The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Exploring the mobility of mobile phone users
Open accessAbstract
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.
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Cites
A Simplex Method for Function Minimization
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Tse, D., & Viswanath, P. (2005) · doi:10.1017/cbo9780511807213 · Cited by 13,162
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic
Bajardi, P., Poletto, C., Ramasco, J. J., Tizzoni, M., Colizza, V., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016591 · Cited by 495
Cited by
Population flow drives spatio-temporal distribution of COVID-19 in China
Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020) · Nature · doi:10.1038/s41586-020-2284-y · Cited by 835
A multi-source dataset of urban life in the city of Milan and the Province of Trentino
Barlacchi, G., De Nadai, M., Larcher, R., Casella, A., Chitic, C., Torrisi, G., Antonelli, F., Vespignani, A., Pentland, A., & Lepri, B. (2015) · Scientific Data · doi:10.1038/sdata.2015.55 · Cited by 560
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data
Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086026 · Cited by 393
Applying mobile phone data to travel behaviour research: A literature review
Wang, Z., He, S. Y., & Leung, Y. (2018) · Travel Behaviour and Society · doi:10.1016/j.tbs.2017.02.005 · Cited by 367
Uncovering urban human mobility from large scale taxi GPS data
Tang, J., Liu, F., Wang, Y., & Wang, H. (2015) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.06.032 · Cited by 280
Metadata
- Authors
- Csáji, B. C., Browet, A., Traag, V. A., Delvenne, J.-C., Huens, E., Van Dooren, P., Smoreda, Z., & Blondel, V. D.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2013-03
- Volume
- 392
- Issue
- 6
- Pages
- 1459-1473
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2004602565
- Cited by
- 239
- References
- 4
- FWCI
- 30.79
- Type
- journal article
- Language
- en
- Open access
- green
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