A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters
Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the pot...
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sg-ntu-dr.10356-1715012023-10-31T15:36:39Z A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters Loos, Sabine Lallemant, David Khan, Feroz McCaughey, Jamie W. W. Banick, Robert Budhathoki, Nama Baker, Jack W. W. Asian School of the Environment Earth Observatory of Singapore Engineering::Environmental engineering Social Vulnerability Natural Hazards Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the potential to fall behind during recovery, by relating surveyed data on recovery progress with data that would be readily available in most countries. We demonstrate this approach for one dimension of recovery—housing reconstruction—analyzing data collected five years after the 2015 Nepal earthquake to identify a range of ongoing social and environmental vulnerabilities related to non-recovery in Nepal. If such information were available in 2015, it would have exposed regional differences in recovery potential due to these vulnerabilities. More generally, moving beyond damage data by estimating non-recovery focuses attention on those most vulnerable sooner after a disaster to better support holistic and nuanced decisions. National Research Foundation (NRF) Published version This project is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the UK DFID, the government of Korea, and the Dept. of Foreign Affairs and Trade of Ireland. The Disaster Analytics for Society Lab is funded by the Singapore National Research Foundation under the NRF-NRFF2018-06 award, and the Earth Observatory of Singapore (contribution no. 514). Sabine Loos was partially funded by the Stanford Urban Resilience Initiative, the John A. Blume Earthquake Engineering Center, and by the National Science Foundation Graduate Research Fellowship. 2023-10-27T04:45:21Z 2023-10-27T04:45:21Z 2023 Journal Article Loos, S., Lallemant, D., Khan, F., McCaughey, J. W. W., Banick, R., Budhathoki, N. & Baker, J. W. W. (2023). A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters. Communications Earth and Environment, 4(1), 40-. https://dx.doi.org/10.1038/s43247-023-00699-4 2662-4435 https://hdl.handle.net/10356/171501 10.1038/s43247-023-00699-4 2-s2.0-85148518967 1 4 40 en NRF-NRFF2018-06 Communications Earth and Environment © 2023 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
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Engineering::Environmental engineering Social Vulnerability Natural Hazards Loos, Sabine Lallemant, David Khan, Feroz McCaughey, Jamie W. W. Banick, Robert Budhathoki, Nama Baker, Jack W. W. A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
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Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the potential to fall behind during recovery, by relating surveyed data on recovery progress with data that would be readily available in most countries. We demonstrate this approach for one dimension of recovery—housing reconstruction—analyzing data collected five years after the 2015 Nepal earthquake to identify a range of ongoing social and environmental vulnerabilities related to non-recovery in Nepal. If such information were available in 2015, it would have exposed regional differences in recovery potential due to these vulnerabilities. More generally, moving beyond damage data by estimating non-recovery focuses attention on those most vulnerable sooner after a disaster to better support holistic and nuanced decisions. |
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Asian School of the Environment |
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Asian School of the Environment Loos, Sabine Lallemant, David Khan, Feroz McCaughey, Jamie W. W. Banick, Robert Budhathoki, Nama Baker, Jack W. W. |
format |
Article |
author |
Loos, Sabine Lallemant, David Khan, Feroz McCaughey, Jamie W. W. Banick, Robert Budhathoki, Nama Baker, Jack W. W. |
author_sort |
Loos, Sabine |
title |
A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
title_short |
A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
title_full |
A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
title_fullStr |
A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
title_full_unstemmed |
A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
title_sort |
data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171501 |
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1781793743933800448 |