Visualizing Uncertainty in Node-Link Diagrams - a User Study

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 607)

Abstract

Uncertainty visualization is manifold and applied in many disciplines of visualization. In node-link diagrams, the edges can have uncertain attributes; these are to be presented directly in the graph. On the one hand, the uncertainty values are encoded with visual variable. On the other hand, the user has to decode the visualization to identify the original value. In this paper, we focus on four uncertainty visualization techniques and their suitability to enable the user to decode and identify the correct values. In our evaluation, we investigate the maximum number of different levels that can be used for each technique such that a user is able to reliably distinguish different values.

Keywords

Uncertainty visualization Edge visualization Node-link diagram User study 

Notes

Acknowledgments

This research was partially funded by the German research foundation (DFG) within the IRTG 2057 “Physical Modeling for Virtual Manufacturing Systems and Processes” and the German Federal Ministry for Economic Affairs and Technology in the context of “Smart Data - Innovations in Data”, grant no. 01MD15004E.

References

  1. 1.
    Boukhelifa, N., Bezerianos, A., Isenberg, T., Fekete, J.D.: Evaluating sketchiness as a visual variable for the depiction of qualitative uncertainty. IEEE Trans. Vis. Comput. Graph. 18(12), 2769–2778 (2012)CrossRefGoogle Scholar
  2. 2.
    Schwank, J., Schöffel, S., Starz, J., Ebert, A.: Visualizing uncertainty of edge attributes in node-link diagrams. In: 20th International Conference Information Visualisation (IV) (2016)Google Scholar
  3. 3.
    MacEachren, A.M.: Visualizing uncertain information. Cartographic Perspect. 13(13), 10–19 (1992)CrossRefGoogle Scholar
  4. 4.
    Guo, H., Huang, J., Laidlaw, D.H.: Representing uncertainty in graph edges: an evaluation of paired visual variables. IEEE Trans. Vis. Comput. Graph. 21(10), 1173–1186 (2015)CrossRefGoogle Scholar
  5. 5.
    Boller, R.A., Braun, S.A., Miles, J., Laidlaw, D.H.: Application of uncertainty visualization methods to meteorological trajectories. Earth Sci. Inf. 3(1–2), 119–126 (2010)CrossRefGoogle Scholar
  6. 6.
    Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Trans. Vis. Compu. Graph. 17(12), 2301–2309 (2011)CrossRefGoogle Scholar
  7. 7.
    Brodlie, K., Allendes Osorio, R., Lopes, A.: A review of uncertainty in data visualization. In: Expanding the Frontiers of Visual Analytics and Visualization, pp. 81–109 (2012)Google Scholar
  8. 8.
    Gillmann, C., Wischgoll, T., Hagen, H.: Uncertainty-awareness in open source visualization solutions. In: IEEE Vis 2016 Workshop on Visualization in Practice (2016)Google Scholar
  9. 9.
    Bonneau, G.P., Hege, H.C., Johnson, C.R., Oliveira, M.M., Potter, K., Rheingans, P., Schultz, T.: Overview and state-of-the-art of uncertainty visualization. Math. Vis. 37, 3–27 (2014)Google Scholar
  10. 10.
    Lu, L., Cao, N., Liu, S., Ni, L., Yuan, X., Qu, H.: Visual analysis of uncertainty in trajectories. In: Advances in knowledge discovery and data mining, vol. 8443, pp. 509–520 (2014)Google Scholar
  11. 11.
    Pang, A.T., Wittenbrink, C.M., Lodha, S.K.: Approaches to uncertainty visualization. Vis. Comput. 13(8), 370–390 (1997)CrossRefGoogle Scholar
  12. 12.
    Ibrekk, H., Morgan, M.: Graphical Communication of Uncertain Quantities to Nontechnical People. Risk Anal. 7(4), 519–529 (1987)CrossRefGoogle Scholar
  13. 13.
    Tak, S., Toet, A., van Erp, J.: Public understanding of visual representations of uncertainty in temperature forecasts. J. Cogn. Eng. Decis. Making 9(3), 241–262 (2015)CrossRefGoogle Scholar
  14. 14.
    Wittenbrink, C.M., Pang, A.T., Lodha, S.K.: Glyphs for visualizing uncertainty in vector fields. IEEE Trans. Vis. Comput. Graph. 2(3), 266–279 (1996)CrossRefGoogle Scholar
  15. 15.
    Bertin, J.: Semiology of graphics: Diagrams, networks, maps. University of Wisconsin Press, Madison (1983)Google Scholar
  16. 16.
    Kinkeldey, C., MacEachren, A.M., Schiewe, J.: How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualisation user studies. Cartographic J. 51(4), 372–386 (2014)CrossRefGoogle Scholar
  17. 17.
    Cesario, N., Pang, A., Singh, L.: Visualizing node attribute uncertainty in graphs. In: Visualization and Data Analysis 2011, vol. 7868, 78680H–13 (2011)Google Scholar
  18. 18.
    Green, M.: Toward a perceptual science of multidimensional data visualization: Bertin and beyond. ERGO/GERO Human Factors Science 8 (1998)Google Scholar
  19. 19.
    Whitaker, R.T., Mirzargar, M., Kirby, R.M.: Contour boxplots: a method for characterizing uncertainty in feature sets from simulation ensembles. IEEE Trans. Vis. Comput. Graph. 19(12), 2713–2722 (2013)CrossRefGoogle Scholar
  20. 20.
    Holten, D., Van Wijk, J.J.: A user study on visualizing directed edges in graphs. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems CHI 09, 2299 (2009)Google Scholar
  21. 21.
    Xu, K., Rooney, C., Passmore, P., Ham, D.H., Nguyen, P.H.: A user study on curved edges in graph visualization. IEEE Trans. Vis. Comput. Graph. 18(12), 2449–2456 (2012)CrossRefGoogle Scholar
  22. 22.
    Tikhonova, A., Ma, K.: A scalable parallel force-directed graph layout algorithm. In: Eurographics Symposium on Parrallel Graphics and Visualization, pp. 25–32 (2008)Google Scholar
  23. 23.
    Di Battista, G., Eades, P., Tamassia, R., Tollis, I.G.: Graph drawing: algorithms for the visualization of graphs‎. In: Handbook of Discrete and Computational Geometry, 1st edn. book, Prentice Hall PTR, Upper Saddle River (1999)Google Scholar
  24. 24.
    Holten, D., Van Wijk, J.J.: Force-Directed edge bundling for graph visualization. Comput. Graph. Forum 28(3), 983–990 (2009)CrossRefGoogle Scholar
  25. 25.
    Ghoniem, M., Fekete, J.D., Castagliola, P.: A comparison of the readability of graphs using node-link and matrix-based representations. In: Proceedings - IEEE Symposium on Information Visualization, INFO VIS, pp. 17–24 (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of KaiserslauternKaiserslauternGermany

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