Advertisement

The Visual Computer

, Volume 32, Issue 3, pp 371–381 | Cite as

Animated visualization of spatial–temporal trajectory data for air-traffic analysis

  • Stefan Buschmann
  • Matthias Trapp
  • Jürgen Döllner
Original Article

Abstract

With increasing numbers of flights worldwide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. To cope with these challenges, cyber worlds can be used for interactive visual analysis and analytical reasoning based on aircraft trajectory data. However, with growing data size and complexity, visualization requires high computational efficiency to process that data within real-time constraints. This paper presents a technique for real-time animated visualization of massive trajectory data. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearance, and (3) real-time rendering within 3D virtual environments such as virtual 3D airport or 3D city models. Different visualization metaphors can be efficiently built upon this technique such as temporal focus+context, density maps, or overview+detail methods. As a general-purpose visualization technique, it can be applied to general 3D and 3+1D trajectory data, e.g., traffic movement data, geo-referenced networks, or spatio-temporal data, and it supports related visual analytics and data mining tasks within cyber worlds.

Keywords

Spatio-temporal visualization Trajectory visualization 3D visualization Visual analytics Real-time rendering 

Notes

Acknowledgments

This work was funded by the German Federal Ministry of Education and Research (BMBF) in the InnoProfile Transfer research group “4DnDVis”. We also wish to thank Deutsche Flugsicherung GmbH for providing the used data set.

