Evaluation of home detection algorithms on mobile phone data using individual-level ground truth.
Data science
Home location detection
Human mobility
Mobile phone data
Journal
EPJ data science
ISSN: 2193-1127
Titre abrégé: EPJ Data Sci
Pays: Germany
ID NLM: 101686785
Informations de publication
Date de publication:
2021
2021
Historique:
received:
05
11
2020
accepted:
12
05
2021
entrez:
7
6
2021
pubmed:
8
6
2021
medline:
8
6
2021
Statut:
ppublish
Résumé
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. The online version contains supplementary material available at 10.1140/epjds/s13688-021-00284-9.
Identifiants
pubmed: 34094810
doi: 10.1140/epjds/s13688-021-00284-9
pii: 284
pmc: PMC8170634
doi:
Types de publication
Journal Article
Langues
eng
Pagination
29Informations de copyright
© The Author(s) 2021.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
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