Exploring the mobility of mobile phone users
Balázs Csanád Csájia b, Arnaud Browetc, Vincent Traagc✉, Jean‐Charles Delvennec d e, Etienne Huensc, Paul M. Van Doorenc, Zbigniew Smoredaf, Vincent D. Blondelc
- aComputer and Automation Research Institute (SZTAKI), Hungarian Academy of Sciences, Hungary
- bDepartment of Electrical and Electronic Engineering, University of Melbourne, Australia
- cICTEAM Institute, Université catholique de Louvain, Belgium
- dCenter for Operations Research and Econometrics (CORE), Université catholique de Louvain, Belgium
- eNamur center for complex systems (NAXYS), Facultés Universitaires Notre-Dame de la Paix, Belgium
- fSociology and Economics of Networks and Services Department, Orange Labs, France
- ✉Corresponding author
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Abstract
Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100,000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users’ frequent locations and show that commuting distances can be reasonably well explained by a gravity model.
TL;DRThe connections between various features of human behavior extracted from a large mobile phone dataset are explored and it is shown that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. (Semantic Scholar)