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Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico
Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimate...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768695/ https://www.ncbi.nlm.nih.gov/pubmed/33293417 http://dx.doi.org/10.1073/pnas.2001671117 |
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author | Acosta, Rolando J. Kishore, Nishant Irizarry, Rafael A. Buckee, Caroline O. |
author_facet | Acosta, Rolando J. Kishore, Nishant Irizarry, Rafael A. Buckee, Caroline O. |
author_sort | Acosta, Rolando J. |
collection | PubMed |
description | Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations. |
format | Online Article Text |
id | pubmed-7768695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-77686952021-01-11 Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico Acosta, Rolando J. Kishore, Nishant Irizarry, Rafael A. Buckee, Caroline O. Proc Natl Acad Sci U S A Biological Sciences Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations. National Academy of Sciences 2020-12-22 2020-12-08 /pmc/articles/PMC7768695/ /pubmed/33293417 http://dx.doi.org/10.1073/pnas.2001671117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Acosta, Rolando J. Kishore, Nishant Irizarry, Rafael A. Buckee, Caroline O. Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico |
title | Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico |
title_full | Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico |
title_fullStr | Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico |
title_full_unstemmed | Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico |
title_short | Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico |
title_sort | quantifying the dynamics of migration after hurricane maria in puerto rico |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768695/ https://www.ncbi.nlm.nih.gov/pubmed/33293417 http://dx.doi.org/10.1073/pnas.2001671117 |
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