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

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 mo-bile operating systems, the high-power consumption of mo-bile 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 reluc-tant to share it. Here we present a large anonymous lon-gitudinal dataset of Activity Point Location (APL) generated from mobile devices' GPS tracking. The GPS data were col-lected by using the Google Location History (GLH), accessible in the Google Maps application. Our dataset, named AnLo-COV 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 avail-able to enable advanced analysis of human mobility and ac-tivity spaces based on the collected datasets.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )