Basic Concepts of Movement Data

  • N. Andrienko
  • G. Andrienko
  • N. Pelekis
  • S. Spaccapietra

From ancient days, people have observed various moving entities, from insects and fishes to planets and stars, and investigated their movement behaviours. Although methods that were used in earlier times for observation, measurement, recording, and analysis of movements are very different from modern technologies, there is still much to learn from past studies. First, this is the thorough attention paid to the multiple aspects of movement. These include not only the trajectory (path) in space, characteristics of motion itself such as speed and direction, and their dynamics over time but also characteristics and activities of the entities that move. Second, this is the striving to relate movements to properties of their surroundings and to various phenomena and events.


Movement Data Time Moment Movement Behaviour Time Path Question Type 
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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  1. 1.
    N. Andrienko and G. Andrienko. Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer, 2006.Google Scholar
  2. 2.
    J. Bertin. Semiology of Graphics. Diagrams, Networks, Maps. University of Wisconsin Press, 1983.Google Scholar
  3. 3.
    U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, pp. 1–34. MIT press, 1996.Google Scholar
  4. 4.
    R.H. Gueting and M. Schneider. Moving Objects Databases. Elsevier, 2005.Google Scholar
  5. 5.
    T. Hagerstrand. What about people in regional science? Papers of the Regional Science Association, 24:7–21, 1970.Google Scholar
  6. 6.
    K. Hornsby and M.J. Egenhofer. Modeling moving objects over multiple granularities. Annual Mathematics Artificial Intelligence, 36(1–2):177–194, 2002.zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    M.-J. Kraak. The space–time cube revisited from a geovisualization perspective. In Proceedings of the Twenty-First International Cartographic Conference (ICC’03), pp. 1988–1995, 2003.Google Scholar
  8. 8.
    P. Laube, S. Imfeld, and R. Weibel. Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science, 19(6):639–668, 2005.CrossRefGoogle Scholar
  9. 9.
    Merriam-Webster’s Collegiate Dictionary, 10 edn.. Merriam-Webster, Incorporated, 1999.Google Scholar
  10. 10.
    H. Miller. A measurement theory for time geography. Geographical Analysis, 37:17–45, 2005.CrossRefGoogle Scholar
  11. 11.
    H. Miller. Modeling accessibility using space–time prism concepts within geographical information systems: Fourteen years on. In Classics of International Journal of Geographical Information Science, pp. 177–182. CRC Press, 2006.Google Scholar
  12. 12.
    H. Miller and J. Han. Geographic data mining and knowledge discovery: An overview. In Geographic Data Mining and Knowledge Discovery, pp. 3–32. Taylor and Francis, 2001.Google Scholar
  13. 13.
    A. Moore, P. Whigwham, A. Holt, C. Alridge, and K. Hodge. A time geography approach to the visualization of sport. In Proceedings of the Seventh International Conference on Geocomputation, 2003.Google Scholar
  14. 14.
    J. Thomas and K. Cook. Illuminating the Path. The Research and development Agenda for Visual Analytics. IEEE Computer Society, 1983.Google Scholar
  15. 15.
    E. Tufte. Visual Display of Quantitative Information. Graphics Press, 1983.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • N. Andrienko
    • 1
  • G. Andrienko
    • 1
  • N. Pelekis
    • 2
  • S. Spaccapietra
    • 3
  1. 1.Fraunhofer Institut Intelligente Analyse- und InformationssystemeSankt AugustinGermany
  2. 2.Computer Technology Institute (CTI) and Department of InformaticsUniversity of PiraeusGreece
  3. 3.Database LaboratoryÉcole Polytechnique Fédérale de LausanneSwitzerland

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