a Category 4 hurricane on September 10, 2017, and affected all counties in the state of Florida, causing 42 deaths and about $50 billion in damages so far. In addition, Hurricane Irma caused widespread power outages, flooding, and downed trees and power lines. Irma produced moderate rainfall across much of western and central Florida. Irma produced moderate storm surge in coastal areas from the Keys north through Naples and along the eastern coast from North of Miami all the way through the northern border with the state of Georgia. The chapter provides an overview of losses and impacts associated with Hurricane Irma, and through cascading and compounding effects how these impacts exacerbated and exposed even greater vulnerabilities. It outlines how the lessons learned can uncover future risks and vulnerabilities and improve planning for disaster resilience.

The evolving demographics, needs, and demands of society requires managers to be culturally competent in all phases of emergency management, especially when responding to disasters. A culturally incompetent manager and/or organization can increase a hazard's impact on often already vulnerable communities. Are cultural competencies lacking in emergency management education? Are these competencies as important as other highly regarded emergency management competencies? What are effective pedagogy and andragogy to create, implement, and evaluate cultural competency in emergency management higher education? As scholars work to advance the emergency management discipline, there is an increasing need for scholarship of teaching and learning research, especially regarding cultural competency. This article reviews the existing literature, as well as pedagogy from related disciplines, to identify gaps and provide recommendations for future research.

International audience; Social vulnerability models are becoming increasingly important for hazard mitigation and recovery planning, but it remains unclear how well they explain disaster outcomes. Most studies using indicators and indexes employ them to either describe vulnerability patterns or compare newly devised measures to existing ones. The focus of this article is construct validation, in which we investigate the empirical validity of a range of models of social vulnerability using outcomes from Hurricane Sandy. Using spatial regression, relative measures of assistance applicants, affected renters, housing damage, and property loss were regressed on four social vulnerability models and their constituent pillars while controlling for flood exposure. The indexes best explained housing assistance applicants, whereas they poorly explained property loss. At the pillar level, themes related to access and functional needs, age, transportation, and housing were the most explanatory. Overall, social vulnerability models with weighted and profile configurations demonstrated higher construct validity than the prevailing social vulnerability indexes. The findings highlight the need to expand the number and breadth of empirical validation studies to better understand relationships among social vulnerability models and disaster outcomes.

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity w...

The concept of disaster resilience has gained attention in political spheres and news outlets over the past few years, yet relatively few empirical measures of the concept exist. Furthermore, resea...

This session brings together academics, consultants, and government professionals to discuss ways marketing can help address needs in a natural disaster. Extreme weather events have increased the visibility of marketing’s importance in relief and recovery solutions well beyond disaster fundraising. Effective Rapid Response Marketing (RRM) is critical. The dynamic and rapidly increasing developments in technology from apps to social media to machine learning and text mining can help marketers develop better Rapid Response Marketing in any crisis situation.

Racial and socio-economic status (SES) disparities exist in prostate cancer (PrCA) incidence and mortality. Less is known regarding how geographical factors, including neighborhood social vulnerability and distance traveled to receive care, affect PrCA risk. The purpose of this research was to use the Veterans Administration Medical System, which provides a unique means for studying PrCA epidemiology among diverse individuals with ostensibly equal access to healthcare, to determine whether area-level characteristics influence PrCA incidence while accounting for individual-level risk factors. From the US Veteran’s Health Administration (VHA) electronic medical records (EMR) database from January 1999 to December 2015, we identified 3,736 PrCA patients and 104,017 cancer-free controls from South Carolina (SC). The VHA EMRs were linked to the US census which provided area-level factors. US census data were used to construct the Social Vulnerability Index which is a continuous composite measure of area-level vulnerability and was divided into tertiles for modeling purposes. Data were analyzed using a Bayesian multivariate conditional autoregressive model (CAR) which accounted for individual-level factors, area-level factors, spatial random effects, and autocorrelation, which were used to identify areas of higher- or lower-than-expected PrCA incidence after controlling for risk factors. As expected, after accounting for age (sixfold and 13-fold increases in men 40–50 years and > 50 years, respectively), race was an important risk factor, with threefold higher odds among Blacks in the fully adjusted model [ORadj 2.98 (2.77, 3.20)]. After accounting for all other factors, residing in a ZIP code tabulated areas (ZCTA) with the greatest level social vulnerability versus the lowest, least vulnerable ZCTA’s, increased PrCA risk by 39% [ORadj 1.39 (1.11, 1.75)]. While accounting for known risk factors for PrCA, including age, race, and marital status, we found geographic areas in SC characterized by higher than average social vulnerability with higher rates of incident PrCA among veterans. Outreach for screening, education, and care coordination may be needed for veterans in these areas.

