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Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data
This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086905/ https://www.ncbi.nlm.nih.gov/pubmed/33906388 http://dx.doi.org/10.1098/rsif.2021.0158 |
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author | Podesta, Cristian Coleman, Natalie Esmalian, Amir Yuan, Faxi Mostafavi, Ali |
author_facet | Podesta, Cristian Coleman, Natalie Esmalian, Amir Yuan, Faxi Mostafavi, Ali |
author_sort | Podesta, Cristian |
collection | PubMed |
description | This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process. |
format | Online Article Text |
id | pubmed-8086905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-80869052021-07-07 Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data Podesta, Cristian Coleman, Natalie Esmalian, Amir Yuan, Faxi Mostafavi, Ali J R Soc Interface Life Sciences–Engineering interface This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process. The Royal Society 2021-04-28 /pmc/articles/PMC8086905/ /pubmed/33906388 http://dx.doi.org/10.1098/rsif.2021.0158 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Engineering interface Podesta, Cristian Coleman, Natalie Esmalian, Amir Yuan, Faxi Mostafavi, Ali Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
title | Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
title_full | Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
title_fullStr | Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
title_full_unstemmed | Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
title_short | Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
title_sort | quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data |
topic | Life Sciences–Engineering interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086905/ https://www.ncbi.nlm.nih.gov/pubmed/33906388 http://dx.doi.org/10.1098/rsif.2021.0158 |
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