Moncayo-Unda Milton Giovanny, Van Droogenbroeck Marc, Saadi Ismaïl, Cools Mario
Central University of Ecuador, Faculty of Engineering and Applied Sciences, Quito, 170521, Ecuador.
University of Liège, Local Environment Management & Analysis - LEMA (UEE), Liège, 4000, Belgium.
Data Brief. 2022 Nov 23;45:108776. doi: 10.1016/j.dib.2022.108776. eCollection 2022 Dec.
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.
使用移动设备收集GPS数据对于理解人类移动性至关重要。然而，由于移动操作系统的一些特定特性、移动设备的高功耗以及用户对隐私的担忧，获取此类数据颇具难度。因此，这类数据很少为科学目的而公开可用，而拥有数据的私人公司往往不愿分享。在此，我们展示了一个由移动设备GPS跟踪生成的大型匿名纵向活动点位置（APL）数据集。GPS数据是通过使用谷歌地图应用中可访问的谷歌位置历史记录（GLH）收集的。我们的数据集此后称为AnLoCOV，包括来自338人的匿名数据以及相应的社会人口统计数据，时间跨度约为十年（2012 - 2022年），涵盖了新冠疫情前后时期，并计算了从厄瓜多尔约1600万个GPS跟踪点中提取的超过200万个每周分类的APL。此外，我们公开了我们的模型，以便能够基于收集到的数据集对人类移动性和活动空间进行深入分析。
