License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.74
URN: urn:nbn:de:0030-drops-189699
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18969/
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Tuccillo, Joseph V.

An Interpretable Index of Social Vulnerability to Environmental Hazards (Short Paper)

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LIPIcs-GIScience-2023-74.pdf (2 MB)

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.

BibTeX - Entry

@InProceedings{tuccillo:LIPIcs.GIScience.2023.74,
  author =	{Tuccillo, Joseph V.},
  title =	{{An Interpretable Index of Social Vulnerability to Environmental Hazards}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{74:1--74:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18969},
  URN =		{urn:nbn:de:0030-drops-189699},
  doi =		{10.4230/LIPIcs.GIScience.2023.74},
  annote =	{Keywords: Social Vulnerability, Environmental Hazard, Synthetic Population, Census, Veteran}
}

Keywords: Social Vulnerability, Environmental Hazard, Synthetic Population, Census, Veteran
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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