Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

  • Patrick Laube
  • Marc van Kreveld
  • Stephan Imfeld


Technological advances in position aware devices increase the availability of tracking data of everyday objects such as animals, vehicles, people or football players. We propose a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geospatial lifeline data. Our approach considers the object’s motion properties in an analytical space as well as spatial constraints of the object’s lifelines in geographic space. We discuss the geometric properties of the formalised patterns with respect to their efficient computation.


Convergence cluster detection motion moving point objects pattern matching proximity 


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  1. Aurenhammer F (1991) Voronoi diagrams: A survey of a fundamental geometric data structure. ACM Comput. Surv. 23(3):345–405CrossRefGoogle Scholar
  2. Batty M, Desyllas J, Duxbury E (2003) The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades. Int. J. Geographical Information Systems 17(7):673–697Google Scholar
  3. Bern M, Eppstein D (1997) Approximation algorithms for geometric problems. In Hochbaum DS (ed) Approximation Algorithms for NP-Hard Problems, PWS Publishing Company, Boston, MA, pp 296–345Google Scholar
  4. Casaer J, Hermy M, Coppin P, Verhagen R (1999) Analysing space use patterns by Thiessen polygon and triangulated irregular network interpolation: a nonparametric method for processing telemetric animal fixes. Int. J. Geographical Information Systems 13(5):499–511Google Scholar
  5. de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (2000) Computational Geometry — Algorithms and Applications. Springer, Berlin, 2nd editionGoogle Scholar
  6. Estivill-Castro V, Lee I (2002) Multi-level clustering and its visualization for exploratory data analysis. GeoInformatica 6(2):123–152CrossRefGoogle Scholar
  7. Ganskopp, D. (2001) Manipulating cattle distribution with salt and water in large arid-land pastures: a GPS/GIS assessment. Applied Animal Behaviour Science 73(4):251–262CrossRefGoogle Scholar
  8. Hornsby K, Egenhofer M (2002) Modeling moving objects over multiple granularities. Annals of Mathematics and Artificial Intelligence 36(1–2):177–194Google Scholar
  9. Iwase S, Saito H (2002) Tracking soccer player using multiple views. In IAPR Workshop on Machine Vision Applications, MVA Proceedings, pp 102–105Google Scholar
  10. Jain A, Duin R, Mao J (2000) Statistical pattern recognition: A review. IEEE Transactions on Pattern Recognition and Machine Intelligence 22(1):4–37Google Scholar
  11. Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Egenhofer M, Mark D (eds), Geographic Information Science, Second International Conference, GIScience 2002, Boulder, CO, USA, September 2002, LNCS 2478, Springer, Berlin, pp 132–144Google Scholar
  12. Mark, D (2003) Geographic information science: Defining the field. In: Duckham M, Goodchild M, Worboys M (eds), Foundations of Geographic Information Science, chap. 1, Taylor and Francis, London New York, pp 3–18Google Scholar
  13. Miller H (2003) What about people in geographic information science? Computers, Environment and Urban Systems 27(5):447–453CrossRefGoogle Scholar
  14. Miller H (2004) Tobler’s first law and spatial analysis. in preparation.Google Scholar
  15. Miller H, Han J (2001) Geographic data mining and knowledge discovery: An overview. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, Taylor and Francis, London New York, pp 3–32Google Scholar
  16. Miller H, Wu Y (2000) GIS software for measuring space-time accessibility in transportation planning and analysis. GeoInformatica 4(2):141–159CrossRefGoogle Scholar
  17. Mountain D, Raper J (2001) Modelling human spatio-temporal behaviour: A challenge for location-based services. Proceedings of GeoComputation, Brisbane, 6Google Scholar
  18. Openshaw S (1994) Two exploratory space-time-attribute pattern analysers relevant to GIS. In: Fotheringham S, Gogerson P (eds) GIS and Spatial Analysis, chap. 5, Taylor and Francis, London New York, pp 83–104Google Scholar
  19. Openshaw S, Turton I, MacGill J (1999) Using geographic analysis machine to analyze limiting long-term illness census data. Geographical and Environmental Modelling 3(1):83–99Google Scholar
  20. Pfoser D, Jensen C (1999) Capturing the uncertainty of moving-object representations. In: Gueting R, Papadias D, Lochowsky, F (eds) Advances in Spatial Databases, 6th International Symposium, SSD’99, Hong Kong, China, July 1999. LNCS 1651, Springer, Berlin Heidelberg, pp 111–131Google Scholar
  21. Ramos E (1999) On range reporting, ray shooting and k-level construction. In: Proc. 15th Annu. ACM Symp. on Computational Geometry, pp 390–399Google Scholar
  22. Roddick J, Hornsby K, Spiliopoulou M (2001) An updated bibliography of temporal, spatial, and spatio-temporal data mining research. In: Roddick J, Hornsby K (eds), Temporal, spatial and spatio-temporal data mining, TSDM 2000, LNAI 2007, Springer, Berlin Heidelberg, pp 147–163Google Scholar
  23. Sibbald AM, Hooper R, Gordon IJ, Cumming S (2001) Using GPS to study the effect of human disturbance on the behaviour of the red deer stags on a highland estate in Scotland. In: Sibbald A, and Gordon IJ (eds) Tracking Animals with GPS, Macaulay Institute, pp 39–43Google Scholar
  24. Smyth C (2001) Mining mobile trajectories. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, Taylor and Francis, London New York, pp 337–361Google Scholar
  25. Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Economic Geography 46(2):234–240Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Patrick Laube
    • 1
  • Marc van Kreveld
    • 2
  • Stephan Imfeld
    • 1
  1. 1.Department of GeographyUniversity of ZurichZurichSwitzerland
  2. 2.Department of Computer ScienceUtrecht UniversityUtrechtThe Netherlands

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