This paper traces the historic development of flood risk and the antecedent conditions that contributed to the catastrophic consequences in central South Carolina as the result of the 2015 ...

The Social Vulnerability Index (SoVI) includes multiple socioeconomic and demographic indicators related to the risk of obesity. However, it is uncertain how the SoVI input variables empirically affect the individual-level risk of obesity or the mechanisms contributing to the condition via the contextual built environment. This study examines the influence of social vulnerability on individual obesity through county-level built environments, using both individual- and county-level data. The total sample size was 204,610 respondents from 205 counties in the US. We employed two-level structural equation modeling to test the hypothesized relationships. The results indicate that counties with high index of Hispanics, African Americans, females, single-parent families, and residents without health insurance were all linked to higher percentages of fast food restaurants and reduced access to exercise opportunities. Furthermore, counties with higher percentages of older adults and population with special needs were positively related to obesity, while wealthier counties and those with larger Asian populations were negatively related to obesity. Our results demonstrate that SoVI variables may be a quality substitute for typical needs assessments, not only for emergency preparedness but also for health prevention.

Recent decades have witnessed an escalation in the social, economic, and ecological impacts of wildfires worldwide. Wildfire losses stem from the complex interplay of social and ecological forces at multiple scales, including global climate change, regional wildfire regimes altered by human activities, and locally managed wildland-urban interface (WUI) zones where homes increasingly encroach upon wildland vegetation. The coupled nature of the human-ecological system is precisely what makes reducing wildfire risks challenging. As losses from wildfire have accelerated, an emerging research and management objective has been to create fire-adapted communities where ecologically functional levels of wildfire are preserved but risks to human lives and property are minimized. Realizing such a vision will require widespread and decentralized action, but questions remain as to when and how such a transformation could take place. We suggest that the period following a destructive wildfire may provide a “hot moment” for community adaptation. Drawing from literature on natural hazard vulnerability, disaster recovery, and wildfire ecology, this paper proposes a linked social-ecological model of community recovery and adaptation after disaster. The model contends that changes during post-wildfire recovery shape a community's vulnerability to the next wildfire event. While other studies have highlighted linked social-ecological dynamics that influence pre-fire vulnerability, few studies have explored social-ecological feedbacks in post-fire recovery. This model contributes to interdisciplinary social science research on wildfires and to scholarship on community recovery by integrating hazard vulnerability reduction with recovery in a cyclical framework. Furthermore, it is adaptable to a variety of hazards beyond wildfire. The model provides a basis for future empirical work examining the nature and effectiveness of recovery efforts aimed at long-term vulnerability reduction.

There is increasing policy and research interest in disaster resilience, yet the extant literature is still mired in definitional debates, epistemological orientations of researchers, and differences in basic approaches to measurement. As a consequence, there is little integration across domains and disciplines on community resilience assessment, its driving forces, and geographic variability. Using US counties as the study unit, this paper creates an empirically-based resilience metric called the Baseline Resilience Indicators for Communities (BRIC) that is both conceptually and theoretically sound, yet, easy enough to compute for use in a policy context. A common set of variables were used to measure the inherent resilience of counties in the United States according to six different domains or capitals as identified in the extant literature – social, economic, housing and infrastructure, institutional, community, and environmental. Data were from public and freely accessible data sources. Counties in the US Midwest and Great Plains states have the most inherent resilience, while counties in the west, along the US-Mexico border, and along the Appalachian ridge in the east contain the least resilience. Further, it was found that inherent resilience is not the opposite of social vulnerability, but a distinctly different construct both conceptually and empirically. While understanding the overall variability in resilience, the BRIC is easily deconstructed to its component parts to provide guidance to policy makers on where investments in intervention strategies may make a difference in the improvement of scores. Such evidence-based research has an opportunity to influence public policy focused on disaster risk.

