Assessing the Quality of Home Detection from Mobile Phone Data for Official Statistics

Vanhoof Maarten (), Reis Fernando (), Ploetz Thomas () and Smoreda Zbigniew ()
Additional contact information
Vanhoof Maarten: Open Lab, Urban Sciences Building, 1 Science Square, Science Central, Newcastle upon Tyne NE4 5TG, United Kingdom.
Reis Fernando: Task Force Big Data, Eurostat, European Commission, Joseph Bech Building, 5, Rue Alphonse Weicker, L-2721Luxembourg.
Ploetz Thomas: School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, U.S.A., and Open Lab, Urban Sciences Building, 1 Science Square, Science Central, Newcastle upon Tyne NE4 5TG, United Kingdom.
Smoreda Zbigniew: Department SENSE, Orange Labs, Orange Gardens, 44 Avenue de la République, 92320Châtillon, France.

Journal of Official Statistics, 2018, vol. 34, issue 4, 935-960

Abstract: Mobile phone data are an interesting new data source for official statistics. However, multiple problems and uncertainties need to be solved before these data can inform, support or even become an integral part of statistical production processes. In this article, we focus on arguably the most important problem hindering the application of mobile phone data in official statistics: detecting home locations. We argue that current efforts to detect home locations suffer from a blind deployment of criteria to define a place of residence and from limited validation possibilities. We support our argument by analysing the performance of five home detection algorithms (HDAs) that have been applied to a large, French, Call Detailed Record (CDR) data set (~18 million users, five months). Our results show that criteria choice in HDAs influences the detection of home locations for up to about 40% of users, that HDAs perform poorly when compared with a validation data set (resulting in 358-gap), and that their performance is sensitive to the time period and the duration of observation. Based on our findings and experiences, we offer several recommendations for official statistics. If adopted, our recommendations would help ensure more reliable use of mobile phone data vis-à-vis official statistics.

Keywords: Mobile phone data; home location; home detection algorithms; official statistics; big data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:4:p:935-960:n:7

DOI: 10.2478/jos-2018-0046

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