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Issue No.06 - November/December (2010 vol.16)
pp: 980-989
David Feng , UNC Chapel Hill
Lester Kwock , UNC Chapel Hill
Yueh Lee , UNC Chapel Hill
Russell Taylor , UNC Chapel Hill
Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets.
Uncertainty visualization, brushing, scatter plots, parallel coordinates, multivariate data
David Feng, Lester Kwock, Yueh Lee, Russell Taylor, "Matching Visual Saliency to Confidence in Plots of Uncertain Data", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 980-989, November/December 2010, doi:10.1109/TVCG.2010.176
[1] C. Ahlberg, Spotfire: an information exploration environment. SIGMOD Rec., 25 (4): 25–29, 1996.
[2] H. Akiba and K.-L. Ma, A tri-space visualization interface for analyzing time-varying multivariate volume data. In EuroVis07 - Eurographics / IEEE VGTC Symposium on Visualization, pages 115–122, May 2007.
[3] S. Bachthaler and D. Weiskopf, Continuous scatterplots. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1428–1435, 2008.
[4] G. E. P. Box and M. E. Muller, A note on the generation of random normal deviates. The Annals of Mathematical Statistics, 29 (2): 610–611, 1958.
[5] M. Castillo, Neuroradiology. Lippincott Williams & Jenkins, 2002.
[6] W. C. Cleveland and M. E. McGill, Dynamic Graphics for Statistics. CRC Press, Inc., Boca Raton, FL, USA, 1988.
[7] N. Elmqvist, P. Dragicevic, and J.-D. Fekete, Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1141–1148, 2008.
[8] D. Feng, L. Kwock, Y. Lee, and R. M. Taylor, II. Linked exploratory visualizations for uncertain MR spectroscopy data. volume 7530, pages 753004–1–753004–12. SPIE, 2010.
[9] D. Feng, Y. Lee, L. Kwock, and R. M. Taylor, II. Evaluation of glyph-based multivariate scalar volume visualization techniques. In APGV '09: Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization, pages 61–68, New York, NY, USA, 2009. ACM.
[10] P. Fisher, Visualizing uncertainty in soil maps by animation. Cartographica: The International Journal for Geographic Information and Geovisualization, 30: 20–27, 1993.
[11] Y.-H. Fua, M. O. Ward, and E. A. Rundensteiner, Hierarchical parallel coordinates for exploration of large datasets. In VIS '99: Proceedings of the conference on Visualization '99, pages 43–50, Los Alamitos, CA, USA, 1999. IEEE Computer Society Press.
[12] M. Graham and J. Kennedy, Using curves to enhance parallel coordinate visualisations. Proceedings of the 7th International Conference on Information Visualization 2003, pages 10–16, July 2003.
[13] J. Heinrich and D. Weiskopf, Continuous parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1531–1538, 2009.
[14] A. Inselberg, The plane with parallel coordinates. The Visual Computer, 1 (4): 69–91, 1985.
[15] S. Johansson and J. Johansson, Scattering points in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1001–1008, October 2009.
[16] R. Kosara, S. Miksch, and H. Hauser, Semantic depth of field. In INFO-VIS '01: Proceedings of the IEEE Symposium on Information Visualization 2001 INFOVIS'01), page 97, Washington, DC, USA, 2001. IEEE Computer Society.
[17] L. Linsen, T. Van Long, P. Rosenthal, and S. Rosswog, Surface extraction from multi-field particle volume data using multi-dimensional cluster visualization. Visualization and Computer Graphics, IEEE Transactions on, 14 (6): 1483–1490, Nov.-Dec. 2008.
[18] J. J. Miller and E. J. Wegman, Construction of line densities for parallel coordinate plots, pages 107–123. Springer-Verlag New York, Inc., New York, NY, USA, 1991.
[19] R. Moustafa and E. Wegman, Multivariate Continuous Data - Parallel Coordinates, pages 143–155. Statistics and Computing. Springer New York, 2006.
[20] M. Novotny and H. Hauser, Outlier-preserving focus+context visualization in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 893–900, 2006.
[21] C. Olston and J. Mackinlay, Visualizing data with bounded uncertainty. In Proceedings of the IEEE Symposium on Information Visualization, pages 37–40, Boston, Massachusetts, Oct. 2002.
[22] S. Palmer, Vision Science: Photons to Phenomenology. MIT Press, 1999.
[23] A. T. Pang, C. M. Wittenbrink, and S. K. Lodh, Approaches to uncertainty visualization. The Visual Computer, 13: 370–390, 1996.
[24] E. Parzen, On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33 (3): 1065–1076, 1962.
[25] K. Potter, Methods for presenting statistical information: The box plot. Hans Hagen, Andreas Kerren, and Peter Dannenmann (Eds.), , Visualization of Large and Unstructured Data Sets, GI-Edition Lecture Notes in Informatics (LNI), S-4: 97–106, 2006.
[26] K. Potter, J. Krueger, and C. Johnson, Towards the visualization of multi-dimentional stochastic distribution data. In Proceedings of The International Conference on Computer Graphics and Visualization (IADIS) 2008, 2008.
[27] S. Provencher, Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med, 30: 672–679, Dec 1993.
[28] C. P. Robert and G. Casella, Monte Carlo Statistical Methods. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005.
[29] J. Sanyal, S. Zhang, G. Bhattacharya, P. Amburn, and R. Moorhead, A user study to compare four uncertainty visualization methods for 1d and 2d datasets. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1209–1218, 2009.
[30] J. Seo and B. Shneiderman, A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections. In INFOVIS '04: Proceedings of the IEEE Symposium on Information Visualization, pages 65–72, Washington, DC, USA, 2004. IEEE Computer Society.
[31] J. Shearer, M. Ogawa, K.-L. Ma, and T. Kohlenberg, Pixelplexing: Gaining display resolution through time. In IEEE Pacific Visualization Symposium 2008., pages 159–166, 5–7 2008.
[32] D. P. Soares and M. Law, Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol, 64: 12–21, Jan 2009.
[33] D. F. Swayne, D. Temple Lang, A. Buja, and D. Cook, GGobi: evolving from XGobi into an extensible framework for interactive data visualization. Computational Statistics & Data Analysis, 43: 423–444, 2003.
[34] J. Thomson, E. Hetzler, A. MacEachren, M. Gahegan, and M. Pavel, A typology for visualizing uncertainty. volume 5669, pages 146–157. SPIE, 2005.
[35] R. van Liere and W. de Leeuw, Graphsplatting: Visualizing graphs as continuous fields. IEEE Transactions on Visualization and Computer Graphics, 9 (2): 206–212, 2003.
[36] Y. Wan, H. Otsuna, C.-B. Chien, and C. Hansen, An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1489–1496, 2009.
[37] M. O. Ward, Xmdvtool: integrating multiple methods for visualizing multivariate data. In VIS '94: Proceedings of the conference on Visualization '94, pages 326–333, Los Alamitos, CA, USA, 1994. IEEE Computer Society Press.
[38] C. Ware, Information visualization: perception for design. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2000.
[39] C. M. Wittenbrink, A. Pang, and S. K. Lodha, Glyphs for visualizing uncertainty in vector fields. IEEE Trans. Vis. Comput. Graph., 2 (3): 266–279, 1996.
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