Social Vulnerability Index (SoVI) was applied to Greater Lisbon (Portugal). Based on the concepts used for the SoVI assessments in the US, 46 variables representing social vulnerability of the 149 civil parishes of Greater Lisbon were chosen. Thirty-eight variables were selected after application of correlation tests. They were standardized, and a Principal Component Analysis and a Varimax rotation were applied to them. Seven factors were extracted using the Kaiser criterion, which explain 79.5% of the variance, and the SoVI scores were then mapped using a standard deviation classification. Twelve of the 149 civil parishes of Greater Lisbon have a very high social vulnerability and 24 of them have a high social vulnerability. The map of SoVI was then integrated with susceptibility maps of earthquakes, floods, flash floods, landslides, tsunami, and coastal erosion, thus delineating risk zones. Twenty-two civil parishes of Greater Lisbon have a very high risk; among them, 17 belong to Lisbon Municipality, four belong to Loures Municipality, and one belongs to Vila Franca de Xira Municipality. Finally, exposed population was considered and combined with risk zones map in order to assess the number of people being potentially exposed to risk and their location.

There are many index-based approaches for assessing vulnerability to socio-natural hazards with differences in underlying theory, indicator selection and aggregation methodology. Spatially explicit output scores depend on these characteristics and contrasting approaches can therefore lead to very different policy implications. These discrepancies call for more critical reflection on index design and utility, a discussion that has not kept pace with the impetus for vulnerability assessments and respective index creation and application following the Hyogo Framework for Action 2005–2015. Comparing index outputs is an effective approach in this regard. Here, the Social Vulnerability Index (SoVI®) and the vulnerability component of the Global Delta Risk Index (GDRI) are applied at census tract level in the Mississippi Delta and visually and quantitatively compared. While the SoVI® is grounded in the hazard/risk research paradigm with primarily socio-economic indicators and an inductive principal component methodology, the GDRI incorporates advancements from sustainability science with ecosystem-based indicators and a modular hierarchical design. Maps, class rank changes, and correlations are used to assess the convergence and divergence of these indexes across the delta. Results show that while very different theoretical frameworks influence scores through indicator selection, methodology of index calculation has an even greater effect on output. Within aggregative methodology, the treatment of inter-indicator correlation is decisive. Implications include the need for an increased focus on index methodology and validation of results, transparency, and critical reflection regarding assessment limitations, as our results imply that contradictory risk reduction policies could be considered depending on the assessment methodology used.

Social vulnerability emphasizes the different burdens of disaster losses within and between places. Although China continuously experiences devastating natural disasters, there is a paucity of research specifically addressing the multidimensional nature of social vulnerability. This article presents an initial study on the social vulnerability of the Yangtze River Delta region in China. The goal is to replicate and test the applicability of the place-based Social Vulnerability Index (SoVI®) developed for the United States in a Chinese cultural context. Twenty-nine variables adapted from SoVI® were collected for each of the 134 analysis units in the study area. Using principal components analysis, six factors were identified from the variable set: employment and poverty, education, poor housing quality, minorities, family size, and housing size—factors similar to those identified for the United States. Factor scores were summed to get the final SoVI® scores and the most and least vulnerable study units were identified and mapped. The highest social vulnerability is concentrated in the southern portions of the study area—Jingning, Suichang, Yunhe, Lanxi, Pan’an, and Shengsi. The least socially vulnerable areas are concentrated southwest, west, and northwest of Shanghai. Limitations of replication are discussed along with policy-relevant suggestions for vulnerability reduction and risk mitigation in China.

