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

Authors :
Chenxiao Atlas Guo
Xinyue Ye
Yuhao Kang
Haowen Xu
Xiao Huang
Xiao Li
Source :
Computational Urban Science
Publication Year :
2021

Abstract

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.

Details

ISSN :
27306852
Volume :
1
Issue :
1
Database :
OpenAIRE
Journal :
Computational urban science
Accession number :
edsair.doi.dedup.....c8fe99a5e67bfbaa9e0296bd13af0d90