Visual Analytics Methods for Movement Data

  • G. Andrienko
  • N. Andrienko
  • I. Kopanakis
  • A. Ligtenberg
  • S. Wrobel

All the power of computational techniques for data processing and analysis is worthless without human analysts choosing appropriate methods depending on data characteristics, setting parameters and controlling the work of the methods, interpreting results obtained, understanding what to do next, reasoning, and drawing conclusions. To enable effective work of human analysts, relevant information must be presented to them in an adequate way. Since visual representation of information greatly promotes man’s perception and cognition, visual displays of data and results of computational processing play a very important role in analysis.


Association Rule Movement Data Time Moment Data Aggregation Movement Characteristic 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S.R. Alpert. Comprehensive mapping of knowledge and information resources: The case of webster. In Knowledge and Information Visualization, pp. 220–237, 2005.Google Scholar
  2. 2.
    N. Andrienko and G. Andrienko. Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer, 2006.Google Scholar
  3. 3.
    N. Andrienko, G. Andrienko, and P. Gatalsky. Impact of data and task characteristics on design of spatiotemporal data visualization tools. In Exploring Geovisualization, pp. 201–222. Elsevier, 2005.Google Scholar
  4. 4.
    N.V. Andrienko, G.L. Andrienko, and P. Gatalsky. Supporting visual exploration of object movement. In Advanced Visual Interfaces, pp. 217–220, 2000.Google Scholar
  5. 5.
    M.Q.W. Baldonado, A. Woodruff, and A. Kuchinsky. Guidelines for using multiple views in information visualization. In Advanced Visual Interfaces, pp. 110–119, 2000.Google Scholar
  6. 6.
    J. Bertin. Semiology of Graphics. Diagrams, Networks, Maps. University of Wisconsin Press, 1983.Google Scholar
  7. 7.
    A. Buja, J.A. McDonald, J. Michalak, and W. Stuetzle. Interactive data visualization using focusing and linking. In Proceedings of the 2nd Conference on Visualization (VIS’91), pp. 156–163. IEEE Computer Society Press, 1991.Google Scholar
  8. 8.
    R.N. Buliung and P.S. Kanaroglou. An exploratory spatial data analysis (esda) toolkit for the analysis of activity/travel data. In Proceedings of Computational Science and Its Applications (ICCSA’04), Vol. 3044. Lecture Notes in Computer Science, pp. 1016–1025. Springer, 2004.Google Scholar
  9. 9.
    P. Buono. Analysing association rules with an interactive graph-based technique. In Proceedings of the International Conference on Human Computer Interaction (HCI’03), pp. 675–679, 2003.Google Scholar
  10. 10.
    A.J. Cañas, R. Carff, G. Hill, M.M. Carvalho, M. Arguedas, T.C. Eskridge, J. Lott, and R. Carvajal. Concept maps: Integrating knowledge and information visualization. In Knowledge and Information Visualization, pp. 205–219, 2005.Google Scholar
  11. 11.
    D. Carr. Looking at large data sets using binned data plots. In Computing and Graphics in Statistics, pp. 7–39. Springer, 1991.Google Scholar
  12. 12.
    I. Drecki and P. Forer. Tourism in New Zealand – International Visitors on the Move (A1 Cartographic Plate). Tourism, Recreation Research and Education Centre (TRREC), Lincoln University, 2000.Google Scholar
  13. 13.
    J.A. Dykes and D.M. Mountain. Seeking structure in records of spatiotemporal behaviour: Visualization issues, efforts and applications. Computational Statistics and Data Analysis, 43(4):581–603, 2003.zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    S. Eick. Engineering perceptually affective visualizations for abstract data. In Scientific Visualization Overviews, Methodologies and Techniques, pp. 191–210. IEEE Computer Science Press, 1997.Google Scholar
  15. 15.
    C. Elzakker. The Use of Maps in the Exploration of Geographic Data. Doctorìs Dissertation, University of Utrecht (Netherlands Geographical Studies 326), 2004.Google Scholar
  16. 16.
    U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery in databases. AI Magazine, 17(3):37–54, 1996.Google Scholar
  17. 17.
    P. Forer and O. Huisman. Space, time and sequencing: Substitution at the physical/virtual interface. In Information, Place and Cyberspace: Issues in Accessibility, pp. 73–90. Springer, 2000.Google Scholar
  18. 18.
    M. Ganesh, E. Han, V. Kumar, S. Shekhar, and J. Srivastava. Visual Data Mining: Framework and Algorithm Development. Technical report.Google Scholar
  19. 19.
    F. Giannotti, M. Nanni, and D. Pedreschi. Efficient mining of temporally annotated sequences. In Proceedings of the Sixth International Conference on Data Mining (SDM’06), pp. 346–357.Google Scholar
  20. 20.
    D. Guo, J. Chen, A.M. MacEachren, and K. Liao. A visualization system for space-time and multivariate patterns (vis-stamp). IEEE Transactions Visualization and Computing Graphics, 12(6):1461–1474, 2006.CrossRefGoogle Scholar
  21. 21.
    D. Guo and M. Gahegan. Spatial ordering and encoding for geographic data mining and visualization. Journal of Intelligent Information Systems, 27(3):243–266, 2006.CrossRefGoogle Scholar
  22. 22.
