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Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges

Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With th...

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Autores principales: Li, Xiao, Xu, Haowen, Huang, Xiao, Guo, Chenxiao (Atlas), Kang, Yuhao, Ye, Xinyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8475419/
https://www.ncbi.nlm.nih.gov/pubmed/34766169
http://dx.doi.org/10.1007/s43762-021-00022-x
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author Li, Xiao
Xu, Haowen
Huang, Xiao
Guo, Chenxiao (Atlas)
Kang, Yuhao
Ye, Xinyue
author_facet Li, Xiao
Xu, Haowen
Huang, Xiao
Guo, Chenxiao (Atlas)
Kang, Yuhao
Ye, Xinyue
author_sort Li, Xiao
collection PubMed
description Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.
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spelling pubmed-84754192021-09-28 Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges Li, Xiao Xu, Haowen Huang, Xiao Guo, Chenxiao (Atlas) Kang, Yuhao Ye, Xinyue Comput.Urban Sci. Opinion Paper Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data. Springer Singapore 2021-09-26 2021 /pmc/articles/PMC8475419/ /pubmed/34766169 http://dx.doi.org/10.1007/s43762-021-00022-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Opinion Paper
Li, Xiao
Xu, Haowen
Huang, Xiao
Guo, Chenxiao (Atlas)
Kang, Yuhao
Ye, Xinyue
Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges
title Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges
title_full Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges
title_fullStr Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges
title_full_unstemmed Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges
title_short Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges
title_sort emerging geo-data sources to reveal human mobility dynamics during covid-19 pandemic: opportunities and challenges
topic Opinion Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8475419/
https://www.ncbi.nlm.nih.gov/pubmed/34766169
http://dx.doi.org/10.1007/s43762-021-00022-x
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