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Issue No.12 - Dec. (2012 vol.18)
pp: 2114-2121
Multivariate visualization techniques have attracted great interest as the dimensionality of data sets grows. One premise of such techniques is that simultaneous visual representation of multiple variables will enable the data analyst to detect patterns amongst multiple variables. Such insights could lead to development of new techniques for rigorous (numerical) analysis of complex relationships hidden within the data. Two natural questions arise from this premise: Which multivariate visualization techniques are the most effective for high-dimensional data sets? How does the analysis task change this utility ranking? We present a user study with a new task to answer the first question. We provide some insights to the second question based on the results of our study and results available in the literature. Our task led to significant differences in error, response time, and subjective workload ratings amongst four visualization techniques. We implemented three integrated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), as well as a baseline case of separate grayscale images. The baseline case fared poorly on all three measures, whereas Datadriven Spots yielded the best accuracy and was among the best in response time. These results differ from comparisons of similar techniques with other tasks, and we review all the techniques, tasks, and results (from our work and previous work) to understand the reasons for this discrepancy.
data visualisation, data analysis, rigorous analysis, multivariate visualization evaluation, multivariate task, data sets dimensionality, visual representation, multiple variables, data analyst, numerical analysis, high-dimensional data sets, utility ranking, response time, subjective workload ratings, integrated techniques, data-driven spots, oriented slivers, attribute blocks, baseline case, grayscale images, Data visualization, Time factors, Image color analysis, Analysis of variance, Shape analysis, Gray-scale, Quantitative evaluation, texture perception, Quantitative evaluation, multivariate visualization, visual task design
M. A. Livingston, J. W. Decker, Zhuming Ai, "Evaluation of Multivariate Visualization on a Multivariate Task", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2114-2121, Dec. 2012, doi:10.1109/TVCG.2012.223
[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] H. Hagen, Visualization of Large Data Sets, chapter 12, pages 187-198. Academic Press, 1994.
[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. El-sevier 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, L. Tateosian, J. T. Enns,, and M. Remple., Perceptually based brush strokes for nonphotorealistic visualization ACM Transactions on Graphics, 23(1) 64-96 2004.
[9] A. Joshi, Art-inspired techniques for visualizing time-varying data. PhD thesis, The University of Maryland, Baltimore County, 2007.
[10] 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.
[11] 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.
[12] J. LeBlanc, M. O. Ward, and N. Wittels, Exploring N-dimensional databases. In Proc. of IEEE Visualization, pages 230-237, Oct. 1990.
[13] 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.
[14] M. A. Livingston and J. W. Decker, Evaluation of trend localization with multi-variate visualizations IEEE Transactions on Visualization and Computer Graphics, 17(12) 2053-2062, Dec. 2011.
[15] M. A. Livingston and J. W. Decker, Evaluation of multi-variate visualizations: A case study of refinements and user experience. In SPIE Visualization and Data Analysis, Jan. 2012.
[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 Analvsis, 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] G. Perlman, Data analysis programs for the UNIX operating system Behavior Research Methods and Instrumentation, 12(5) pp. 554-558, 1980.
[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] 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 & Aplications, 26(4) 74-82, 2006.
[21] C. Ware, Information Visualization: Perception for Design. Morgan Kaufmann, 2ndedition, 2004.
[22] 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.
[23] E. Williams, Experimental designs balanced for the estimation of residual effects of treatments. Australian Journal of Scientific Research, Series A, 2 149-168, 1949.
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