Envisioning information
Visualizing multivalued data from 2D incompressible flows using concepts from painting
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization
IEEE Transactions on Visualization and Computer Graphics
Harnessing Natural Textures for Multivariate Visualization
IEEE Computer Graphics and Applications
Effectively Visualizing Multi-Valued Flow Data using Color and Texture
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Simple 3D Glyphs for Spatial Multivariate Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Multivariate Glyphs for Multi-Object Clusters
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Term distribution visualizations with Focus+Context
Proceedings of the 2009 ACM symposium on Applied Computing
Analyzing statistical relationships between global indicators through visualization
ICTD'09 Proceedings of the 3rd international conference on Information and communication technologies and development
Term distribution visualizations with Focus+Context
Multimedia Tools and Applications
A new weaving technique for handling overlapping regions
Proceedings of the International Conference on Advanced Visual Interfaces
Technical Section: Using color in visualization: A survey
Computers and Graphics
Comparing averages in time series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual comparison for information visualization
Information Visualization - Special issue on State of the Field and New Research Directions
Hi-index | 0.00 |
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.