Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Computational urban science, 2021-12, Vol.1 (1), p.22-22, Article 22

Ort / Verlag

Singapore: Springer Singapore

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.