Local and regional hazard management initiatives undertake loss and impact studies to prioritize funds for future mitigation programs. However, hazard loss records typically include only direct damages to property and crops. More flexible measures of hazard losses and impacts are needed to allow comparisons across local places and different time frames, as one mechanism for prioritizing disaster aid and mitigation resources. This study introduces a relative loss ratio at the county level for the US. All losses from geophysical, meteorological, and hydrologic hazards are collected for the period 1980–2009. The loss data are normalized by a measure of economic capacity calculated using county and state employment data and state Gross Domestic Product estimates. A statistical regionalization technique is utilized to identify hazard regions of the US which consistently suffered heavy losses in comparison to their economic capacities. For the nation, the average relative loss ratio is 0.15% of the US GDP, suggesting that overall relative impact of disasters has not been that large. However, downscaling to regional or county levels shows a different result with the relative impact exceeding 10% of county GDP for many places, and more than 3% of regional GDP in the central US and the Gulf Coast. While the national capacity to absorb and recover from disasters is quite high, local capacities are often exceeded by singular large events or the cumulative impacts of smaller events, necessitating the influx of external aid to help these places recover from hazards.

While flood risk management planning in the United States has focused on flood control structures designed to protect the economic value of property, it has consistently undervalued other social impacts associated with flooding. The US Army Corps of Engineers (USACE) recently initiated research aimed at understanding how to incorporate social characteristics into the measures currently utilised in flood control project evaluation and consideration. This paper proposes a methodology for incorporating a known measure of social vulnerability, the Social Vulnerability Index (SoVI), into USACE civil works planning. Using the USACE South Atlantic Division as the study area, this paper evaluates eight different variations of the social vulnerability metric and their potential deployment in USACE projects. Each formulation is compared with the original-computed SoVI as a means to test its spatial and statistical sensitivity, including an assessment of each variant's robustness, reducibility, scalability, and transferability. Results indicate that while it is possible to create simplified, yet robust, versions of SoVI for individual places, such ‘lite’ metrics tend to fall short in areas of scalability and transferability in relation to the original SoVI formulation.

Continued population growth and development in exposed areas across Coastal Louisiana has created a new geography of hazards and disasters within the coastal zone. Increasing storm frequencies coupled with sea level rise will undoubtedly intensify the intersection between flood hazards and coastal residents. Accordingly, the baseline (inherent) capacity of places to adequately prepare for and rebound from disaster events will be negatively impacted. This paper summarizes the value of incorporating research-based techniques into a non-structural assessment of flood vulnerability within the Northshore Region of Louisiana. The exploratory nature of the methodology employed in this study was focused on determining the value of non-structural measures of vulnerability in mitigation planning and the role of research in evidence-based decision support for public officials. The outcome of the study highlighted new perspectives for measuring vulnerability within a policy environment, offering community officials a more robust understanding of the dynamic intersection of the physical threats, social vulnerability, and economic components of flood risk. This knowledge is currently being used by decision makers in the region to cultivate enhanced mitigation tactics that have traditionally been structurally focused. By incorporating the biophysical, economic, and social vulnerability into a qualitative “place” vulnerability matrix for the study area, the authors have been able to gain a more robust understanding of the flood risks across the region. By integrating this new understanding of risk into potential mitigation strategies, planning for risk reduction expenditures can more appropriately consider the drivers of place-specific vulnerability.

In the United States, direct losses from natural hazards are on the rise with hurricanes, flooding, and severe storms contributing about three quarters of the total damages. While losses from severe storms have been stable over the past fifty years, hurricane and flood losses have tripled. Per capita losses are also increasing showing that impacts outpace population growth with high per capita losses occurring largely in the Southeast and Midwest. If the loss escalation of the past two decades continues into the future, then direct losses of $300 to $400 billion within a single decade are possible. In order to reverse this trend, sustainable development, vulnerability reduction, and hazard mitigation must become priorities and current loss reduction efforts need to be evaluated and re-assessed in terms of their effectiveness. These conclusions are drawn from the analysis of spatial and temporal trends in direct losses from natural hazards using SHELDUS TM data from 1960 through 2009. Loss data are adjusted for inflation, population, and wealth to capture both trends in total losses and per capita losses. The loss data are then compared to disaster-related federal government and private insurance expenditures.

