An anonymised longitudinal GPS location dataset to understand changes in activity-travel behaviour between pre- and post-COVID periods

Milton Giovanny Moncayo-Unda; Marc Van Droogenbroeck; Ismaïl Saadi; Mario Cools

doi:10.1016/j.dib.2022.108776

An anonymised longitudinal GPS location dataset to understand changes in activity-travel behaviour between pre- and post-COVID periods

Data Brief. 2022 Nov 23:45:108776. doi: 10.1016/j.dib.2022.108776. eCollection 2022 Dec.

Authors

Milton Giovanny Moncayo-Unda^{1

2}, Marc Van Droogenbroeck³, Ismaïl Saadi^{2

4}, Mario Cools^{2

5

6}

Affiliations

¹ Central University of Ecuador, Faculty of Engineering and Applied Sciences, Quito, 170521, Ecuador.
² University of Liège, Local Environment Management & Analysis - LEMA (UEE), Liège, 4000, Belgium.
³ University of Liège, Montefiore Institute of Electrical Engineering and Computer Science, Liège, 4000, Belgium.
⁴ Université Gustave Eiffel, IFSTTAR, COSYS-GRETTIA, Marne-la-Vallée, 77420, France.
⁵ KULeuven Campus Brussels, Department of Information Management Simulation and Modeling, Brussels, 1000, Belgium.
⁶ Hasselt University, Faculty of Business Economics, Hasselt, 3500, Belgium.

Abstract

Collecting GPS data using mobile devices is essential to understanding human mobility. However, getting this type of data is tricky because of some specific features of mobile operating systems, the high-power consumption of mobile devices, and users' privacy concerns. Therefore, data of this kind are rarely publicly available for scientific purposes, while private companies that own the data are often reluctant to share it. Here we present a large anonymous longitudinal dataset of Activity Point Location (APL) generated from mobile devices' GPS tracking. The GPS data were collected by using the Google Location History (GLH), accessible in the Google Maps application. Our dataset, named AnLoCOV hereafter, includes anonymised data from 338 persons with corresponding socio-demographics over approximately ten years (2012-2022), thus covering pre- and post-COVID periods, and calculates over 2 million weekly-classified APL extracted from approximately 16 million GPS tracking points in Ecuador. Furthermore, we made our models publicly available to enable advanced analysis of human mobility and activity spaces based on the collected datasets.

Keywords: Activity point location (APL); GPS; Google location history (GLH); Longitudinal data; Timeline tracking.