Eric Tate

Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis

Social vulnerability indices have emerged over the past decade as quantitative measures of the social dimensions of natural hazards vulnerability. But how reliable are the index rankings? Validation of indices with external reference data has posed a persistent challenge in large part because social vulnerability is multidimensional and not directly observable. This article applies global sensitivity analyses to internally validate the methods used in the most common social vulnerability index designs: deductive, hierarchical, and inductive. Uncertainty analysis is performed to assess the robustness of index ranks when reasonable alternative index configurations are modeled. The hierarchical design was found to be the most accurate, while the inductive model was the most precise. Sensitivity analysis is employed to understand which decisions in the vulnerability index construction process have the greatest influence on the stability of output rankings. The deductive index ranks are found to be the most sensitive to the choice of transformation method, hierarchical models to the selection of weighting scheme, and inductive indices to the indicator set and scale of analysis. Specific recommendations for each stage of index construction are provided so that the next generation of social vulnerability indices can be developed with a greater degree of transparency, robustness, and reliability.

Uncertainty Analysis for a Social Vulnerability Index

Indexes have gained favor over the past decade as a tool to measure social vulnerability to hazards. Numerous index designs have been put forward, yet we still know very little about their reliability. This research investigates the methods of social vulnerability index construction, examining decisions related to indicator selection, scale of analysis, measurement error, data transformation, normalization, and weighting. Each of these stages is imbued with uncertainty due to choices made by the index developer. The study applies Monte Carlo–based uncertainty analysis to assess and visualize uncertainty for a hierarchical social vulnerability index. Confidence limits are computed for the index rankings, leading to a finding of a high magnitude of uncertainty. The performance of the index compared to alternative configurations is strong in some places but statistically biased in about a third of the census tracts. The variability of index rankings is also assessed, indicating that index precision decreases with increasing vulnerability. Uncertainty analysis provides a useful, yet largely unapplied stage of index production that highlights places where the model is most reliable. If applied to the creation of social vulnerability indexes, output metrics can be produced with a greater degree of precision, transparency, and credibility.

Integrated Multihazard Mapping

A primary goal of the US Disaster Mitigation Act of 2000 is to slow the increase in disaster losses by emphasizing a proactive approach focusing on predisaster hazard mitigation, rather than postdisaster relief. The legislation requires local communities to produce hazard-mitigation plans that include multihazard maps, signifying a de facto prioritization of mitigation dollars on the basis of areas with the greatest vulnerability. However, there is little formal or practical guidance for communities on how to produce such maps. We propose a methodology for hazard-vulnerability assessments using multihazard mapping, where hazard frequency is a measure of risk, historical dollar losses are a proxy for infrastructure impact or exposure, and the Social Vulnerability Index (SoVI) evaluates human vulnerability. Using a test case of one county, Charleston, South Carolina, a geographic information system spatially combined these dimensions of vulnerability across multiple hazards. The resulting maps provide a tool for hazard-mitigation planning, which contains an initial screening element to highlight zones of highest multihazard vulnerability. The approach helps to generate a view of not just what is at risk, but who is at risk, and where, thus enhancing the implementation of targeted impact-reduction strategies.

Integrated Hazards Mapping Tool

The Disaster Mitigation Act of 2000 formally establishes a national program for pre-disaster mitigation. As part of the mitigation planning effort, state and local governments are required to perform assessments of hazards vulnerability, including the development of multi-hazard maps. However, the number of communities possessing the technology, expertise, and time to create multi-hazard vulnerability maps is limited due to technical and resource constraints. The use of Internet mapping technology has the potential to overcome these hurdles because it does not require users to possess a high level of GIS expertise or costly software, and it standardizes the vulnerability mapping approach. This article describes the Integrated Hazards Assessment Tool, a web-based multi-hazard vulnerability mapping application for local and state hazard mitigation practitioners in the state of South Carolina. The initial findings suggest the application holds strong potential as a viable decision support tool for hazard mitigation planning.