    M. Hao, M. Hsu, U. Dayal, S. Wei, T. Sprenger, and T. Holenstein. Market Basket Analysis Visualization on a Spherical Surface. Technical Report., 2001.
  23. 23.
    B. Hetzler, W. Harris, S. Havre, and P. Whitney. Visualizing the full spectrum of document relationships, 1998.Google Scholar
  24. 24.
    S. Imfeld. Time, Points and Space: Analysis of Wildlife Data in GIS. Dissertation, University of Zurich, Department of Geography, Zurich, 2000.Google Scholar
  25. 25.
    T. Kapler and W. Wright. Geotime information visualization. Information Visualization, 4(2):136–146, 2005.CrossRefGoogle Scholar
  26. 26.
    D.A. Keim. Information visualization and visual data mining. IEEE Transactions Visualization and Computer Graphics, 8(1):1–8, 2002.CrossRefGoogle Scholar
  27. 27.
    I. Kopanakis. Visualization of Data Mining Outcomes., 2006.
  28. 28.
    I. Kopanakis and B. Theodoulidis. Visual data mining modeling techniques for the visualization of mining outcomes. Journal of Visual Languages and Computing, 14(6):543–589, 2003.CrossRefGoogle Scholar
  29. 29.
    M.-J. Kraak. The space-time cube revisited from a geovisualization perspective. In Proceedings of the 21st International Cartographic Conference (ICC’03), pp. 1988–1995, 2003.Google Scholar
  30. 30.
    Y. Kurata and M.J. Egenhofer. Structure and semantics of arrow diagrams. In Proceedings of Conference On Spatial Information Theory (COSIT’05), pp. 232–250, 2005.Google Scholar
  31. 31.
    M.-P. Kwan and J. Lee. Geovisualization of human activity patterns using 3D GIS: A time-geographic approach. In Spatially Integrated Social Science. Oxford University Press, 2004.Google Scholar
  32. 32.
    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
  33. 33.
    H. Miller. Modeling accessibility using space-time prism concepts within geographical information systems: Fourteen years. In Classics of International Journal of Geographical Information Science, pp. 177–182. CRC Press, 2006.Google Scholar
  34. 34.
    D. Mountain. Exploring Mobile Trajectories: An Investigation of Individual Spatial Behavior and Geographic Filters for Information Retrieval. Dissertation, City University, London, 2005.Google Scholar
  35. 35.
    D. Mountain. Visualizing, querying and summarizing individual spatio-temporal behaviour. In Exploring Geovisualization, pp. 181–200. Elsevier, 2005.Google Scholar
  36. 36.
    D. Mountain and J. Dykes. What I did on my vacation: Spatio-temporal log analysis with interactive graphics and morphometric surface derivatives. In Proceedings of The GIS Research UK (GISRUK’02), 2002.Google Scholar
  37. 37.
    D. Mountain and J. Raper. Modelling human spatio-temporal behaviour: A challenge for location-based services. In Proceedings of 6th International Conference on Geocomputation, 2001.Google Scholar
  38. 38.
    C. Newton. Graphics: from alpha to omega in data analysis. In Graphical Representation of Multivariate Data, pp. 59–92. Academic Press, 1978.Google Scholar
  39. 39.
    C. North and B. Schneiderman. A Taxonomy of Multiple Window Coordinations. Technical Report CS-TR-3854, 1997.Google Scholar
  40. 40.
    J.C. Roberts. On encouraging multiple views for visualisation. In Information Visualization. IEEE Computer Society, 1998.Google Scholar
  41. 41.
    B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. In IEEE Visual Languages, Number UMCP-CSD CS-TR-3665, pp. 336–343, 1996.Google Scholar
  42. 42.
    T. Slocum, R. MacMaster, F. Kessler, and H. Howard. Thematic Cartography and Geographic Visualization. Prentice Hall, 2005.Google Scholar
  43. 43.
    R. Spence and L. Tweedie. The attribute explorer: Information synthesis via exploration. Interacting with Computers, 11(2):137–146, 1998.CrossRefGoogle Scholar
  44. 44.
    S.-O. Tergan and T. Keller. Knowledge and Information Visualization: Searching for Synergies. Springer, 2005.Google Scholar
  45. 45.
    J. Thomas and K. Cook. Illuminating the Path. The Research and Development Agenda for Visual Analytics. IEEE Computer Society, 1983.Google Scholar
  46. 46.
    W. Tobler. Experiments in migration mapping by computer. The American Cartographer, 14(2):155–163, 1987.CrossRefGoogle Scholar
  47. 47.
    W. Tobler. Display and Analysis of Migration Tables., 2005.
  48. 48.
    B. Tversky, J.B. Morrison, and M. Bétrancourt. Animation: Can it facilitate? International Journal Human-Computer Studies, 57(4):247–262, 2002.CrossRefGoogle Scholar
  49. 49.
    L. Wilkinson. The grammar of graphics. Springer-Verlag, 1999.Google Scholar
  50. 50.
    H. Yu. Spatial-temporal gis design for exploring interactions of human activities. Cartography and Geographic Information Science, 33(1):3–19, 2006.CrossRefGoogle Scholar
  51. 51.
    K. Zhao and B. Liu. Visual analysis of the behavior of discovered rules. In Proceeding of Workshop on Visual Data Mining (VDM’01), pp. 59–64, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • G. Andrienko
    • 1
  • N. Andrienko
    • 1
  • I. Kopanakis
    • 2
  • A. Ligtenberg
    • 3
  • S. Wrobel
    • 1
  1. 1.Fraunhofer Institut Intelligente Analyse- und InformationssystemeSankt AugustinGermany
  2. 2.Technological Educational Institute of CreteGreece
  3. 3.Centre for GeoInformationWageningen URNetherlands

Personalised recommendations