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Issue No.05 - September/October (2010 vol.30)
pp: 7-11
Caroline Ziemkiewicz , Brown University
Visualization is at a point in its development where its practitioners frequently find themselves grappling with big questions about its nature and purpose. These include fundamental questions about how visualization works—that is, how do people interpret visual forms as information? Classical visualization theory sees this as a process of encoding data variables as visual variables, which the viewer then decodes. Although this body of theory is useful, it doesn't account for visual structure's role in shaping information. Experiments on how design affects users' interpretations of simple visualizations suggest that structural elements such as borders, fills, and arrangement (in addition to the traditional marks) carry significant, predictable semantic information. Drawing on these findings as well as design traditions, the authors argue that visual structure's apparent dynamics play a major role in a user's understanding of data and must be considered in the design and evaluation of visualizations.
information visualization, visual design, visualization theory, visual structure, computer graphics, graphics and multimedia
Caroline Ziemkiewicz, "Beyond Bertin: Seeing the Forest despite the Trees", IEEE Computer Graphics and Applications, vol.30, no. 5, pp. 7-11, September/October 2010, doi:10.1109/MCG.2010.83
1. J. Bertin, Semiology of Graphics, Univ. of Wisconsin Press, 1967.
2. J. Mackinlay, "Automating the Design of Graphical Presentations of Relational Information," ACM Trans. Graphics, vol. 5, no. 2, 1986, pp. 110–141.
3. L. Wilkinson, The Grammar of Graphics, Springer, 2004.
4. C. Ziemkiewicz and R. Kosara, "The Shaping of Information by Visual Metaphors," IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, 2008, pp. 1269–1276.
5. C. Ziemkiewicz and R. Kosara, "Implied Dynamics in Information Visualization," Proc. 10th Int'l Working Conf. Advanced Visual Interfaces (AVI 10), ACM Press, 2010, pp. 215–222.
6. C. Ziemkiewicz and R. Kosara, "Preconceptions and Individual Differences in Understanding Visual Metaphors," Computer Graphics Forum, vol. 28, no. 3, 2009, pp. 911–918.
7. B. Schmitt and A. Simonson, Marketing Aesthetics: The Strategic Management of Brands, Identity, and Image, Simon and Schuster, 1997.
8. J. Albers, Interaction of Color, Yale Univ. Press, 1963.
9. J. Itten, The Elements of Color, Von Nostrand Reinhold, 1970.
10. R. Arnheim, The Power of the Center: A Study of Composition in the Visual Arts, Univ. of California Press, 1982.
11. L. Elting et al., "Influence of Data Display Formats on Physician Investigators' Decisions to Stop Clinical Trials: Prospective Trial with Repeated Measures," British Medical J.,5 June 1999, pp. 1527–1531.
12. J. Zacks and B. Tversky, "Bars and Lines: A Study of Graphic Communication," Memory & Cognition, vol. 27, no. 6, 1999, pp. 1073–1079.
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