Abstract
Index-based measures of social vulnerability to environmental hazards are commonly modeled from composites of population-level risk factors. These models overlook individual context in communities’ experiences of environmental hazards, producing metrics that may hinder spatial decision support for mitigating and responding to hazards. This paper introduces an interpretable, high-resolution model for generating an individual-oriented social vulnerability index (IOSVI) for the United States built on synthetic populations that couples individual and social determinants of vulnerability. The IOSVI combines an individual vulnerability index (IVI) that ranks individuals in an area’s synthetic population based on intersecting risk factors, with a social vulnerability index (SVI) based on the population’s cumulative distribution of IVI scores. Interpretability of the IOSVI procedure is demonstrated through examples of national, metropolitan, and neighborhood (census tract) level spatial variation in index scores and IVI themes, as well as an exploratory analysis examining risk factors affecting a specific sub-population (military veterans) in areas of high social and environmental vulnerability.
| Original language | English |
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| Title of host publication | 12th International Conference on Geographic Information Science, GIScience 2023 |
| Editors | Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, Sarah Wise |
| Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
| ISBN (Electronic) | 9783959772884 |
| DOIs | |
| State | Published - Sep 2023 |
| Event | 12th International Conference on Geographic Information Science, GIScience 2023 - Leeds, United Kingdom Duration: Sep 12 2023 → Sep 15 2023 |
Publication series
| Name | Leibniz International Proceedings in Informatics, LIPIcs |
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| Volume | 277 |
| ISSN (Print) | 1868-8969 |
Conference
| Conference | 12th International Conference on Geographic Information Science, GIScience 2023 |
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| Country/Territory | United Kingdom |
| City | Leeds |
| Period | 09/12/23 → 09/15/23 |
Funding
Funding Notice: This work is sponsored by the US Department of Veterans Affairs. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Notice: This work is sponsored by the US Department of Veterans Affairs. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Census
- Environmental Hazard
- Social Vulnerability
- Synthetic Population
- Veteran