Large datasets at a glance: combining textures and colors in scientific visualization

This paper presents a new method for using texture and color to visualize multivariate data elements arranged on an underlying height field. We combine simple texture patterns with perceptually uniform colors to increase the number of attribute values we can display simultaneously. Our technique builds multicolored perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in an element are used to vary the appearance of its pexel. Texture and color patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by varying three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and human visual psychophysics have identified these dimensions as important for the formation of perceptual texture patterns. The pexels are colored using a selection technique that controls color distance, linear separation, and color category. Proper use of these criteria guarantees colors that are equally distinguishable from one another. We describe a set of controlled experiments that demonstrate the effectiveness of our texture dimensions and color selection criteria. We then discuss new work that studies how texture and color can be used simultaneously in a single display. Our results show that variations of height and density have no effect on color segmentation, but that random color patterns can interfere with texture segmentation. As the difficulty of the visual detection task increases, so too does the amount of color on texture interference increase. We conclude by demonstrating the applicability of our approach to a real-world problem, the tracking of typhoon conditions in Southeast Asia.