Uncertainty and Sensitivity Analysis of the HAZUS-MH Flood Model

AbstractEstimating flood losses for hazard mitigation planning is an increasingly important aspect of flood risk management. The loss estimation process typically includes sequential analysis of the flood hazard, building characteristics, structural damage, and economic loss. However, the reliability of economic loss estimates from the modeling process is not well understood. This research applies the method of global sensitivity analysis to the HAZUS-MH flood loss estimation model. The work is guided by two research questions: what is the uncertainty of the flood loss estimates, and what is the relative contribution of each model component to the overall uncertainty? Based on the uncertainty analysis, the estimate at the upper bound of loss distribution was found to be a factor of three higher than the lower-bound estimate. The sensitivity analysis revealed the choice of digital elevation data to be the most influential modeling stage. Recommendations are provided so that HAZUS-MH users can develop more ...

Flood recovery and property acquisition in Cedar Rapids, Iowa

Voluntary property acquisitions are playing an increasingly prominent role in the aftermath of US flood disasters, as policy tools for community recovery and hazard mitigation. Following historic flooding in 2008, the City of Cedar Rapids, Iowa, instituted a federally supported program for the acquisition of over 1300 damaged properties. Using Cedar Rapids as a case study, this article investigates post-flood property acquisition from the perspectives of cost effectiveness and social equity. To assess economic viability, a benefit-cost analysis was performed at the parcel scale. Social equity was assessed using a social vulnerability index tailored to flood recovery. The results indicate that the property acquisitions are cost effective based on the avoidance of future flood losses, and prioritize socially vulnerable neighborhoods. The dual economic and social analysis sheds light on the capacity of federally supported buyouts to support holistic post-disaster planning and decision-making.

Unequal Recovery? Federal Resource Distribution after a Midwest Flood Disaster

Following severe flooding in 2008, three Iowa communities acquired over 1000 damaged properties to support disaster recovery and mitigation. This research applies a distributive justice framework to analyze the distribution of disaster recovery funds for property acquisition. Two research questions drive the analysis: (1) how does recovery vary by acquisition funding source; and (2) what is the relationship between recovery and vulnerable populations? Through spatial econometric modeling, relative recovery is compared between two federal programs that funded the acquisitions, and across socially vulnerable populations. The results indicate both distributive and temporal inequalities in the allocation of federal recovery funds. In particular, Latino and elderly populations were associated with lower recovery rates. Recommendations for future research in flood recovery and acquisitions are provided.

Pigs in Space: Determining the Environmental Justice Landscape of Swine Concentrated Animal Feeding Operations (CAFOs) in Iowa.

Given the primacy of Iowa in pork production for the U.S. and global markets, we sought to understand if the same relationship with traditional environmental justice (EJ) variables such as low income and minority populations observed in other concentrated animal feeding operation (CAFO) studies exists in the relationship with swine CAFO densities in Iowa. We examined the potential for spatial clustering of swine CAFOs in certain parts of the state and used spatial regression techniques to determine the relationships of high swine concentrations to these EJ variables. We found that while swine CAFOs do cluster in certain regions and watersheds of Iowa, these high densities of swine are not associated with traditional EJ populations of low income and minority race/ethnicity. Instead, the potential for environmental injustice in the negative impacts of intensive swine production require a more complex appraisal. The clustering of swine production in watersheds, the presence of antibiotics used in swine production in public waterways, the clustering of manure spills, and other findings suggest that a more literal and figurative “downstream” approach is necessary. We document the presence and location of antibiotics used in animal production in the public waterways of the state. At the same time, we suggest a more “upstream” understanding of the structural, political and economic factors that create an environmentally unjust landscape of swine production in Iowa and the Upper Midwest is also crucial. Finally, we highlight the important role of publicly accessible and high quality data in the analysis of these upstream and downstream EJ questions.