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

Authors Seda Şalap-Ayça , Erica Akemi Goto

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Author Details

Seda Şalap-Ayça
  • Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI, USA
  • Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
  • Department of Earth, Geographic, and Climate Sciences, University of Massachusetts, Amherst, MA, USA
Erica Akemi Goto
  • Arizona Institute for Resilience, University of Arizona, Tucson, AZ, USA

Acknowledgements

Seda {Şalap-Ayça} wants to thank Aykut {Ayça} for his discussion on selecting probability distribution functions.

Cite AsGet BibTex

Seda Şalap-Ayça and Erica Akemi Goto. Beware the Rise of Models When They Are Wrong: A Look at Heat Vulnerability Modeling Through the Lens of Sensitivity (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 64:1-64:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.64

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.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • heat vulnerability
  • uncertainty
  • sensitivity analysis

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