A Bounded Measure for Estimating the Benefit of Visualization

TL;DR

This paper proposes a revised, bounded measure for estimating visualization benefits using information theory, improving interpretability and estimation over previous unbounded measures, validated through case studies.

Contribution

It introduces a new bounded divergence measure for visualization benefit estimation, replacing the unbounded term in existing information-theoretic measures.

Findings

The new measure has better mathematical properties.

Case studies demonstrate practical applicability.

Real-world data supports measure selection.

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.