Spatiotemporal correlations of handset-based service usages
© Jo et al.; licensee Springer. 2012
Received: 7 April 2012
Accepted: 9 October 2012
Published: 6 November 2012
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.
Understanding macroscopic socio-economic phenomena of a large number of individuals has been extensively studied by means of social, physical, and computational sciences [1–3]. Recent access to large-scale digital datasets on human dynamics and social interaction has enabled us to quantitatively investigate the structure and dynamics of human communication networks. Indeed, researchers have studied various datasets, ranging from email and mobile phone communications to social network services, e.g. Twitter and Facebook [4–11]. Mobile phones or handsets are now actively utilized to accurately measure or sense human behavior because the handsets equipped with a variety of sensors, including GPS and WiFi, are carried around by the users everyday and all day through. Highly resolved location data collected from handsets have been recently used to uncover human mobility patterns [12–20]. The reliability of data collected from handsets, i.e. ‘behavioral’ data, was tested in the serial studies conducted within the frame of MIT’s Reality Mining project [17, 18, 21]. It was shown that the behavioral data are at least comparable to self-report survey data in terms of friendship network and even capturing information that self-reports are missing .
The handset usage patterns are known to be diverse among users when measured by the number or duration of the phone sessions and by the amount of data received, to name a few [22, 23]. Within the individual handset usage patterns, temporal inhomogeneities due to circadian and weekly cycles were also reported , which are in close relation to the spatial inhomogeneities, such as nighttime at home and daytime in office. Therefore, for conducting a comprehensive study, it is important to identify the context characterizing the situation of handset user, and then to understand how the context affects service usage patterns [23–27]. However, it is only very recently when the effect of context on the handset-based service usages was investigated. But so far the analysis has been conducted mostly at the aggregate level, while the temporal diversities of service usage among users have been ignored .
In this paper, we study spatiotemporal correlations of the service usage patterns of individual users by analyzing a handset-based dataset. This dataset was collected from 124 users’ handsets for over 16 months as a part of the OtaSizzle project at Aalto University, Finland . A software installed on handsets collected information about the handset’s locations and usages of various services, including web domain visits, applications, emails, voice calls, and short message services, with the resolution of seconds in time and mobile network base stations spatially. After constructing spatiotemporal trajectories of the users we identify several contexts that are meaningful to them by using the context detection method . Other methods include, for example, places of interest or meaningful locations [29, 30] and eigenmode analysis [31–33]. Then, we find correlations between the spatiotemporal trajectories and the service usage patterns. We observe the similarity and diversity in temporal patterns of the service usages and discuss their temporal correlations, time-ordering behavior between services, and behavioral overlap network based on the clustering results. Our approach provides a useful framework for handset-based data analysis, and hence it would be important for better design of information and communications technology (ICT) enabled social environments and services.
This paper is organized as follows. In Section 2 we describe the data collection and preparation methods. In Section 3 several contexts for each user are identified by means of the context detection method applied to user’s spatiotemporal trajectory. In Section 4 we uncover the spatiotemporal correlations and the similarity and diversity in temporal patterns of the service usages. Finally, we summarize the results with concluding remarks in Section 5.
2 Handset-based dataset
2.1 Data collection method
The handset-based dataset in this study was collected by the MobiTrack software installed on Nokia Symbian smartphones of 183 participants or users from September 2009 to December 2010, i.e. for a period spanning about 16 months. All users were students and staff members of Aalto University, Finland and identified as early adopters of mobile phones and services . The dataset was anonymized so that no personal information of the users could be obtained. We consider only 124 users with the overall duration of handset usage longer than 30 days, see Section 3 for details.
For service usage data we consider five services: web domain visit (web), application (app), email, voice call (call), and short message service (SMS). Each service usage or event was recorded with a timestamp with one second resolution together with service-specific relevant information. In the case of web domain visits, a URL (Uniform Resource Locator) was extracted and recorded whether it was visited via browser or widget. Only the applications visible in the foreground of the handset were recorded so that no process or application running in the background was considered. The records of communication services, such as email, call, and SMS, include the information on whether the user was an initiator or receiver of the communication event, and on the communication partner if available. For more information regarding the data collection method, see .
2.2 Data preparation method
We also ignore some user-generated application events associated with other service usages, corresponding to 17% of entire events. For example, the user opens the messaging application when sending or receiving SMSs. These application events might lead to artificial correlations between different service usages. In addition, corrupted events, less than 0.1% of the whole dataset, have been ignored or manually corrected. Finally, we have 792,971 web domain visits, 433,726 application events, 17,976 emails, 79,779 calls, and 79,283 SMSs in the service usage dataset.
