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



PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.64.pdf
  • Filesize: 0.95 MB
  • 6 pages

Document Identifiers

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Chris Brunsdon, Stewart Fotheringham, and Martin Charlton. Geographically weighted regression-modelling spatial non-stationarity. Journal of the Royal Statistical Society. Series D (The Statistician), 47(3):431-443, 1998. URL: http://www.jstor.org/stable/2988625.
  2. Susan L Cutter, Bryan J Boruff, and WL Shirly. Social science quarterly. Soc. Vulnerability Environ. Hazards, 84:242-261, 2003. Google Scholar
  3. Yaella Depietri, Torsten Welle, and Fabrice G. Renaud. Social vulnerability assessment of the cologne urban area (germany) to heat waves: links to ecosystem services. International Journal of Disaster Risk Reduction, 6:98-117, 2013. Google Scholar
  4. Abbas El-Zein and Fahim N Tonmoy. Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in sydney. Ecological Indicators, 48:207-2017, 2015. Google Scholar
  5. A. Fotheringham, Chris Brunsdon, and Martin Charlton. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, 2002. Google Scholar
  6. Rachel Franklin. Quantitative methods i: Reckoning with uncertainty. Progress in Human Geography, 46(2):689-697, 2022. Google Scholar
  7. Jon Herman and Will Usher. SALib: An open-source python library for sensitivity analysis. The Journal of Open Source Software, 2(9), January 2017. Google Scholar
  8. Daniel P. Johnson, Austin Stanforth, Vijay Lulla, and George Luber. Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Applied Geography, 35(1):23-31, 2012. Google Scholar
  9. Shirley Laska and Betty Hearn Morrow. Social vulnerabilities and hurricane katrina: an unnatural disaster in new orleans. Marine technology society journal, 40(4), 2006. Google Scholar
  10. K. Lieberknecht, D. Zoll, and K. Castles. Hurricane harvey: equal opportunity storm or disparate disaster? Local Environment, 2(26), 2021. Google Scholar
  11. Francisco de la Barrera Luis Inostroza, Massimo Palme. A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for santiago de chile. PLOS ONE, 2016. Google Scholar
  12. Alex Nguyen and Erin Douglas. Texas heat-related deaths reached a two-decade high in 2022 amid extreme temperatures. CHRON, 2012. Google Scholar
  13. AZHS Arizona Department of Health Services. Extreme weather & public health. URL: https://www.azdhs.gov/preparedness/epidemiology-disease-control/extreme-weather/heat-safety/index.php#heat-home.
  14. D. Reckien. What is in an index? construction method, data metric, and weighting scheme determine the outcome of composite social vulnerability indices in new york city. Regional Environmental Change, 18:1439-1451, 2018. Google Scholar
  15. C. Rinner, D. Patychuk, K. Bassil, S. Nasr, S. Gower, and M. Campbell. The role of maps in neighborhood-level heat vulnerability assessment for the city of toronto. Cartography and Geographic Information Science, 1(37), 2010. Google Scholar
  16. Samain Sabrin, Maryam Karimi, Md Golam Rabbani Fahad, and Rouzbeh Nazari. Quantifying environmental and social vulnerability: Role of urban heat island and air quality, a case study of camden, nj. Urban Climate, 34, 2020. Google Scholar
  17. Seda Şalap-Ayça. Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization. Transactions in GIS, 26(4):1718-1734, 2022. Google Scholar
  18. Seda Şalap-Ayça, Piotr Jankowski, Keith C Clarke, Phaedon C Kyriakidis, and Atsushi Nara. A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based urban growth and land-use change model. International Journal of Geographical Information Science, 32(4):637-662, 2018. Google Scholar
  19. S. Sheridan T. Dolney. The relationship between extreme heat and ambulance response calls for the city of toronto, ontario, canada. Environmental Research, 100:94-103, 2006. Google Scholar
  20. Iwanaga Takuya, William Usher, and Jonathan Herman. Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling, 4:18155, May 2022. URL: https://doi.org/10.18174/sesmo.18155.
  21. Michael Wehner, Sonia Seneviratne, Xuebin Zhang, Muhammad Adnan, Wafae Badi, Claudine Dereczynski, Alejandro Di Luca, Subimal Ghosh, Iskhaq Iskandar, James Kossin, et al. Weather and climate extreme events in a changing climate. In AGU Fall Meeting Abstracts, volume 2021, pages U13B-11, 2021. Google Scholar
  22. Wes McKinney. Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference, pages 56-61, 2010. Google Scholar
  23. David C. Wheeler and Antonio Páez. Geographically weighted regression. In Manfred M. Fischer and Arthur Getis, editors, Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, pages 461-486. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. Google Scholar
  24. Tanja Wolf and Glenn McGregor. The development of a heat wave vulnerability index for london, united kingdom. Weather and Climate Extremes, 1:59-68, 2013. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail