Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods
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We present an evaluation of a parameterized set of 2D icon-based visualization methods where we quantified how perceptual interactions among visual elements affect efficient data exploration. During the experiment, subjects quantified three different design factors for each method: the spatial resolution it could represent, the number of data values it could display at each point, and the degree to which it is visually linear. The class of visualization methods includes Poisson-disk distributed icons where icon size, icon spacing, and icon brightness can be set to a constant or coupled to data values from a 2D scalar field. By only coupling one of those visual components to data, we measured filtering interference for all three design factors. Filtering interference characterizes how different levels of the constant visual elements affect the evaluation of the data-coupled element. Our novel experimental methodology allowed us to generalize this perceptual information, gathered using ad-hoc artificial datasets, onto quantitative rules for visualizing real scientific datasets. This work also provides a framework for evaluating visualizations of multi-valued data that incorporate additional visual cues, such as icon orientation or color.
[1] 1133 D. Acevedo, C. Jackson, D. H. Laidlaw, and F. Drury, Using visual design expertise to characterize the effectiveness of 2d scientific visualization methods. IEEE Visualization'05, Poster Compendium, October 2005.
[2] J. R. Bergen, Theories of visual texture perception. In Spatial Vision. CRC, Boca Raton, FL, 1991.
[3] J. Bertin, Semiology of Graphics. University of Wisconsin Press, 1983.
[4] A. A. Bokinsky, Multivariate Data Visualization with Data-Driven Spots. PhD thesis, University of North Carolina at Chapel Hill, 2003.
[5] T. C. Callaghan, Dimensional interaction of hue and brightness in preattentive field segregation. Perception and Psychopysics, 36 (1): 25–34, 1984.
[6] T. C. Callaghan, Interference and dominance in texture segregation: Hue, geometric form, and line orientation. Perception and Psychopysics, 46 (4): 299–311, 1989.
[7] S. K. Card and J. Mackinlay, The structure of the information visualization design space. In Proceedings of IEEE Symposium on Information Visualization, pages 92–99, 1997.
[8] C. M. Carswell and C. Wickens, The perceptual interaction of graphical attributes: Configurality, stimulus homogeneity, and object integration. Perception and Psychopysics, 47: 157–168, 1990.
[9] S. M. Casner, A task-analytic approach to the automated design of graphic presentations. ACM Transactions on Graphics, 10 (2): 111–151, April 1991.
[10] E. H. Chi, A taxonomy of visualization techniques using the data state reference model. In Proceedings of InfoVis 2000, pages 69–76, 2000.
[11] W. S. Cleveland and R. McGill, Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79 (387): 531–554, September 1984.
[12] S. G. Eick, Engineering perceptually effective visualizations for abstract data. In Scientific Visualization Overviews, Methodologies and Techniques, pages 191–210. IEEE Computer Science Press, 1995.
[13] W. D. Ellis, A source book of gestalt psychology. Harcourt, Brace, and company, New York, NY, 1939.
[14] P. Hanrahan, Teaching visualization. Computer Graphics, 39 (1): 4–5, February 2005.
[15] C. G. Healey, R. S. Amant, and M. S. Elhaddad, Via: A perceptual visualization assistant. In 28th Workshop on Advanced Imagery Pattern Recognition, 1999.
[16] C. G. Healey, K. S. Booth, and J. T. Enns, Harnessing preattentive processes for multivariate data visualization. In Proceedings of Graphics Interface, pages 107–117, Toronto, Canada, 1993.
[17] C. G. Healey, K. S. Booth, and J. T. Enns, High-speed visual estimation using preattentive processing. ACM Transactions on Human-Computer Interaction, 3 (2): 107–135, June 1996.
[18] B. Hibbard, The top five problems that motivated my work. IEEE Computer Graphics and Applications, 24 (6): 9–13, November/December 2004.
[19] C. Jackson, D. Acevedo, D. H. Laidlaw, F. Drury, E. Vote, and D. Keefe, Designer-critiqued comparison of 2D vector visualization methods: A pilot study. In SIGGRAPH 2003 Sketches and Applications. ACM SIG-GRAPH, 2003.
[20] C. Johnson, Top scientific visualization research problems. IEEE Computer Graphics and Applications, 24 (4): 13–17, July/August 2004.
[21] D. Keefe, D. Karelitz, E. Vote, and D. H. Laidlaw, Artistic collaboration in designing VR visualizations. IEEE Computer Graphics and Applications, 25 (2): 18–23, March/April 2005.
[22] R. Kosara, C. G. Healey, V. Interrante, D. H. Laidlaw, and C. Ware, User studies: Why, how, and when. Computer Graphics and Applications, 23 (4): 20–25, July/August 2003.
[23] D. H. Laidlaw, Loose, artistic "textures" for visualization. IEEE Computer Graphics and Applications, 21 (2): 6–9, March/April 2001.
[24] D. H. Laidlaw, M. Kirby, C. Jackson, J. S. Davidson, T. Miller, M. DaSilva, W. Warren, and M. Tarr, Comparing 2D vector field visualization methods: A user study. Transactions on Visualization and Computer Graphics, 11 (1): 59–70, January–February 2005.
[25] S. Lange, H. Schumann, W. Muller, and D. Kromker, Problem-oriented visualisation of multi-dimensional data sets. In Proceedings of the International Symposium and Scientific Visualization, pages 1–15, 1995.
[26] A. MacEachren and M.-J. Kraak, Exploratory cartographic visualization: Advancing the agenda. Computers and Geosciences, 23 (4): 335–343, 1997.
[27] J. Mackinlay, Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5 (2): 110–141, April 1986.
[28] G. F. McCleary Jr, An effective graphic vocabulary. IEEE Computer Graphics and Applications, 3 (2): 46–53, March/April 1983.
[29] R. Nagappan, A compositional model for multidimensional data visualisation. In In Proceedings of SPIE, Visual Data Exploration and Analysis VIII, pages 156–167, January 2001.
[30] L. T. Nowell, Graphical Encoding for Information Visualization: Using Icon Color, Shape, and Size to Convey Nominal and Quantitative Data. PhD thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, November 1997.
[31] P. K. Robertson, A methodology for choosing data representations. IEEE Computer Graphics and Applications, 11 (3): 56–67, May/June 1991.
[32] H. Senay and E. Ignatius, A knowledge-based system for visualization design. IEEE Computer Graphics and Applications, 14 (6): 36–47, November 1994.
[33] M. Tory and T. Moller, Human factors in visualization research. IEEE Transactions on Visualization and Computer Graphics, 10 (1): 72–84, January/February 2004.
[34] C. Wallschlaeger and C. Busic-Snyder, Basic Visual Concepts and Principles for Artists, Architects and Designers. McGraw Hill, 1992.
[35] C. Ware, Information Visualization. Perception for Design. Elsevier, 2nd edition, 2004.
[36] J. M. Wolfe, Visual search. In Attention. University College London Press, London, UK, 1998.
Index Terms:
Perception models, 2D visualization methods, visualization evaluation, perceptual interactions, visual design.
Citation:
Daniel Acevedo, David Laidlaw, "Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods," IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 5, pp. 1133-1140, Sept. 2006, doi:10.1109/TVCG.2006.180
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