Published in
Elsevier, Physica A: Statistical Mechanics and its Applications, 6(392), p. 1459-1473, 2013
DOI: 10.1016/j.physa.2012.11.040
Links
- Elsevier
- ORCID
- [hdl.handle.net] | PDF
- [eprints.sztaki.hu] | PDF
- [dial.uclouvain.be] | PDF
- [arxiv.org] | PDF
- [www.researchgate.net] | PDF
- [arxiv.org] | PDF
- [real.mtak.hu] | PDF
- [citeseerx.ist.psu.edu] | PDF
- [www.unifr.ch] | PDF
Tools
Exploring the Mobility of Mobile Phone Users
,
Jean-Charles Delvenne,
Etienne Huens,
Paul Van Dooren,
Zbigniew Smoreda,
Vincent D. Blondel
Full text: Download
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 100000 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 oce locations and compare the results with ocial 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.