Work

Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

Year: 2021

Type: article

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.

Source: EPJ Data Science

DOI: 10.1140/epjds/s13688-021-00284-9

Authors: Luca Pappalardo, Leo Ferres, Manuel Sacasa, Ciro Cattuto, Loreto Bravo

Institutions: Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", National Research Council, Universidad del Desarrollo, Institute for Scientific Interchange, University of Turin

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,690