Authors: Yunhe Cui, University of Connecticut, Tao Hu*, Harvard University, Zhenlong Li, University of South Carolina, Sisi Wang, University of Queensland, Bing She, University of Michigan, Mengxi Zhang, Ball State University
Topics: Transportation Geography
Keywords: COVID-19,pandemic,human mobility,mobility data,challenges
Session Type: Virtual Paper
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 24
Presentation File: No File Uploaded
To tackle the COVID-19 pandemic, countries worldwide have implemented a variety of stringent policies, especially human movement restrictions, such as stay at home, school closing, workplace closing, public transport closing, and public events canceling. How effective have these policies been in reducing human movement? Do the human movement restrictions help contain the COVID-19 transmission? To answer these questions, we need to get more insights from the mobility data. There have been many data sources ranging from public transportation and telecommunication companies to mobile phone apps and Geotagged social media platform. Each data source help identifies human mobility and publishes mobility datasets in different formats, including Google Community Mobility Reports, Apple Mobility Reports, Safegraph Social Distancing Index, and so on. This study reviews the features, advantages, and disadvantages of each dataset, and summarizes how the datasets are applied for COVID-19 applications, such as human mobility trends detection, association exploration with other demographic and socioeconomic factors, and COVID-19 cases prediction and simulations. Finally, several mobility data challenges in the pandemics are discussed, including how to balance data privacy and data sharing, how to select primary variables in the mobility data, how to choose the right mobility index in the applications, and how to integrate with other data sources to enhance the applications in the pandemic.