A Bounded Measure for Estimating the Benefit of Visualization
Abstract
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.
Cite
@article{arxiv.2002.05282,
title = {A Bounded Measure for Estimating the Benefit of Visualization},
author = {Min Chen and Mateu Sbert and Alfie Abdul-Rahman and Deborah Silver},
journal= {arXiv preprint arXiv:2002.05282},
year = {2022}
}
Comments
Comment on version 2: This revised version, which includes a new formal proof, many additions, and a detailed revision report, was submitted to SciVis 2020. Unexpectedly, our revision effort did not have much influence on the SciVis 2020 reviewers who gave an outright rejection with lower scores than EuroVis reviews. We will share these reviews after we have completed our feedback