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Issue No.12 - Dec. (2011 vol.17)
pp: 2053-2062
Mark Livingston , Naval Research Laboratory
Jonathan Decker , Naval Research Laboratory
Multi-valued data sets are increasingly common, with the number of dimensions growing. A number of multi-variate visualization techniques have been presented to display such data. However, evaluating the utility of such techniques for general data sets remains difficult. Thus most techniques are studied on only one data set. Another criticism that could be levied against previous evaluations of multi-variate visualizations is that the task doesn't require the presence of multiple variables. At the same time, the taxonomy of tasks that users may perform visually is extensive. We designed a task, trend localization, that required comparison of multiple data values in a multi-variate visualization. We then conducted a user study with this task, evaluating five multivariate visualization techniques from the literature (Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, Dimensional Stacking) and juxtaposed grayscale maps. We report the results and discuss the implications for both the techniques and the task.
User study, multi-variate visualization, visual task design, visual analytics.
Mark Livingston, Jonathan Decker, "Evaluation of Trend Localization with Multi-Variate Visualizations", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 2053-2062, Dec. 2011, doi:10.1109/TVCG.2011.194
[1] J. Beddow, Shape coding of multidimensional data on a microcomputer display. In Proceedings of IEEE Visualization, pages 238–246, Oct. 1990.
[2] A. A. Bokinsky, Multivariate Data Visualization with Data-driven Spots. PhD thesis, The University of North Carolina at Chapel Hill, 2003.
[3] D. B. Carr and L. W. Pickle, Visualizing Data Patterns with Micromaps. CRC Press, 2010.
[4] J. W. Decker and M. A. Livingston, Poster: An interactive, visual composite tuner for multi-layer spatial data sets. In IEEE Visualization, 2010.
[5] H. Hagh-Shenas, V. Interrante, C. Healey, and S. Kim, Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. In Proceedings of the 3rd Symposium on Applied Perception in Graphics and Visualization, page 164, 2006.
[6] S. G. Hart and L. E. Staveland, Development of NASA-TLX (task load index): Results of empirical and theoretical research. In P. A. Hancock, and N. Meshkati editors, Human Mental Workload, pages 239–250. Elsevier Science Publishers, 1988.
[7] C. G. Healey, and J. T. Enns, Building perceptual textures to visualize multidimensional datasets. In IEEE Visualization, pages 111–118, 1998.
[8] C. G. Healey, S. Kocherlakota, V. Rao, R. Mehta, and R. S. Amant, Visual perception and mixed-initiative interaction for assisted visualization design. IEEE Transactions on Visualization and Computer Graphics, 14 (2): 396–411, March–April 2008.
[9] C. G. Healey, L. Tateosian, J. T. Enns, and M. Remple, Perceptually based brush strokes for nonphotorealistic visualization. ACM Transactions on Graphics, 23 (1): 64–96, 2004.
[10] A. Joshi, Art-inspired techniques for visualizing time-varying data. PhD thesis, The University of Maryland, Baltimore County, 2007.
[11] R. M. Kirby, H. Marmanis, and D. H. Laidlaw, Visualizing multivalued data from 2d incompressible flows using concepts from painting. In Proceedings of IEEE Visualization '99, pages 333–340, 1999.
[12] D. H. Laidlaw, E. T. Ahrens, D. Kremers, M. J. Avalos, R. E. Jacobs, and C. Readhead, Visualizing diffusion tensor images of the mouse spinal cord. In Proceedings of IEEE Visualization '98, pages 127–134, 1998.
[13] D. H. Laidlaw, R. M. Kirby, C. D. Jackson, J. S. Davidson, T. S. Miller, M. da Silva, W. H. Warren, and M. J. Tarr, Comparing 2D vector field visualization methods: A user study. IEEE Transactions on Visualization and Computer Graphics, 11 (1): 59–70, January/February 2005.
[14] J. LeBlanc, M. O. Ward, and N. Wittels, Exploring N-dimensional databases. In Proc. of IEEE Visualization, pages 230–237, Oct. 1990.
[15] H. Levkowitz, Color icons: Merging color and texture perception for integrated visualization of multiple parameters. In Proceedings of IEEE Visualization, pages 164–170, 420, Oct. 1991.
[16] M. A. Livingston, J. W. Decker, and Z. Ai, An evaluation of methods for encoding multiple, 2D spatial data. In SPIE Visualization and Data Analysis, Jan. 2011.
[17] J. R. Miller, Attribute blocks: Visualizing multiple continuously defined attributes. IEEE Computer Graphics & Applications, 27 (3): 57–69, May/June 2007.
[18] R. M. Pickett and G. G. Grinstein, Iconographic displays for visualizing multidimensional data. In Proc. of the 1988 IEEE Intl. Conf. on Systems, Man, and Cybernetics, pages 514–519, Aug. 1988.
[19] Y. Tang, H. Qu, Y. Wu, and H. Zhou, Natural textures for weather data visualization. In Tenth International Conference on Information Visualization, pages 741–750, July 2006.
[20] J. J. Thomas and K. A. Cook, Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, 2005.
[21] T. Urness, V. Interrante, E. Longmire, I. Marusic, S. O'Neill, and T. W. Jones, Strategies for the visualization of multiple 2D vector fields. IEEE Computer Graphics & Applications, 26 (4): 74–82, 2006.
[22] T. Urness, V. Interrante, I. Marusic, E. Longmire, and B. Ganapathisubramani, Effectively visualizing multi-valued flow data using color and texture. In IEEE Visualization, pages 115–121, Oct. 2003.
[23] C. Ware, Information Visualization: Perception for Design. Morgan Kaufmann, 2000.
[24] C. Weigle, W. Emigh, G. Liu, R. M. Taylor II, J. T. Enns, and C. G. Healey, Effectively visualizing multi-valued flow data using color and texture. In Graphics Interface, pages 153–162, 2000.
[25] M. X. Zhou and S. K. Feiner, Visual task characterization for automated visual discourse synthesis. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 392–399, Apr. 1998.
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