1. Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility
- Author
-
Chen, Qingqing and Poorthuis, Ate
- Subjects
Technology ,Computer science ,Geography, Planning and Development ,Twitter ,0211 other engineering and technologies ,0507 social and economic geography ,Social Sciences ,02 engineering and technology ,SocArXiv|Social and Behavioral Sciences|Geography ,Library and Information Sciences ,computer.software_genre ,comparison and reproducibility ,Information Science & Library Science ,021101 geological & geomatics engineering ,Reproducibility ,Science & Technology ,Computer Science, Information Systems ,Database ,Geography ,location-based services (LBS) ,05 social sciences ,R package ,bepress|Social and Behavioral Sciences|Geography ,Geography, Physical ,Open source ,Work (electrical) ,Physical Geography ,Physical Sciences ,Computer Science ,bepress|Social and Behavioral Sciences ,SocArXiv|Social and Behavioral Sciences ,Identifying home locations ,050703 geography ,computer ,Information Systems - Abstract
Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which - compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R software package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research.
- Published
- 2021