November 2012, 1:9,
Open Access This content is freely available online to anyone, anywhere at any time.
Date: 06 Nov 2012
Spatiotemporal correlations of handset-based service usages
We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the spatiotemporal trajectories of the users we detect several meaningful places or contexts for each one of them and show how the context affects the service usage patterns. We find that temporal patterns of service usages are bound to the typical weekly cycles of humans, yet they show maximal activities at different times. We first discuss their temporal correlations and then investigate the time-ordering behavior of communication services like calls being followed by the non-communication services like applications. We also find that the behavioral overlap network based on the clustering of temporal patterns is comparable to the communication network of users. Our approach provides a useful framework for handset-based data analysis and helps us to understand the complexities of information and communications technology enabled human behavior.
Goyal S: Connections: an introduction to the network economy. Princeton University Press, Princeton; 2009.
Castellano C, Fortunato S, Loreto V: Statistical physics of social dynamics. Rev Mod Phys 2009,81(2):591–646.CrossRef
Lazer D, Pentland A, Adamic L, Aral S, Barabási AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D, Van Alstyne M: Computational social science. Science 2009,323(5915):721–723.CrossRef
Barabási AL: The origin of bursts and heavy tails in human dynamics. Nature 2005, 435:207–211.CrossRef
Onnela JP, Saramäki J, Hyvönen J, Szabó G, Lazer D, Kaski K, Kertész J, Barabási AL: Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci USA 2007,104(18):7332–7336.CrossRef
Kwak H, Lee C, Park H, Moon S: What is Twitter, a social network or a news media. In Proceedings of the 19th international conference on World Wide Web, WWW ’10. ACM, New York; 2010:591–600.CrossRef
Lewis K, Kaufman J, Gonzalez M, Wimmer A, Christakis N: Tastes, ties, and time: a new social network dataset using Facebook.com. Soc Netw 2008,30(4):330–342.CrossRef
Kovanen L, Karsai M, Kaski K, Kertész J, Saramäki J: Temporal motifs in time-dependent networks. J Stat Mech Theory Exp 2011.,2011(11): Article ID P11005
Jo HH, Karsai M, Kertész J, Kaski K: Circadian pattern and burstiness in mobile phone communication. New J Phys 2012., 14: Article ID 013055
Karsai M, Kaski K, Barabási AL, Kertész J: Universal features of correlated bursty behaviour. Sci Rep 2012., 2: Article ID 397
González MC, Hidalgo CA, Barabási AL: Understanding individual human mobility patterns. Nature 2008,453(7196):779–782.CrossRef
Candia J, González MC, Wang P, Schoenharl T, Madey G, Barabási AL: Uncovering individual and collective human dynamics from mobile phone records. J Phys A, Math Theor 2008.,41(22): Article ID 224015
Wang P, González MC, Hidalgo CA, Barabási AL: Understanding the spreading patterns of mobile phone viruses. Science 2009,324(5930):1071–1076.CrossRef
Song C, Koren T, Wang P, Barabasi AL: Modelling the scaling properties of human mobility. Nat Phys 2010,6(10):818–823.CrossRef
Eagle N, Pentland A: Reality mining: sensing complex social systems. Pers Ubiquitous Comput 2006,10(4):255–268.CrossRef
Eagle N, Pentland AS, Lazer D: Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA 2009,106(36):15274–15278.CrossRef
Krings G, Calabrese F, Ratti C, Blondel VD: Urban gravity: a model for inter-city telecommunication flows. J Stat Mech Theory Exp 2009.,2009(7): Article ID L07003
Bagrow JP, Lin YR: Mesoscopic structure and social aspects of human mobility. PLoS ONE 2012.,7(5): Article ID e37676
Aharony N, Pan W, Ip C, Khayal I, Pentland A: Social fMRI: investigating and shaping social mechanisms in the real world. Pervasive Mob Comput 2011,7(6):643–659.CrossRef
Falaki H, Mahajan R, Kandula S, Lymberopoulos D, Govindan R, Estrin D: Diversity in smartphone usage. In Proceedings of the 8th international conference on mobile systems, applications, and services, MobiSys ’10. ACM, New York; 2010:179–194.
Soikkeli T, Karikoski J, Hammainen H: Diversity and end user context in smartphone usage sessions. In Next generation mobile applications, services and technologies (NGMAST), 2011 5th international conference on. IEEE Press, New York; 2011:7–12.
Dey AK: Understanding and using context. Pers Ubiquitous Comput 2001, 5:4–7.CrossRef
Verkasalo H (2009) Handset-based analysis of mobile service usage. PhD thesis, Helsinki University of Technology, Espoo, FinlandVerkasalo H (2009) Handset-based analysis of mobile service usage. PhD thesis, Helsinki University of Technology, Espoo, Finland
Soikkeli T (2011) The effect of context on smartphone usage sessions. Master’s thesis, Aalto University, Espoo, Finland. http://aalto-fi.academia.edu/TapioSoikkeli/Papers
Karikoski J, Soikkeli T: Contextual usage patterns in smartphone communication services. Pers Ubiquitous Comput 2011. doi:10.1007/s00779–011–0503–0
OtaSizzle project. http://sizl.org
Montoliu R, Perez DG: Discovering human places of interest from multimodal mobile phone data. In Proceedings of the 9th international conference on mobile and ubiquitous multimedia, MUM ’10. ACM, New York; 2010.
Nurmi P, Koolwaaij J: Identifying meaningful locations. Mobile and ubiquitous systems: networking and services, 2006 third annual international conference on 2006, 1–8.
Eagle N, Pentland AS: Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 2009,63(7):1057–1066.CrossRef
Reades J, Calabrese F, Ratti C: Eigenplaces: analysing cities using the space-time structure of the mobile phone network. Environ Plan B, Plan Des 2009,36(5):824–836.CrossRef
Park J, Lee DS, González MC: The eigenmode analysis of human motion. J Stat Mech Theory Exp 2010.,2010(11): Article ID P11021
Karikoski J (2012) Handset-based data collection process and participant attitudes. Int J Handheld Comput Res (in press)Karikoski J (2012) Handset-based data collection process and participant attitudes. Int J Handheld Comput Res (in press)
HIIT OpenNetMap project. http://opennetmap.rista.fi
Karikoski J, Luukkainen S: Substitution in smartphone communication services. In Intelligence in next generation networks (ICIN), 2011 15th international conference on. IEEE Press, New York; 2011:313–318.CrossRef
Gan G, Ma C, Wu J: Data clustering: theory, algorithms, and applications. SIAM, Philadelphia; 2007. illustrated edn
Palla G, Derényi I, Farkas I, Vicsek T: Uncovering the overlapping community structure of complex networks in nature and society. Nature 2005,435(7043):814–818.CrossRef
Smoot ME, Ono K, Ruscheinski J, Wang PLL, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011,27(3):431–432.CrossRef
- Spatiotemporal correlations of handset-based service usages
- Open Access
- Available under Open Access This content is freely available online to anyone, anywhere at any time.
EPJ Data Science
- Online Date
- November 2012
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
- Author Affiliations
- 1. Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, P.O. Box 12200, Espoo, Finland
- 2. Department of Communications and Networking, School of Electrical Engineering, Aalto University, P.O. Box 13000, Helsinki, Finland