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.2023.64
URN: urn:nbn:de:0030-drops-189594
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18959/
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Şalap-Ayça, Seda ; Goto, Erica Akemi

Beware the Rise of Models When They Are Wrong: A Look at Heat Vulnerability Modeling Through the Lens of Sensitivity (Short Paper)

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LIPIcs-GIScience-2023-64.pdf (1.0 MB)

Abstract

Extreme heat affects communities across the globe and is likely to increase as the climate changes; however, its consequences are not uniform. Geographically weighted regression is a useful modeling effort to understand the spatial linkage between various factors to heat-related casualty and supports decision-making in the spatial context. Still, as every complex spatial modeling approach, it is also bounded by uncertainty. Understanding model uncertainty and how this uncertainty is related to model input can be revealed by sensitivity analysis. In this study, we applied a spatial global sensitivity analysis to assess the model dynamics to address which input factors need to be prioritized in decision-making. A visual representation of the model’s sensitivity and the spatial pattern of factor influence is an important step toward establishing a robust confidence mechanism for understanding heat vulnerability and supporting policy-making.

BibTeX - Entry

@InProceedings{salapayca_et_al:LIPIcs.GIScience.2023.64,
  author =	{\c{S}alap-Ay\c{c}a, Seda and Goto, Erica Akemi},
  title =	{{Beware the Rise of Models When They Are Wrong: A Look at Heat Vulnerability Modeling Through the Lens of Sensitivity}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{64:1--64:6},
  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/2023/18959},
  URN =		{urn:nbn:de:0030-drops-189594},
  doi =		{10.4230/LIPIcs.GIScience.2023.64},
  annote =	{Keywords: heat vulnerability, uncertainty, sensitivity analysis}
}

Keywords: heat vulnerability, uncertainty, sensitivity analysis
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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