ArticleEPJ data science2021
Evaluation of home detection algorithms on mobile phone data using individual-level ground truth.
Article in EPJ data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A visitor-enriched census in the U.S. cities using large-scale mobile positioning data.Scientific data · 2025Article
- Revealing urban area from mobile positioning data.Scientific reports · 2024Article
- The social stratification of internal migration and daily mobility during the COVID-19 pandemic.Scientific reports · 2024Article
- A dataset to assess mobility changes in Chile following local quarantines.Scientific data · 2023Article
- A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data.Urban informatics · 2022Article
- Using mobile money data and call detail records to explore the risks of urban migration in Tanzania.EPJ data science · 2022Article
- A Deep Gravity model for mobility flows generation.Nature communications · 2021Article
- How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing.Sensors (Basel, Switzerland) · 2021Article
- COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour.Journal of the Royal Society, Interface · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring mobile phone users' home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1140/epjds/s13688-021-00284-9.
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