Visual Analysis of Bird Moving Patterns

  • Krešimir MatkovićEmail author
  • Denis Gračanin
  • Michael Beham
  • Rainer Splechtna
  • Miriah Meyer
  • Elena Ginina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)


In spite of recent advances in data analysis techniques, exploration of complex, unstructured spatial-temporal data could still be difficult. An interactive approach, with human in the analysis loop, represents a valuable add on to automatic analysis methods. We describe an interactive visual analysis method to exploration of complex spatio-temporal data sets. The proposed approach is illustrated using a publicly available data set, a collection of bird locations recorded over an extended period of time. In order to explore and comprehend complex patterns in bird movements over time, we provide two new views, the centroids scatter plot view and the distance plot view. Successful analysis of the birds data indicates the usefulness of the newly proposed approach for other spatio-temporal data of a similar structure.


Visual analytics Spatio-temporal data Patterns in movement data 



VRVis is funded by BMVIT, BMDW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (854174) which is managed by FFG.


  1. 1.
    Andrienko, G., Andrienko, N., Bak, P., Keim, D., Wrobel, S.: Visual Analytics of Movement. Springer, Berlin (2013). Scholar
  2. 2.
    Chen, W., Guo, F., Wang, F.Y.: A survey of traffic data visualization. IEEE Trans. Intell. Transp. Syst. 16(6), 2970–2984 (2015)CrossRefGoogle Scholar
  3. 3.
    Cibulski, L., et al.: ITEA-interactive trajectories and events analysis. Vis. Comput. 32(6), 847–857 (2016)CrossRefGoogle Scholar
  4. 4.
    Ferreira, N., et al.: BirdVis: visualizing and understanding bird populations. IEEE Trans. Visual. Comput. Graph. 17(12), 2374–2383 (2011)CrossRefGoogle Scholar
  5. 5.
    Ferreira, N., Poco, J., Vo, H.T., Freire, J., Silva, C.T.: Visual exploration of big spatio-temporal urban data: a study of New York city taxi trips. IEEE Trans. Visual. Comput. Graph. 19(12), 2149–2158 (2013)CrossRefGoogle Scholar
  6. 6.
    Harrower, M., Brewer, C.A.: an online tool for selecting colour schemes for maps. Cartograph. J. 40(1), 27–37 (2003)CrossRefGoogle Scholar
  7. 7.
    IEEE VIS 2018 Conference: VAST Challenge 2018: Mini-challenge 1 (2018).
  8. 8.
    Lin, S., Fortuna, J., Kulkarni, C., Stone, M., Heer, J.: Selecting semantically-resonant colors for data visualization. Comput. Graph. Forum 32(3), 401–410 (2013)CrossRefGoogle Scholar
  9. 9.
    Orellana, D., Bregt, A.K., Ligtenberg, A., Wachowicz, M.: Exploring visitor movement patterns in natural recreational areas. Tour. Manag. 33(3), 672–682 (2012)CrossRefGoogle Scholar
  10. 10.
    Radoš, S., Splechtna, R., Matković, K., Djuras, M., Gröller, E., Hauser, H.: Towards quantitative visual analytics with structured brushing and linked statistics. Comput. Graph. Forum 35(3), 251–260 (2016)CrossRefGoogle Scholar
  11. 11.
    Roberts, J.C.: State of the art: coordinated amp; multiple views in exploratory visualization. In: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007) (2007)Google Scholar
  12. 12.
    Sarikaya, A., Gleicher, M.: Scatterplots: tasks, data, and designs. IEEE Trans. Visual. Comput. Graph. 24(1), 402–412 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.VRVis Research CenterViennaAustria
  2. 2.Virginia TechBlacksburgUSA
  3. 3.University of UtahSalt Lake CityUSA

Personalised recommendations