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College of Arts & Sciences
Hazards & Vulnerability Research Institute

Collaborative Research: Measuring Social Vulnerability--Reducing Uncertainty and Validating Indicators

Lead Investigators:  Dr. Christopher T. Emrich (Univ. of South Carolina/Univ. of Central Florida)

                                       Dr. Eric C. Tate (University of Iowa)

                                       Dr. Seth E. Speilman (University of Colorado, Boulder)



     The study tests the central hypothesis that the robustness and utility of social vulnerability indicators will significantly improve by reducing uncertainties in input data and methods, and validating output metrics. The rationale is that the production of more reliable vulnerability metrics will increase their credibility and use in risk reduction decision-making. The PIs bring together expertise in social vulnerability theory and measurement and demographic data analysis, and have previously collaborated and published on theoretical and practical questions in the assessment of hazards vulnerability and model uncertainty. The investigation is based on three specific objectives:

1. Reduce demographic attribute uncertainty in social vulnerability indicators. New statistical and geospatial approaches will be developed to reduce the uncertainty of U.S. Census Bureau demographic estimates. 

2. Reduce modeling uncertainty in social vulnerability indices. Global sensitivity analysis will be used quantify uncertainty in the modeling process, determine its influence on social vulnerability ranks, and develop methods to minimize it. 

3. Validate social vulnerability indices with empirical data. Multiple regression and spatial analytic techniques will be used to identify disaster outcome measures that correlate with social vulnerability indices.


     This research is expected to generate new understanding of the critical inputs and decision points in social vulnerability modeling, the level of agreement of resultant indicators with observed disaster outcomes, and the role of uncertainty in social vulnerability modeling. By narrowing the existing chasm between vulnerability models and current real-world practice, improved social vulnerability indicators will allow for better disaster planning and interventions that account for the most vulnerable populations. For more information see