APA
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Hagh-Shenas, H., Interrante, V., Healey, C. G., & Kim, S. (2007). 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.
Chicago/Turabian
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Hagh-Shenas, H., V. Interrante, C. G. 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.” IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (2007): 1270–1277.
MLA
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Hagh-Shenas, H., et al. “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, vol. 13, no. 6, 2007, pp. 1270–77.
BibTeX Click to copy
@article{h2007a,
title = {Weaving versus blending: A quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color},
year = {2007},
issue = {6},
journal = {IEEE Transactions on Visualization and Computer Graphics},
pages = {1270-1277},
volume = {13},
author = {Hagh-Shenas, H. and Interrante, V. and Healey, C. G. and Kim, S.}
}
In many applications, it is important to understand the individual values of, and relationships between, multiple related scalar variables defined across a common domain. Several approaches have been proposed for representing data in these situations. In this paper we focus on strategies for the visualization of multivariate data that rely on color mixing. In particular, through a series of controlled observer experiments, we seek to establish a fundamental understanding of the information-carrying capacities of two alternative methods for encoding multivariate information using color: color blending and color weaving. We begin with a baseline experiment in which we assess participants' abilities to accurately read numerical data encoded in six different basic color scales defined in the L*a*b* color space. We then assess participants' abilities to read combinations of 2, 3, 4 and 6 different data values represented in a common region of the domain, encoded using either color blending or color weaving. In color blending a single mixed color is formed via linear combination of the individual values in L*a*b* space, and in color weaving the original individual colors are displayed side-by-side in a high frequency texture that fills the region. A third experiment was conducted to clarify some of the trends regarding the color contrast and its effect on the magnitude of the error that was observed in the second experiment. The results indicate that when the component colors are represented side-by-side in a high frequency texture, most participants' abilities to infer the values of individual components are significantly improved, relative to when the colors are blended. Participants' performance was significantly better with color weaving particularly when more than 2 colors were used, and even when the individual colors subtended only 3 minutes of visual angle in the texture. However, the information-carrying capacity of the color weaving approach has its limits. We found that participants' abilities to accurately interpret each of the individual components in a high frequency color texture typically falls off as the number of components increases from 4 to 6. We found no significant advantages, in either color blending or color weaving, to using color scales based on component hues thatare more widely separated in the L*a*b* color space. Furthermore, we found some indications that extra difficulties may arise when opponent hues are employed.
