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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...

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Autores principales: Podesta, Cristian, Coleman, Natalie, Esmalian, Amir, Yuan, Faxi, Mostafavi, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
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.
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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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