Impact of Natural and Social Events on Mobile Call Data Records – An Estonian Case Study

  • Hendrik Hiir
  • Rajesh SharmaEmail author
  • Anto Aasa
  • Erki Saluveer
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)


Mobile Call Data Records (CDR) can be used for identifying human behavior. For example, researchers have studied mobile CDR to understand the social fabric of a country or for predicting the human mobility patterns. Additionally, CDR data has been combined with external data, for example, financial data to understand socio-economic patterns. In this paper, we study an anonymised CDR dataset provided by one of the biggest mobile operators in Estonia with two objectives. First, we explore the data to identify and interpret social network patterns. Our study points that mobile calling network is fragmented and sparse in Estonia. Second, we study the impact of natural and social events on mobile call activity. Our results show that these activities do have an impact on the calling activity. To the best of our knowledge, this is the first study, which has analysed the impact of varied types of events on mobile calling activity specifically in Estonian landscape.


Mobile Call Data Records Social network analysis Sociocultural analysis 



This work has been supported in part by EU H2020 project SoBigData and Estonian Research Council project Understanding the Vicious Circles of Segregation. A Geographic Perspective (PUT PRG306). We are also thankful to Estonian mobile operator for providing us the data.


  1. 1.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994) Google Scholar
  2. 2.
    Lambiotte, R., Blondel, V., de Kerchove, C., Huens, E., Prieur, C., Smoreda, Z., Van Dooren, P.: Geographical dispersal of mobile communication networks. Phys. A 387, 5317–5325 (2008)CrossRefGoogle Scholar
  3. 3.
    Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.-L.: Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. U.S.A. 104, 7332–7336 (2007)CrossRefGoogle Scholar
  4. 4.
    Ponieman, N.B., Sarraute, C., Minnoni, M., Travizano, M., Rodriguez Zivic, P., Salles, A.: Mobility and sociocultural events in mobile phone data records. AI Commun. 29, 77–86 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Song, C., Zehui, Q., Blumm, N., Barabási, A.-L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  7. 7.
    Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., Willinger, W.: Human mobility modeling at metropolitan scales. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys 2012, pp. 239–252. ACM, New York (2012)Google Scholar
  8. 8.
    Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D., Tatem, A.J.: Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. 111(45), 15888–15893 (2014) CrossRefGoogle Scholar
  9. 9.
    Williams, N.E., Thomas, T.A., Dunbar, M., Eagle, N., Dobra, A.: Measures of human mobility using mobile phone records enhanced with GIS data. CoRR, abs/1408.5420 (2014)Google Scholar
  10. 10.
    Sørensen, A.Ø., Bjelland, J., Bull-Berg, H., Landmark, A.D., Akhtar, M.M., Olsson, N.O.: Use of mobile phone data for analysis of number of train travellers. J. Rail Transp. Plan. Manage. 8(2), 123–144 (2018)Google Scholar
  11. 11.
    Leo, Y., Karsai, M., Sarraute, C., Fleury, E.: Correlations of consumption patterns in social-economic networks. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, pp. 493–500 (2016)Google Scholar
  12. 12.
    Horanont, T., Phithakkitnukoon, S., Leong, T.W., Sekimoto, Y., Shibasaki, R.: Weather effects on the patterns of people’s everyday activities: a study using GPS traces of mobile phone users. PLoS ONE 8, e81153 (2013)CrossRefGoogle Scholar
  13. 13.
    Eagle, N., Pentland, A.(Sandy), Lazer, D.: Mobile phone data for inferring social network structure. In: Liu, H., Salerno, J.J., Young, M.J. (eds.) Social Computing, Behavioral Modeling, and Prediction, pp. 79–88. Springer, Boston (2008)Google Scholar
  14. 14.
    Ahas, R., Mark, Ü.: Location based services—new challenges for planning and public administration? Futures 37(6), 547–561 (2005)CrossRefGoogle Scholar
  15. 15.
    Farrahi, K., Emonet, R., Cebrian, M.: Predicting a community’s flu dynamics with mobile phone data. In: Computer-Supported Cooperative Work and Social Computing, Vancouver, Canada, March 2015Google Scholar
  16. 16.
    Singh, V.K., Freeman, L., Lepri, B., Pentland, A.: Predicting spending behavior using socio-mobile features. In: International Conference on Social Computing, SocialCom, Washington, DC, USA, pp. 174–179 (2013)Google Scholar
  17. 17.
    Ratti, C., Frenchman, D., Pulselli, R.M., Williams, S.: Mobile landscapes: using location data from cell phones for urban analysis. Environ. Plan. 33(5), 727–748 (2006)CrossRefGoogle Scholar
  18. 18.
    Toole, J.L., Colak, S., Sturt, B., Alexander, L.P., Evsukoff, A., González, M.C.: The path most traveled: travel demand estimation using big data resources. Transp. Res. Part C Emerg. Technol. 58, 162–177 (2015). Big Data in Transportation and Traffic EngineeringCrossRefGoogle Scholar
  19. 19.
    Ahas, R., Aasa, A., Mark, Ü., Pae, T., Kull, A.: Seasonal tourism spaces in Estonia: case study with mobile positioning data. Tour. Manag. 28, 898–910 (2007)CrossRefGoogle Scholar
  20. 20.
    Kumar, M., Hanumanthappa, M., Kumar, T.V.S.: Crime investigation and criminal network analysis using archive call detail records. In: 2016 Eighth International Conference on Advanced Computing (ICoAC), pp. 46–50, January 2017Google Scholar
  21. 21.
    Khan, E.S., Azmi, H., Ansari, F., Dhalvelkar, S.: Simple implementation of criminal investigation using call data records (CDRs) through big data technology. In: 2018 International Conference on Smart City and Emerging Technology (ICSCET), pp. 1–5, January 2018Google Scholar
  22. 22.
  23. 23.
    Tana öösel näeme taiskuud (2015).
  24. 24.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hendrik Hiir
    • 1
  • Rajesh Sharma
    • 1
    Email author
  • Anto Aasa
    • 2
  • Erki Saluveer
    • 3
  1. 1.Institute of Computer ScienceUniversity of TartuTartuEstonia
  2. 2.Department of GeographyUniversity of TartuTartuEstonia
  3. 3.OÜ PositiumTartuEstonia

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