Context-Aware Similarity of Trajectories

  • Maike Buchin
  • Somayeh Dodge
  • Bettina Speckmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7478)


The movement of animals, people, and vehicles is embedded in a geographic context. This context influences the movement. Most analysis algorithms for trajectories have so far ignored context, which severely limits their applicability. In this paper we present a model for geographic context that allows us to integrate context into the analysis of movement data. Based on this model we develop simple but efficient context-aware similarity measures. We validate our approach by applying these measures to hurricane trajectories.


Movement data geographic context similarity measures 


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  1. 1.
    Alt, H., Godau, M.: Computing the Fréchet distance between two polygonal curves. International Journal of Computational Geometry and Applications 5, 75–91 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Andrienko, G., Andrienko, N., Heurich, M.: An event-based conceptual model for context-aware movement analysis. International Journal of Geographical Information Science 25, 1347–1370 (2011)CrossRefGoogle Scholar
  3. 3.
    Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: Proc. 31st International Conference on Very Large Data Bases, pp. 853–864 (2005)Google Scholar
  4. 4.
    Buchin, K., Buchin, M., Gudmundsson, J.: Constrained free space diagrams: a tool for trajectory analysis. International Journal of Geographical Information Science 24, 1101–1125 (2010)CrossRefGoogle Scholar
  5. 5.
    Buchin, K., Buchin, M., Gudmundsson, J., Löffler, M., Luo, J.: Detecting commuting patterns by clustering subtrajectories. International Journal of Computational Geometry and Applications 21(3), 253–282 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Buchin, K., Buchin, M., van Kreveld, M.J., Luo, J.: Finding long and similar parts of trajectories. Computational Geometry: Theory and Applications 44(9), 465–476 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Cheung, Y.K., Daescu, O.: Fréchet Distance Problems in Weighted Regions. In: Dong, Y., Du, D.-Z., Ibarra, O. (eds.) ISAAC 2009. LNCS, vol. 5878, pp. 97–111. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Dodge, S.: Exploring Movement Using Similarity Analysis. PhD thesis, University of Zurich (2011)Google Scholar
  9. 9.
    Elsner, J., Kara, A.: Hurricanes of the North Atlantic: Climate and society. Oxford University Press (1999)Google Scholar
  10. 10.
    Frentzos, E., Gratsias, K., Theodoridis, Y.: Index-based most similar trajectory search. In: Proc. 23rd IEEE International Conference on Data Engineering, pp. 816–825 (2007)Google Scholar
  11. 11.
    Hwang, J.-R., Kang, H.-Y., Li, K.-J.: Searching for Similar Trajectories on Road Networks Using Spatio-temporal Similarity. In: Manolopoulos, Y., Pokorný, J., Sellis, T.K. (eds.) ADBIS 2006. LNCS, vol. 4152, pp. 282–295. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Laube, P., Purves, R.: How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS 15(3), 401–418 (2011)CrossRefGoogle Scholar
  13. 13.
    Miller, H.J., Han, J.: Geographic Data Mining and Knowledge Discovery, 2nd edn. Taylor & Francis Group (2009)Google Scholar
  14. 14.
    Mountain, D.: The dimensions of context and its role in mobile information retrieval. SIGSPATIAL Special 3, 71–77 (2011)CrossRefGoogle Scholar
  15. 15.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems 27, 267–289 (2006)CrossRefGoogle Scholar
  16. 16.
    Nathan, R., Getz, W.M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., Smouse, P.E.: A movement ecology paradigm for unifying organismal movement research. Proc. National Academy of Sciences of the United States of America 105(49), 19052–19059 (2008)CrossRefGoogle Scholar
  17. 17.
    Nutanong, S., Jacox, E.H., Samet, H.: An incremental Hausdorff distance calculation algorithm. In: Proc. 37th International Conference on Very Large Data Bases, vol. 4(8), pp. 506–517 (2011)Google Scholar
  18. 18.
    Sinha, G., Mark, D.M.: Measuring similarity between geospatial lifelines in studies of environmental health. Journal of Geographical Systems 7(1), 115–136 (2005)CrossRefGoogle Scholar
  19. 19.
    Tiakas, E., Papadopoulos, A., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Searching for similar trajectories in spatial networks. Journal of Systems and Software 82(5), 772–788 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maike Buchin
    • 1
  • Somayeh Dodge
    • 2
  • Bettina Speckmann
    • 1
  1. 1.Dept. of Mathematics and Computer ScienceTU EindhovenThe Netherlands
  2. 2.Dept. of Civil, Environmental, and Geodetic EngineeringOhio State UniversityUSA

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