References

  1. 1.
    Akenine-Möller, T., Haines, E., Hoffman, N.: Real-Time Rendering, 3rd edn. A. K. Peters Ltd, Natick (2008)CrossRefGoogle Scholar
  2. 2.
    Andrienko, G., Andrienko, N.: Interactive cluster analysis of diverse types of spatiotemporal data. ACM SIGKDD Explor. Newsl. 11(2), 19–28 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology pp. 3–10 (2009)Google Scholar
  4. 4.
    Andrienko, G., Andrienko, N., Schumann, H., Tominski, C.: Visualization of trajectory attributes in space-time cube and trajectory wall. In: Cartography from Pole to Pole, pp. 157–163. Springer, New York (2014)Google Scholar
  5. 5.
    Andrienko, G., et al.: Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 24(10), 1577–1600 (2010)CrossRefGoogle Scholar
  6. 6.
    Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer, New York (2005)Google Scholar
  7. 7.
    Andrienko, N., Andrienko, G., Gatalsky, P.: Visual data exploration using space-time cube. In: 21st International Cartographic Conference, pp. 1981–1983 (2003)Google Scholar
  8. 8.
    Bavoil, L., Sainz, M.: Screen space ambient occlusion. NVIDIA 6 (2008)Google Scholar
  9. 9.
    Carvalho, A., de Sousa, A.A., Ribeiro, C., Costa, E.: A temporal focus + context visualization model for handling valid-time spatial information. Inf. Vis. 7(3), 265–274 (2008)CrossRefGoogle Scholar
  10. 10.
    Chang, R., Ebert, D., Keim, D.: Introduction to the special issue on interactive computational visual analytics. ACM Trans. Interact. Intell. Syst. (TiiS) 4(1), 3 (2014)Google Scholar
  11. 11.
    Elmqvist, N., Tsigas, P.: A taxonomy of 3D occlusion management for visualization. IEEE TVCG 14(5), 1095–1109 (2008)Google Scholar
  12. 12.
    Hägerstrand, T.: What about people in regional science? In: Papers of the Regional Science Association, pp. 7–21 (1970)Google Scholar
  13. 13.
    Hurter, C., Alligier, R., Gianazza, D., Puechmorel, S., Andrienko, G., Andrienko, N.: Wind parameters extraction from aircraft trajectories. Computers, Environment and Urban Systems (2014)Google Scholar
  14. 14.
    Hurter, C., Conversy, S., Gianazza, D., Telea, A.: Interactive image-based information visualization for aircraft trajectory analysis. Transp. Res. Part C Emerg. Technol. (2014)Google Scholar
  15. 15.
    Hurter, C., Ersoy, O., Fabrikant, S., Klein, T., Telea, A.: Bundled visualization of dynamic graph and trail data. IEEE TVCG (2013)Google Scholar
  16. 16.
    Hurter, C., Ersoy, O., Telea, A.: Graph bundling by kernel density estimation. In: Computer Graphics Forum, vol. 31, pp. 865–874. Wiley Online Library (2012)Google Scholar
  17. 17.
    Hurter, C., Tissoires, B., Conversy, S.: Fromdady: spreading aircraft trajectories across views to support iterative queries. IEEE TVCG 15(6), 1017–1024 (2009)Google Scholar
  18. 18.
    Kessenich, J., Baldwin, D., Rost, R.: The OpenGL Shading Language Language Version: 4.40 Document Revision 9. The Khronos Group Inc. (2014)Google Scholar
  19. 19.
    Klein, T., van der Zwan, M., Telea, A.: Dynamic multiscale visualization of flight data. In: VISAPP 2014 (2014)Google Scholar
  20. 20.
    Kraak, M.J.: The space-time cube revisited from a geovisualization perspective. In: Proceedings of 21st International Cartographic Conference, pp. 10–16 (2003)Google Scholar
  21. 21.
    Kraak, M.J., Koussoulakou, A.: A visualization environment for the space-time-cube. In: Developments in Spatial Data Handling: 11th International Symposium on Spatial Data Handling, pp. 189–200. Springer, New York (2005)Google Scholar
  22. 22.
    Kristensson, P.O., et al.: An evaluation of space time cube representation of spatiotemporal patterns. IEEE TVCG 15(4), 696–702 (2009)Google Scholar
  23. 23.
    Krone, M., Bidmon, K., Ertl, T.: Gpu-based visualisation of protein secondary structure. In: TPCG’08, pp. 115–122 (2008)Google Scholar
  24. 24.
    Kveladze, I., Kraak, M.J., van Elzakker, C.P.: A methodological framework for researching the usability of the space-time cube. Cartogr. J. 50(3), 201–210 (2013)CrossRefGoogle Scholar
  25. 25.
    Leung, Y.K., Apperley, M.D.: A review and taxonomy of distortion-oriented presentation techniques. ACM Trans. Comput. Hum. Interact. 1(2), 126–160 (1994)CrossRefGoogle Scholar
  26. 26.
    Li, X., Kraak, M.J.: The time wave. a new method of visual exploration of geo-data in time-space. Cartogr. J. 45(3), 193–200 (2008)CrossRefGoogle Scholar
  27. 27.
    Lottes, T.: Fxaa (2009)Google Scholar
  28. 28.
    Luebke, D.P.: Level of Detail for 3d Graphics. Morgan Kaufmann (2003)Google Scholar
  29. 29.
    Luft, T., Colditz, C., Deussen, O.: Image enhancement by unsharp masking the depth buffer. ACM Trans. Graph. 25(3), 1206–1213 (2006)CrossRefGoogle Scholar
  30. 30.
    MacEachren, A.M.: How maps work: representation, visualization and design. Guilford Press (1995)Google Scholar
  31. 31.
    Nienhaus, M., Döllner, J.: Depicting dynamics using principles of visual art and narrations. IEEE CGA 25(3), 40–51 (2005)Google Scholar
  32. 32.
    Saito, T., Takahashi, T.: Comprehensible rendering of 3-d shapes. ACM SIGGRAPH 24(4), 197–206 (1990)CrossRefGoogle Scholar
  33. 33.
    Scheepens, R., Willems, N., van de Wetering, H., van Wijk, J.J.: Interactive Density Maps for Moving Objects. IEEE CGA 32(1), 56–66 (2012)Google Scholar
  34. 34.
    Sidharth, T., Hanson, A.: A 3D visualization of multiple time series on maps. In: 14th International Conference Information Visualisation, pp. 336–343 (2010)Google Scholar
  35. 35.
    Tominski, C., Schulz, H.J.: The great wall of space-time. In: Workshop on Vision, Modeling & Visualization (VMV), pp. 199–206. Eurographics Association (2012)Google Scholar
  36. 36.
    Tominski, C., Schulze-Wollgast, P., Schumann, H.: 3D information visualization for time dependent data on maps. In: Ninth International Conference on Information Visualisation (IV’05), pp. 175–181Google Scholar
  37. 37.
    Tominski, C., Schumann, H., Andrienko, G., Andrienko, N.: Stacking-based visualization of trajectory attribute data. IEEE TVCG 18(12), 2565–2574 (2012)Google Scholar
  38. 38.
    Trapp, M., Schmechel, S., Döllner, J.: Interactive rendering of complex 3d-treemaps with a comparative performance evaluations. GRAPP IVAPP 2013, 165–175 (2013)Google Scholar
  39. 39.
    Ware, C.: Information visualization, vol. 2. Morgan Kaufmann (2000)Google Scholar
  40. 40.
    Willems, N., van de Wetering, H., van Wijk, J.: Visualization of vessel movements. Computer Graph. Forum 28(3), 959–966 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Stefan Buschmann
    • 1
  • Matthias Trapp
    • 1
  • Jürgen Döllner
    • 1
  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany

Personalised recommendations