International Internet of Things Summit

IoT360 2014: Internet of Things. IoT Infrastructures pp 192-197 | Cite as

Understanding the Impact of Data Sparsity and Duration for Location Prediction Applications

  • Alasdair Thomason
  • Matthew Leeke
  • Nathan Griffiths
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 151)


As mobile devices capable of sensing location have become pervasive, the collection and transmission of location data has become commonplace, enabling the creation of models of behaviour that support location prediction. With such devices often heavily resource-constrained, the nature of data used in location prediction must be understood in order to optimise storage and processing requirements. This paper specifically explores data sparsity and collection duration. The results presented provide insight which suggest: (i) a relationship of diminishing returns in predictive accuracy when collecting user location data at increased rates over a fixed period, and (ii) the duration over which a fixed size sample of location data is collected has a greater impact on predicative accuracy than data sparsity.


Collection Data Duration Location prediction Sparsity 


  1. 1.
    Noulas, A., et al. Mining User Mobility Features for Next Place Prediction in Location-Based Services. In: ICDM, pp. 1038–1043 (2012)Google Scholar
  2. 2.
    Roy, A., et al. Location aware resource management in smart homes. In: PerCom, pp. 481–488 (2003)Google Scholar
  3. 3.
    Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with GPS. In: ISWC, pp. 101–108 (2002)Google Scholar
  4. 4.
    Lu, E., et al.: Mining cluster-based temporal mobile sequential patterns in location-based service environments. IEEE Trans. Knowl. data Eng. 23(6), 914–927 (2011)CrossRefGoogle Scholar
  5. 5.
    Fukano, J., et al.: A next location prediction method for smartphones using blockmodels, pp. 1–4. IEEE Virtual Reality (2013)Google Scholar
  6. 6.
    Akoush, S. et al.: Bayesian Learning of Neural Networks for Mobile User Position Prediction. Computer Communications and Networks, pp. 1234–1239 (2007)Google Scholar
  7. 7.
    Yava, G., et al.: A data mining approach for location prediction in mobile environments. Data Knowl. Eng. 54(2), 121–146 (2005)CrossRefGoogle Scholar
  8. 8.
    Google. Our Mobile Planet. Accessed July 2014
  9. 9.
    Cao, H., et al.: Mining frequent spatio-temporal sequential patterns. In: ICDM, pp. 82–89 (2005)Google Scholar
  10. 10.
    Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Comparison of different methods for next location prediction. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 909–918. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  11. 11.
    Gomes, J.B., Phua, C., Krishnaswamy, S.: Where will you go? mobile data mining for next place prediction. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 146–158. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  12. 12.
    Nguyen, L., et al. PnLUM : System for prediction of next location for users. In: Mobile Data Challenge by Nokia Workshop at Pervasive (2012)Google Scholar
  13. 13.
    Vintan, L., et al. Person Movement Prediction Using Neural Networks. In: 1st Workshop on Modeling and Retrieval of Context (2004)Google Scholar
  14. 14.
    Gambs, S., et al. Next place prediction using mobility markov chains. In: 1st Workshop on Measurement, Privacy, and Mobility, pp. 1–6 (2012)Google Scholar
  15. 15.
    Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.T.: NextPlace: a spatio-temporal prediction framework for pervasive systems. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 152–169. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  16. 16.
    Vukovic, M., Adaptive User Movement Prediction for Advanced Location-aware Services. In: SoftCOM, pp. 343–347 (2009)Google Scholar
  17. 17.
    Zhang, Y., et al. Location prediction model based on bayesian network theory. In: GLOBECOM, pp. 1–6 (2009)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • Alasdair Thomason
    • 1
  • Matthew Leeke
    • 1
  • Nathan Griffiths
    • 1
  1. 1.University of WarwickCoventryUK

Personalised recommendations