<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Task Force Big Data, Eurostat, European Commission

School of Interactive Computing, Georgia Institute of Technology

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mobile phone data are an interesting new data source for official statistics. However, multiple problems and uncertainties need to be solved before these data...

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

Online veröffentlicht: 14. Dez. 2018

Seitenbereich: 935 - 960

Eingereicht: 01. Juni 2017

Akzeptiert: 01. Apr. 2018

DOI: https://doi.org/10.2478/jos-2018-0046

Schlüsselwörter
Mobile phone data, home location, home detection algorithms, official statistics, big data

© 2018 Maarten Vanhoof et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 14. Dez. 2018

Seitenbereich: 935 - 960

Eingereicht: 01. Juni 2017

Akzeptiert: 01. Apr. 2018

DOI: https://doi.org/10.2478/jos-2018-0046

SchlüsselwörterMobile phone data, home location, home detection algorithms, official statistics, big data

© 2018 Maarten Vanhoof et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Mobile phone data, home location, home detection algorithms, official statistics, big data