A rule-based tool for assisting colormap selection

Abstract

The paper presents an interactive approach for guiding the user's select of color-maps in visualization. PRAVDAColor, implemented as a module in the IBM Visualization Data Explorer, provides the user a selection of appropriate color-maps given the data type and spatial frequency, the user's task, and properties of the human perceptual system. 1: IntroductionVisualization is a process of mapping data onto visual dimensions to create a pictorial representation. A successful visualization provides a representation which allows the user to gain insight into the structure of the data, or to communicate aspects of this structure effectively [2] [4] [22]. Even with modern visualization systems, which give the user considerable interactive control over the mapping process, it can be difficult to produce an effective visualization. One strategy for improving this situation is to guide the user in the design of a visualization. In our previous work, we have described an interactive rule-based architecture for incorporating such guidance, and have described certain perceptual and cognitive rules which may be relevant [I41 WI 1161. In this paper, we focus on improving the user's selection of colormaps. To do so, we have built a library of colormaps, and a set of perceptual rules for selecting appropriate maps based on the structure of the data and the goal of the visualization. We have encapsulated this rule-based colormap selection process as a tool, PRAVDAColor, in the IBM Visualization Data Explorer software package, and demonstrate how this module can be incorporated into visualization applications involving the mapping of color onto two-and three-dimensional surfaces. This implementation demonstrates the viability of the technique, and provides a testbed for evaluating the rules. In this architecture, each visualization operation can be associated with rules which constrain the set of choices the user is offered. The architecture also provides for linkages between rules that control different visualization operations, with a choice of parameters for one operation constraining choices that are available for others. This network of linked, intelligent operations helps guide the user through the complex process of designing a visualization.In our previous work, we have described the general principles for implementing such an assemblage of rulebased visualization operations. In this paper, we describe a full ,.implementation of one of these operations, colormap selection. In PRAVDAColor, perceptual rules constrain the set of colormaps offered to the user based on system-provided metadata (data type, data range), metadata computed by algorithm (spatial frequency) and metadata provided by the user (the visualization task). This is in contrast to previous rulebased systems for visualization which do not explicitly support user tasks, color perception, or interactivity in the guidance they offer (e.g., [18]). Limitations of Current TechnologyPerhaps the most common operation in visualization is mapping the values ...

A Physiologically-based Model for Simulation of Color Vision Deficiency

“…The choice of an appropriate color scale for a given visualization should take into account different factors, such as characteristics of the dataset, questions that one would like to answer about the data, the intended viewers and their cultural backgrounds [24]. Thus, our model is not intended to automatically build or guide the construction of color maps, even though it can be used to provide information for approaches that do so [3,11,18,30]. Instead, it gives visualization designers an understanding of the perceptual limitations of each class of CVD.…”

“…Kuhn et al [17] The mapping of data content to some color scale is a fundamental operation in visualization and several techniques have been proposed for building or guiding the construction of color maps [3,11,18,30].…”

Visualizing Complex Process Hierarchies during the Modeling Process

“…Next, we use a perception-based colour scheme as suggested in [25] and [26], which have proven to be effective. From a collection of such schemes we choose the most appropriate one with respect to the users' visualization goal, which consists of five different qualitative values of plan ordering.…”

Aesthetic Data Visualization as a Resource for Educating Creative Design

“…The design of an appropriate data mapping metaphor is mostly motivated from task performance considerations, with a strong focus on the accurate and objective depiction of data values. Few research approaches have even proposed automated techniques that recommend the most effective data visualization method according to specific dataset characteristics (Bergman, Rogowitz and Treinish 1995;Healey, Amant and Elhaddad 2000). The integration of creative design aspects is mostly neglected, except of some attention to "usability" or "user experience" effects.…”