Assessing the quality of home detection from mobile phone data for official statistics

TL;DR

This paper evaluates the effectiveness of five home detection algorithms using French mobile phone data, highlighting their limitations and proposing recommendations for more reliable use in official statistics.

Contribution

It provides a comprehensive analysis of current home detection algorithms and offers practical guidelines to improve their application in official statistical processes.

Findings

Home detection criteria influence results for up to 40% of users.

Algorithms perform poorly against validation datasets.

Performance varies with time period and observation duration.

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 paper, 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) dataset (~18 million users, 5 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 dataset (the 35{\deg}-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 in ensuring a more reliable use of mobile phone data vis-\`a-vis official statistics.