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Issue No.06 - November/December (2009 vol.15)
pp: 1523-1530
Colin Ware , Center for Coastal and Ocean Mapping, University of New Hampshire
ABSTRACT
Representing bivariate scalar maps is a common but difficult visualization problem. One solution has been to use two dimensional color schemes, but the results are often hard to interpret and inaccurately read. An alternative is to use a color sequence for one variable and a texture sequence for another. This has been used, for example, in geology, but much less studied than the two dimensional color scheme, although theory suggests that it should lead to easier perceptual separation of information relating to the two variables. To make a texture sequence more clearly readable the concept of the quantitative texton sequence (QTonS) is introduced. A QTonS is defined a sequence of small graphical elements, called textons, where each texton represents a different numerical value and sets of textons can be densely displayed to produce visually differentiable textures. An experiment was carried out to compare two bivariate color coding schemes with two schemes using QTonS for one bivariate map component and a color sequence for the other. Two different key designs were investigated (a key being a sequence of colors or textures used in obtaining quantitative values from a map). The first design used two separate keys, one for each dimension, in order to measure how accurately subjects could independently estimate the underlying scalar variables. The second key design was two dimensional and intended to measure the overall integral accuracy that could be obtained. The results show that the accuracy is substantially higher for the QTonS/color sequence schemes. A hypothesis that texture/color sequence combinations are better for independent judgments of mapped quantities was supported. A second experiment probed the limits of spatial resolution for QTonSs.
INDEX TERMS
Bivariate maps, texture, texton, legibility, quantitative texton sequence, QTonS
CITATION
Colin Ware, "Quantitative Texton Sequences for Legible Bivariate Maps", IEEE Transactions on Visualization & Computer Graphics, vol.15, no. 6, pp. 1523-1530, November/December 2009, doi:10.1109/TVCG.2009.175
REFERENCES
[1] L. D. Bergman, B. E. Rogowitz, and L. A. Treinish, A Rule-based Tool for Assisting Colormap Selections. Proc of IEEE Visualization, 118-125, October 1995.
[2] J. Bertin, Semiology of Graphics. University of Wisconsin Press. 1983.
[3] C.A. Brewer, Guidelines for selecting colors for diverging schemes on maps. Cartographic Journal, 33 (2): 79-86, 1996.
[4] C A. Brewer, Prediction of simultaneous contrast between map colors with Hunt's model of color appearance. Color Research and Application, 21 (3) 221-235, 1996.
[5] A. Cedilnik and P. Rheingans, Procedural Annotation of Uncertainty Information. Proc IEEE Visualization, 2000. Washington, DC. 77-83, 2000.
[6] W. S. Cleveland and R. McGill, A Color-Caused Optical Illusion on a Statistical Graph. The American Statistician, 37: 101-105, 1983.
[7] R. Dunn, A dynamic approach to two variable color mapping. The American Statistician, Vol 43 (3) 245-252, 1983.
[8] W.R. Garner, The Processing of Information and Structure. Erlbaum: Hilldale NJ. 1974.
[9] H. Hagh-Shenas, S. Kim, V. Interrante, C. Healey, Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. IEEE Transactions on Visualization and Computer Graphics. 13 (6) 1270-1277, 2007.
[10] D.B. Haug, A.M. MacEachren, F.P. Boscoe, D. Brown, M. Marra, C. Polsky, and J. Beedasy, Implementing exploratory spatial data analysis methods for multivariate health statistics. Proc. GIS/LIS'97 Oct, 1994.
[11] C. Healey and J. Enns, Large datasets at a glance: Combining textures and colors in scientific visualization. IEEE Transactions on Visualizations and Computer Graphics, 5 (2) 145-167, 1999.
[12] C. Healey and J. Enns, Building perceptual textures to visualize multidimensional datasets. IEEE Transactions on Visualization and Computer Graphics 5 :22, 145-167, 1999.
[13] B. Julesz, Textons, the elements of texture perception, and their interactions. Nature 290, 91-97. 1981.
[14] A. Leonowicz, Two-variable choropleth maps as a useful tool for visualization of geographical relationships. Geographija, 42, 33-37, 2006.
[15] W.T. Maddox, Perceptual and Decisional Separability. University of California Press. 1996.
[16] J.R. Miller, Attribute Blocks: Visualizing multiple continuously defined variables. IEEE Computer Graphics and Applications, 73 (3) 57-69. 2007.
[17] B. Pham, Spline-based color sequences for univariate, bivariate and trivariate mapping. Proc. IEEE Visualization. 202-208, 1990.
[18] R.M. Pickett and G.G. Grinstein, Iconographic displays for visualizing multidimensional data. Proc. IEEE Conference on Systems, Man and Cybernetics. 1, 514-519, 1988.
[19] M.I. Posner, Information reduction in the analysis of sequential tasks. Psychological Review, 71 (6) 491-504, 1964.
[20] P. Rheingans, Dynamic color mapping of bivariate qualitative data. IEEE Vis 97. 1997.
[21] P. Robertson and J. O'Callaghan, (1986) The generation of color sequences for univariate and bivariate mapping IEEE Computer Graphics and Applications, 6.
[22] P. Rheingans, Task-based color scale design. SPIE. Task-Based Color Scale Design. Proceedings Applied Image and Pattern Recognition. SPIE, 35-43. October 1999.
[23] A.R. Smith, (1978). Color Gamut Transform Pairs. Proc. SIGGRAPH'78. Computer Graphics 12 (3). 12-19.
[24] E. Trumbo, Theory for Coloring Bivariate Statistical Maps," The American Statistician, vol. 35, no. 4, pp. 220-226, 1981.
[25] C. Ware and KnightW., Orderable dimensions of visual texture for data display: orientation, size and contrast. Proc. ACM CHI'92 203-209, 1992.
[26] C. Ware, Color Sequences for Univariate Maps:" Theory, Experiments and Principles, IEEE Computer Graphics and Applications, Sept. 1988, pp. 41-49, 1988.
[27] H. Wainer and C.M. Fancolini, An empirical inquiry concerning human understanding of two variable map. The American Statistician, 34 (2) 81-89, 1980.
[28] C.E. Weigle, W. Emigh, G. Iiu, R.M. Taylor II, J.T. Enns, C.G. Healey, oriented sliver textures: A technique for local value estimation of multiple scalar fields. Proceedings, Graphics Interface, 2000, 163-170.
[29] G. Wyszecki, W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, Wiley, NY. 1982.
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