Comparing GPS and cell-based mobile phone data to identify activity participation during the COVID-19 pandemic
Year: 2024
Type: article
Abstract: This study conducts a detailed analysis of population mobility during the COVID-19 pandemic, utilizing a unique approach that contrasts two types of mobile phone data: GPS-based and cell-based. The primary objective is to evaluate the effects of governmental restrictions on a variety of activities including school attendance, work, shopping, and leisure. We compare both data sets by using a set of defined criteria, including anticipated activity reductions during full and partial closures, as well as the timing of activity changes in response to policy implementations. Our research reveals that while cell-based data lacks the precision to differentiate between various out-of-home activities effectively, GPS-based data, especially when integrated with OpenStreetMap, proves significantly more adept at identifying and categorizing specific activity types. The GPS-based data shows, for example, that school activities fell by more than 80% while work activities were only reduced by around 50%.
Source: EPJ Data Science
Institution: Technische Universität Berlin
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,790
Awards: Modellierungsnetz: infoXpand - Informationen, Meinungen, Mobilität, Verhalten und Bayes'sche Inferenz bei der Modellierung von Infektionskrankheiten - Teilprojekt D, MODUS-COVID: Modellgestützte Untersuchung von Schulschließungen und weiteren Maßnahmen zur Eindämmung von Covid-19 - Teilprojekt 1: Urbane und regionale Simulation auf Basis von datengestützten, synthetischen Bewegungsprofilen
OpenAlex ID: W4404536268