Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
Date
2021
Type:
Article
item.page.extent
item.page.accessRights
item.contributor.advisor
ORCID:
Journal Title
Journal ISSN
Volume Title
Publisher
item.page.isbn
item.page.issn
item.page.issne
item.page.doiurl
item.page.other
item.page.references
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.
Description
item.page.coverage.spatial
item.page.sponsorship
Citation
Pappalardo, L., Ferres, L., Sacasa, M. et al. Evaluación de algoritmos de detección de viviendas en datos de teléfonos móviles utilizando la verdad del terreno a nivel individual. EPJ Data Sci. 10, 29 (2021). https://doi.org/10.1140/epjds/s13688-021-00284-9
Keywords
Mobile phone data, Data science, Human mobility, Home location detection