3 Context detection from spatiotemporal pattern
duration on weekdays (),
duration on weekdays between 0 AM and 6 AM (), and
duration on weekdays between 10 AM and 4 PM ().
4 Spatiotemporal correlations of service usages
We investigate correlations between users’ spatiotemporal trajectories and their service usage patterns. Here five services, such as web domain visit (web), application (app), email, voice call (call), and short message service (SMS), are considered and each service is denoted by s. The spatiotemporal correlation of service usages for user i is fully characterized by the number of events corresponding to the service s in the cell c and at time t, denoted by . For gaining contextual understanding of correlations we consider the contexts instead of cells, i.e. , where the summation is over c detected as context C.
4.1 Contextual correlations of service usages
where denotes the duration of user i for context C. The results are shown in Figure 8 (right). Despite of the diversity among users, the means of intensities of different services for the same context have to some extent similar values. The large mean of intensity of email usage in Office might be due to the fact that users prefer emails to calls or SMSs in classes or laboratories during the working time. The large mean of intensity of web usage at Else could be the result of users killing time by surfing the webpages while on the move. One could also say that users while abroad tend to use SMSs more than other communication services. Finally, for all services, only the means of intensity at Home turn out to be less than 1 and most inactive, which could be partly because users have many other activities to do at Home.
4.2 Temporal correlations and time-ordering of service usages
where and are weights for normalization. In addition we obtain the weekday and weekend event rates averaged over all users.
where denotes the number of services the user i have used.
4.3 Clustering and overlaps in temporal patterns of service usage
where denotes the number of users showing any activity in service s. We similarly define the service-averaged event rates for each user for the clustering, to be denoted by avg. In each case we set the number of clusters as and the cluster index is denoted by . Clustering has been conducted 2,000 times with different initial conditions and here we present the result maximizing the quality of clustering or validity index, defined as the minimum inter-cluster distance divided by the sum of intra-cluster distances .
k -means clustering results for weekly patterns of service usages
We have investigated spatiotemporal correlations and temporal diversities of service usages by analyzing a handset-based dataset collected from 124 users for over 16 months. The dataset consists of locations and service usages. After constructing the precise spatiotemporal trajectory for each user based on the location dataset, we identify several meaningful places or contexts by means of context detection method. As contexts, Home, Office, Other meaningful place, Elsewhere, and Abroad are considered. We showed how the context affects the service usage patterns of users, including their web domain visit (web), application (app), email, voice call (call), and short message service (SMS).
In this study we have found the similarity and diversity of weekly patterns among users and services, in terms of temporal correlations, time-ordering behavior between services, and overlap network based on clustering. The services used at the same time (at different times) of the week lead to the positive (negative) correlations between them, which can be interpreted as being complementary (substitutive) to each other. By conducting the event-based analysis instead of weekly patterns we observe the time-ordering behavior between services, such that communication services, i.e. email, call, and SMS, are followed by the non-communication services, i.e. web and app. Finally, the similarity and diversity of weekly patterns of service usages enable us to classify users into several different clusters, e.g. as characterized by the morning-type or evening-type usage patterns, except for the web and email usages. The behavioral overlap network constructed based on the clustering results can be used to reveal the communication or real social network structure of users.
Our findings on the spatiotemporal correlations of service usage patterns for different contexts enable us to better understand the behavior of humans and what that implies. This is also important for better design of information and communications technology (ICT) enabled social environments and services. However, more detailed analysis with higher resolution is required to reveal the underlying mechanism or the origin of spatiotemporal correlations.
The research data were collected in the OtaSizzle project that is funded by Aalto University’s MIDE program and Helsinki University of Technology TKK’s ‘Technology for Life’ campaign donations from private companies and communities. The authors thank MobiTrack Innovations Ltd. for providing the mobile audience measurement platform. The sponsoring from Nokia and Elisa to this work is also acknowledged. Financial support by Aalto University postdoctoral program (HJ), from EU’s 7th Framework Program’s FET-Open to ICTeCollective project no. 238597, by the Academy of Finland, the Finnish Center of Excellence program 2006-2011, project no. 129670 (MK, KK), and by Future Internet Graduate School and MoMIE project (JK) are gratefully acknowledged.