The southern United States is no stranger to hazard and disaster events. Intense hurricanes, drought, flooding, and other climate-sensitive hazards are commonplace and have outnumbered similar events in other areas of the United States annually in both scale and magnitude by a ratio of almost 4:1 during the past 10 years. While losses from climate-sensitive hazards are forecast to increase in the coming years, not all of the populations residing within these hazard zones have the same capacity to prepare for, respond to, cope with, and rebound from disaster events. The identification of these vulnerable populations and their location relative to zones of known or probably future hazard exposure is necessary for the development and implementation of effective adaptation, mitigation, and emergency management strategies. This paper provides an approach to regional assessments of hazards vulnerability by describing and integrating hazard zone information on four climate-sensitive hazards with socioeconomic and demographic data to create an index showing both the areal extent of hazard exposure and social vulnerability for the southern United States. When examined together, these maps provide an assessment of the likely spatial impacts of these climate-sensitive hazards and their variability. The identification of hotspots—counties with elevated exposures and elevated social vulnerability—highlights the distribution of the most at risk counties and the driving factors behind them. Results provide the evidentiary basis for developing targeted strategic initiatives for disaster risk reduction including preparedness for response and recovery and longer-term adaptation in those most vulnerable and highly impacted areas.

The historical disparities in the socio-demographic structure of New Orleans shaped the social vulnerability of local residents and their responses to Hurricane Katrina and its aftermath. These disparities, derived from race, class, gender, and age differences, have resulted in the uneven impact of the catastrophe on various communities in New Orleans, and importantly, their ability to recover. This article examines how the pre-existing social vulnerabilities within New Orleans interacted with the level of flood exposure to produce inequities in the socio-spatial patterns of recovery. Utilizing a combination of statistical and spatial approaches, we found a distinct geographic pattern to the recovery suggesting that the social burdens and impacts from Hurricane Katrina are uneven—the less flooded and less vulnerable areas are recovering faster than tracts with more vulnerable populations and higher levels of flooding. However, there is a more nuanced story, which suggests that it is neighborhoods in the mid-range of social vulnerability where recovery is lagging. While private resources and government programs help groups in the high and low categories of social vulnerability, the middle group shows the slowest rates of recovery. Further, it appears that the congressionally funded State of Louisiana Road Home Program (designed to provide compensation to Louisiana’s homeowners who suffered impacts by Hurricanes Katrina and Rita for the damage to their home) is not having a significant effect in stimulating recovery within the city.

The pace of disaster recovery varies considerably from one place to another. Even when places suffer impacts from the same event, recovery studies often lack the spatial and temporal resolution to ...

Purpose of the Study We define, map, and analyze geodemographic patterns of socially and medically vulnerable older adults within the tri-county region of South Florida. Design and Methods We apply principal components analysis (PCA) to a set of previously identified indicators of social and medical vulnerability at the census tract level. We create and map age-stratified vulnerability scores using a geographic information system (GIS), and use spatial analysis techniques to identify patterns and interactions between social and medical vulnerability. Results Key factors contributing to social vulnerability in areas with higher numbers of older adults include age, large household size, and Hispanic ethnicity. Medical vulnerability in these same areas is driven by disease burden, access to emergency cardiac services, availability of nursing home and hospice beds, access to home health care, and available mental health services. Age-dependent areas of social vulnerability emerge in Broward County, whereas age-dependent areas of medical vulnerability emerge in Palm Beach County. Older-adult social and medical vulnerability interact differently throughout the study area. Implications Spatial analysis of older adult social and medical vulnerability using PCA and GIS can help identify age-dependent pockets of vulnerability that are not easily identifiable in a populationwide analysis; improve our understanding of the dynamic spatial organization of health care, health care needs, access to care, and outcomes; and ultimately serve as a tool for health care planning.

Introduction. Determining spatial patterns of cancer risk from air toxics is important to reduce environmental health disparities that cause cancer in areas experiencing disparate environmental exp...

Introduction. This study assessed spatial variations in cancer risk and cancer outcomes (incidence and mortality) by racial and socioeconomic composition in South Carolina. Methods. Cancer risk dat...

(2006). The Long Road Home: Race, Class, and Recovery from Hurricane Katrina. Environment: Science and Policy for Sustainable Development: Vol. 48, No. 2, pp. 8-20.