A Bounded Measure for Estimating the Benefit of Visualization
This work addresses a specific theoretical issue in visualization analysis for researchers and practitioners, but it is incremental as it builds on existing measures.
The authors tackled the problem of an unbounded term in existing information-theoretic cost-benefit measures for visualization, which is hard to estimate and interpret, by proposing a revised measure with a bounded term, and they applied it to two case studies to demonstrate practical use.
Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon divergence and a new divergence measure formulated as part of this work. We use visual analysis to support the multi-criteria comparison, narrowing the search down to those options with better mathematical properties. We apply those remaining options to two visualization case studies to instantiate their uses in practical scenarios, while the collected real world data further informs the selection of a bounded measure, which can be used to estimate the benefit of visualization.