A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation

TL;DR

This paper proposes a bounded measure for estimating visualization benefits by replacing an unbounded term in information-theoretic analysis, introducing new divergence measures, and providing a theoretical foundation for future empirical validation.

Contribution

It introduces a new bounded divergence measure for visualization benefit estimation and offers a theoretical framework for comparing different measures.

Findings

Identified limitations of existing unbounded benefit measures

Proposed new divergence measures with better mathematical properties

Provided a theoretical basis for future empirical evaluation

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 describe the rationale for proposing a new divergence measure. As the first part of comparative evaluation, we use visual analysis to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. The theoretical discourse and conceptual evaluation in this paper provide the basis for further comparative evaluation through synthetic and experimental case studies, which are to be reported in a separate paper.