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

Xiao Li, Haowen Xu, Xiao Huang, Chenxiao (Atlas) Guo, Yuhao Kang, Xinyue Ye

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish (US)
Article number22
JournalComputational Urban Science
Issue number1
StatePublished - Dec 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Urban Studies
  • Artificial Intelligence
  • Computer Science Applications
  • Environmental Science (miscellaneous)


  • COVID-19
  • Connected vehicle
  • Mobile device data
  • Mobility data
  • Social media


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