- Goyal S: Connections: an introduction to the network economy. Princeton University Press, Princeton; 2009.Google Scholar
- Castellano C, Fortunato S, Loreto V: Statistical physics of social dynamics. Rev Mod Phys 2009,81(2):591–646. 10.1103/RevModPhys.81.591View ArticleGoogle Scholar
- 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. 10.1126/science.1167742View ArticleGoogle Scholar
- Eckmann JP, Moses E, Sergi D: Entropy of dialogues creates coherent structures in e-mail traffic. Proc Natl Acad Sci USA 2004,101(40):14333–14337. 10.1073/pnas.0405728101MATHMathSciNetView ArticleGoogle Scholar
- Barabási AL: The origin of bursts and heavy tails in human dynamics. Nature 2005, 435: 207–211. 10.1038/nature03459View ArticleGoogle Scholar
- 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. 10.1073/pnas.0610245104View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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. 10.1016/j.socnet.2008.07.002View ArticleGoogle Scholar
- Kovanen L, Karsai M, Kaski K, Kertész J, Saramäki J: Temporal motifs in time-dependent networks. J Stat Mech Theory Exp 2011.,2011(11):P11005View ArticleGoogle Scholar
- Jo HH, Karsai M, Kertész J, Kaski K: Circadian pattern and burstiness in mobile phone communication. New J Phys 2012., 14:013055Google Scholar
- Karsai M, Kaski K, Barabási AL, Kertész J: Universal features of correlated bursty behaviour. Sci Rep 2012., 2: 397Google Scholar
- González MC, Hidalgo CA, Barabási AL: Understanding individual human mobility patterns. Nature 2008,453(7196):779–782. 10.1038/nature06958View ArticleGoogle Scholar
- 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):224015View ArticleGoogle Scholar
- Wang P, González MC, Hidalgo CA, Barabási AL: Understanding the spreading patterns of mobile phone viruses. Science 2009,324(5930):1071–1076. 10.1126/science.1167053View ArticleGoogle Scholar
- Song C, Qu Z, Blumm N, Barabási AL: Limits of predictability in human mobility. Science 2010,327(5968):1018–1021. 10.1126/science.1177170MATHMathSciNetView ArticleGoogle Scholar
- Song C, Koren T, Wang P, Barabasi AL: Modelling the scaling properties of human mobility. Nat Phys 2010,6(10):818–823. 10.1038/nphys1760View ArticleGoogle Scholar
- Eagle N, Pentland A: Reality mining: sensing complex social systems. Pers Ubiquitous Comput 2006,10(4):255–268. 10.1007/s00779-005-0046-3View ArticleGoogle Scholar
- 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. 10.1073/pnas.0900282106View ArticleGoogle Scholar
- 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):L07003Google Scholar
- Bagrow JP, Lin YR: Mesoscopic structure and social aspects of human mobility. PLoS ONE 2012.,7(5): e37676View ArticleGoogle Scholar
- 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. 10.1016/j.pmcj.2011.09.004View ArticleGoogle Scholar
- 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.Google Scholar
- 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.Google Scholar
- Dey AK: Understanding and using context. Pers Ubiquitous Comput 2001, 5: 4–7. 10.1007/s007790170019View ArticleGoogle Scholar
- Verkasalo H (2009) Handset-based analysis of mobile service usage. PhD thesis, Helsinki University of Technology, Espoo, FinlandGoogle Scholar
- Soikkeli T (2011) The effect of context on smartphone usage sessions. Master’s thesis, Aalto University, Espoo, Finland.http://aalto-fi.academia.edu/TapioSoikkeli/PapersGoogle Scholar
- Karikoski J, Soikkeli T: Contextual usage patterns in smartphone communication services. Pers Ubiquitous Comput 2011. doi:10.1007/s00779–011–0503–0Google Scholar
- OtaSizzle project. http://sizl.org 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.Google Scholar
- Nurmi P, Koolwaaij J: Identifying meaningful locations. Mobile and ubiquitous systems: networking and services, 2006 third annual international conference on 2006, 1–8.Google Scholar
- Eagle N, Pentland AS: Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 2009,63(7):1057–1066. 10.1007/s00265-009-0739-0View ArticleGoogle Scholar
- 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. 10.1068/b34133tView ArticleGoogle Scholar
- Park J, Lee DS, González MC (2010) The eigenmode analysis of human motion. J Stat Mech Theory Ex 2010(11):P11021View ArticleGoogle Scholar
- Karikoski J (2012) Handset-based data collection process and participant attitudes. Int J Handheld Comput Res (inpress)Google Scholar
- HIIT OpenNetMap project. http://opennetmap.rista.fi/ HIIT OpenNetMap project. http://opennetmap.rista.fi/
- OpenCellID. http://www.opencellid.org OpenCellID. http://www.opencellid.org
- Location-API. http://location-api.com Location-API. http://location-api.com
- 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.View ArticleGoogle Scholar
- Gan G, Ma C, Wu J: Data clustering: theory, algorithms, and applications. SIAM, Philadelphia; 2007. illustrated edn illustrated ednView ArticleGoogle Scholar
- 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. 10.1038/nature03607View ArticleGoogle Scholar
- 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. 10.1093/bioinformatics/btq675View ArticleGoogle Scholar
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