Encyclopedia of GIS

2008 Edition
| Editors: Shashi Shekhar, Hui Xiong

Movement Patterns in Spatio‐temporal Data

  • Joachim Gudmundsson
  • Patrick Laube
  • Thomas Wolle
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-35973-1_823

Synonyms

Motion patterns; Trajectory patterns; Exploratory data analysis; Flocking; Converging; Collocation, spatio‐temporal; Indexing trajectories; TPR-trees; R-tree, multi-version; Indexing, parametric space; Indexing, native space; Association rules, spatio‐temporal; Pattern, moving cluster; Pattern, periodic; Pattern, leadership; Pattern, flock; Pattern, encounter

Definition

Spatio‐temporal data is any information relating space and time. This entry specifically considers data involving point objects moving over time. The terms entity and trajectory will refer to such a point object and the representation of its movement, respectively. Movement patterns in such data refer to (salient) events and episodes expressed by a set of entities.

Keywords

Movement Pattern Association Rule Range Query Digital Terrain Model Association Rule Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Recommended Reading

  1. 1.
    Andersson, M., Gudmundsson, J., Laube, P., Wolle T.: Reporting leadership patterns among trajectories. In: Proceedings of the 2007 ACM Symposium on Applied Computing, pp. 3–7. ACM Press, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Andrienko, N.V., Andrienko, G.L.: Interactive maps for visual data exploration. Int. J. Geogr. Inf. Sci. 13(4), 355–374 (2003)CrossRefGoogle Scholar
  3. 3.
    Batty, M.,Desyllas, J., Duxbury, E.: The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades. Int. J. Geogr. Inf. Sci. 17(7), 673–697 (2003)CrossRefGoogle Scholar
  4. 4.
    Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. In: Proceedings of the 14th European Symposium on Algorithms. Lecture Notes in Computer Science, vol. 4168 pp. 660–671. Springer, Berlin, Heidelberg (2006)Google Scholar
  5. 5.
    Brillinger, D.R., Preisler, H.K., Ager, A.A., Kie, J.G.: An exploratory data analysis (EDA) of the paths of moving animals. J. Stat. Plan. Inf. 122(2), 43–63 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Dobson, J.E., Fisher, P.F.: Geoslavery. IEEE Technology and Society Magazine 22(1), 47–52 (2003)CrossRefGoogle Scholar
  7. 7.
    Dumont, B., Boissy, A., Achard, C., Sibbald, A.M., Erhard, H.W.: Consistency of animal order in spontaneous group movements allows the measurement of leadership in a group of grazing heifers. Appl. Anim. Behav. Sci. 95(1–2), 55–66 (2005)CrossRefGoogle Scholar
  8. 8.
    Dykes, J.A., Mountain, D.M.: Seeking structure in records of spatio‐temporal behaviour: visualization issues, efforts and application. Comput. Stat. Data Anal. 43(4), 581–603 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Hadjieleftheriou, M., Kollios, G., Tsotras, V.J., Gunopulos, D.: Indexing spatio‐temporal archives. The VLDB J. 15(2), 143–164 (2006)CrossRefGoogle Scholar
  10. 10.
    Hägerstrand, T.: What about people in regional science. Pap. Region. Sci. Assoc. 24, 7–21 (1970)CrossRefGoogle Scholar
  11. 11.
    Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio‐temporal data. In: Medeiros, C.B., Egenhofer, M.J., Bertino, E. (eds.) Proceedings of the 9th International Symposium on Advances in Spatial and Temporal Databases. Lecture Notes in Computer Science, vol. 3633, pp. 364–381. Springer, Berlin, Heidelberg (2005)Google Scholar
  12. 12.
    Kwan, M.P.: Interactive geovisualization of activity‐travel patterns using three dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research Part C 8(1–6), 185–203 (2000)CrossRefGoogle Scholar
  13. 13.
    Langran, G.: Time in Geographic Information Systems. PhD thesis, University of Washington (1999)Google Scholar
  14. 14.
    Laube, P., van Kreveld, M., Imfeld, S.: Finding REMO – detecting relative motion patterns in geospatial lifelines. In: Fisher, P.F. (ed.) Developments in Spatial Data Handling, Proceedings of the 11th International Symposium on Spatial Data Handling, pp. 201–214. Springer, Berlin Heidelberg (2004)Google Scholar
  15. 15.
    Lorentzos, N.A.: A Formal Extension of the Relational Model for the Representation and Manipulation of Generic Intervals. PhD thesis, Birbeck College, University of London (1988)Google Scholar
  16. 16.
    Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the 10th ACM International Conference On Knowledge Discovery and Data Mining, pp. 236–245. ACM Press, New York (2004)Google Scholar
  17. 17.
    Šaltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 331–342 (2000)Google Scholar
  18. 18.
    Verhein, F., Chawla, S.: Mining spatio‐temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In: Proceedings of the 11th International Conference on Database Systems for Advanced Applications. Lecture Notes in Computer Science, vol. 3882, pp. 187–201. Springer, Berlin, Heidelberg (2006)Google Scholar
  19. 19.
    Wilensky, U.: Netlogo flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Joachim Gudmundsson
    • 1
  • Patrick Laube
    • 2
  • Thomas Wolle
    • 1
  1. 1.NICTASydneyAustralia
  2. 2.Department of GeomaticsUniversity of MelbourneMelbourneAustralia