KI - Künstliche Intelligenz

, Volume 26, Issue 3, pp 241–251 | Cite as

Visual Analytics for Understanding Spatial Situations from Episodic Movement Data

  • Natalia Andrienko
  • Gennady Andrienko
  • Hendrik Stange
  • Thomas Liebig
  • Dirk Hecker
Fachbeitrag

Abstract

Continuing advances in modern data acquisition techniques result in rapidly growing amounts of geo-referenced data about moving objects and in emergence of new data types. We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilising Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data by means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) between the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors.

References

  1. 1.
    Andrienko G, Andrienko N, Bak P, Keim D, Kisilevich S, Wrobel S (2011) A conceptual framework and taxonomy of techniques for analyzing movement. J Vis Lang Comput 22(3):213–232 CrossRefGoogle Scholar
  2. 2.
    Andrienko G, Andrienko N, Bremm S, Schreck T, von Landesberger T, Bak P, Keim D (2010) Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Comput Graph Forum 29(3):913–922 CrossRefGoogle Scholar
  3. 3.
    Andrienko N, Andrienko G (2011) Spatial generalization and aggregation of massive movement data. IEEE Trans Vis Comput Graph 17(2):205–219 CrossRefGoogle Scholar
  4. 4.
    Bak P, Mansmann F, Janetzko H, Keim DA (2009) Spatio-temporal analysis of sensor logs using growth-ring maps. IEEE Trans Vis Comput Graph 15(6):913–920 CrossRefGoogle Scholar
  5. 5.
    Boyandin I, Bertini E, Lalanne D (2010) Visualizing the world’s refugee data with JFlowMap. In: Poster abstracts at Eurographics/IEEE-VGTC symposium on visualisation Google Scholar
  6. 6.
    Bruno R, Delmastro F (2003) Design and analysis of a bluetooth-based indoor localisation system. In: Proc personal wireless communications (PWC), IFIP-TC6 8th international conference, pp 711–725 Google Scholar
  7. 7.
    Guo D, Chen J, MacEachren A, Liao K (2006) A visualisation system for space-time and multivariate patterns (VIS-STAMP). IEEE Trans Vis Comput Graph 12(6):1461–1474 CrossRefGoogle Scholar
  8. 8.
    Jankowski P, Andrienko N, Andrienko G, Kisilevich S (2010) Discovering landmark preferences and movement patterns from photo postings. In: Transaction in GIS, 2010, vol 46, pp 833–852 Google Scholar
  9. 9.
    Keim D, Andrienko G, Fekete J-D, Görg C, Kohlhammer J, Melançon G (2008) Visual analytics: definition, process, and challenges. In: Kerren A, Stasko JT, Fekete J-D, North C (eds) Information visualisation—human-centered issues and perspectives. Lecture notes in computer science, vol 4950. Springer, Berlin, pp 154–175 Google Scholar
  10. 10.
    Kraak M-J, Ormeling F (2003) Cartography: visualisation of spatial data, 2nd edn. Pearson Education, Harlow Google Scholar
  11. 11.
    Phan D, Xiao L, Yeh R, Hanrahan P, Winograd T (2005) Flow map layout. In: Proc IEEE symposium on information visualization InfoVis 05, Minneapolis, Minnesota, USA, 23–25 October, 2005, pp 219–224 CrossRefGoogle Scholar
  12. 12.
    Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18:401–409 CrossRefGoogle Scholar
  13. 13.
    Stange H, Liebig T, Hecker D, Andrienko G, Andrienko N (2011) Analytical workflow of monitoring human mobility in big event settings using bluetooth. In: Third international workshop on indoor spatial awareness (ISA 2011), 1 November, 2011, Chicago, USA Google Scholar
  14. 14.
    Vrotsou K, Andrienko N, Andrienko G, Jankowski P (2011) Exploring city structure from georeferenced photos using graph centrality measures. In: Proc machine learning and knowledge discovery in databases (PKDD 2011). Lecture notes in computer science, vol 6913, pp 654–657 CrossRefGoogle Scholar
  15. 15.
    Wood J, Dykes J, Slingsby A (2010) Visualization of origins, destinations and flows with OD maps. Cartogr J 47(2):117–129 CrossRefGoogle Scholar
  16. 16.
    Wood J, Slingsby A, Dykes J (2011) Visualizing the dynamics of London’s bicycle hire scheme. Cartographica 46(4):239–251 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Natalia Andrienko
    • 1
  • Gennady Andrienko
    • 1
  • Hendrik Stange
    • 1
  • Thomas Liebig
    • 1
  • Dirk Hecker
    • 1
  1. 1.Fraunhofer Institute IAIS (Intelligent Analysis and Information Systems)Sankt AugustinGermany

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