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

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.

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

“…The COVID-19 pandemic highlights the importance of rapid human mobility monitoring. User-generated information from social media platforms (e.g., Twitter, Facebook, Sina Weibo, and Instagram), when coupled with geo-information (i.e., geograohic coordinates and information on place names), allows human–human, human-place, and place-place interactions to be monitored in an active and less privacy-concerning manner ( Huang et al, 2020 , Li et al, 2021a ), thus serving as an important venue where timely human mobility dynamics can be collected and analyzed to assist with decision making. Despite the existence of many social media platforms, only a small proportion of them permit information mining or open-source aggregated mobility records for researchers and the public, while for some social media platforms (e.g., Facebook and Sina Weibo), certain agreements have to be met to access to the records.…”

“…In general, the geotagging levels include country, first-level subdivision, second-level subdivision, city, neighborhood/point of interest (POI), and exact coordinates. A study conducted by Li et al summarized the positioning levels of 1.4 billion geotagged tweets worldwide: 1.1 billion (79%) at the city level, 138.1 million (9.8%) at the first-level subdivision (state or province), 90.4 million (6.4%) with exact coordinates, 46.2 million (3.3%) at country level, and 21.4 million (1.5%) at neighborhood/point of interest ( Li et al, 2021a ). Certainly, different social media platforms have varying preferences towards certain positioning levels.…”

“…Due to social media’s interactive nature and popularity amongst users throughout the pandemic, there is rising democratization of health communications, which poses a sharp contrast to decades ago, when communications were predominantly controlled by individuals and entities endowed with the power, money, public trust, or platforms required to drive the conversation ( Schillinger et al, 2020 ). The emerging concepts of “Web 2.0” ( Murugesan, 2007 ), “Big Data” ( Yang et al, 2017 ), and “Citizen as Sensors” ( Goodchild, 2007 ) have greatly promoted social media as the platforms and virtual communities where users worldwide can create and share information, forming vast sensing networks that allow information in certain topics to be collected, stored, mined, and analyzed in a rapid manner ( Li et al, 2021a , Ye et al, 2021 , Gong and Yang, 2020 ).…”

Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas

“…The home locations of device users are first determined (to a Geohash-7 granularity ( )) using the common nighttime location of each device over a six-week period, and users’ daily movement patterns at the CBG level are further reported (X. Li et al, 2021 ; Z. Li et al, 2021 ; SafeGraph, 2020 ).…”

“… Li et al, 2021 ; Z. Li et al, 2021 ; SafeGraph, 2020 ). That is to say, the OD matrix extracted from this dataset measures the daily moving pattern with home location as the origin location.…”