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Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data
In the spring of 2020, the COVID-19 pandemic changed the daily lives of people around the world. In an effort to quantify these changes, Google released an open-source dataset pertaining to regional mobility trends—including park visitation trends. Changes in park visitation are calculated from an e...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677329/ https://www.ncbi.nlm.nih.gov/pubmed/34934999 http://dx.doi.org/10.1016/j.wss.2021.100037 |
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author | Rice, William L. Pan, Bing |
author_facet | Rice, William L. Pan, Bing |
author_sort | Rice, William L. |
collection | PubMed |
description | In the spring of 2020, the COVID-19 pandemic changed the daily lives of people around the world. In an effort to quantify these changes, Google released an open-source dataset pertaining to regional mobility trends—including park visitation trends. Changes in park visitation are calculated from an earlier baseline period for measurement. Park visitation is robustly linked to positive wellbeing indicators across the lifespan, and has been shown to support wellbeing during the COVID-19 pandemic. Therefore, this dataset offers vast application potential, containing aggregated information from location data collected via smartphones worldwide. However, empirical analysis of these data is limited. Namely, the factors influencing reported changes in mobility and the degree to which these changes can be directly attributable to COVID-19 remain unknown. This study aims to address these gaps in our understanding of the changes in park visitation, the causes of these changes (e.g., safer-at-home orders, amount of COVID-19 cases per county, climate, etc.) and possible impacts to wellbeing by constructing and testing a spatial regression model. Results suggest that elevation and latitude serve as primary influences of reported changes in park visitation from the baseline period. Therefore, it is surmised that Google's reported changes in park-related mobility are only partially the function of COVID-19. |
format | Online Article Text |
id | pubmed-8677329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86773292021-12-17 Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data Rice, William L. Pan, Bing Wellbeing Space Soc Article In the spring of 2020, the COVID-19 pandemic changed the daily lives of people around the world. In an effort to quantify these changes, Google released an open-source dataset pertaining to regional mobility trends—including park visitation trends. Changes in park visitation are calculated from an earlier baseline period for measurement. Park visitation is robustly linked to positive wellbeing indicators across the lifespan, and has been shown to support wellbeing during the COVID-19 pandemic. Therefore, this dataset offers vast application potential, containing aggregated information from location data collected via smartphones worldwide. However, empirical analysis of these data is limited. Namely, the factors influencing reported changes in mobility and the degree to which these changes can be directly attributable to COVID-19 remain unknown. This study aims to address these gaps in our understanding of the changes in park visitation, the causes of these changes (e.g., safer-at-home orders, amount of COVID-19 cases per county, climate, etc.) and possible impacts to wellbeing by constructing and testing a spatial regression model. Results suggest that elevation and latitude serve as primary influences of reported changes in park visitation from the baseline period. Therefore, it is surmised that Google's reported changes in park-related mobility are only partially the function of COVID-19. The Author(s). Published by Elsevier Ltd. 2021 2021-05-11 /pmc/articles/PMC8677329/ /pubmed/34934999 http://dx.doi.org/10.1016/j.wss.2021.100037 Text en © 2021 The Author(s). Published by Elsevier Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Rice, William L. Pan, Bing Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data |
title | Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data |
title_full | Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data |
title_fullStr | Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data |
title_full_unstemmed | Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data |
title_short | Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data |
title_sort | understanding changes in park visitation during the covid-19 pandemic: a spatial application of big data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677329/ https://www.ncbi.nlm.nih.gov/pubmed/34934999 http://dx.doi.org/10.1016/j.wss.2021.100037 |
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