Visualization of Uncertainty without a Mean

As dataset size and complexity steadily increase, uncertainty is becoming an important data aspect. So, today´s visualizations need to incorporate indications of uncertainty. However, characterizing uncertainty for visualization isn´t always straightforward. Entropy, in the information-theoretic sense, can be a measure for uncertainty in categorical datasets. The authors discuss the mathematical formulation, interpretation, and use of entropy in visualizations. This research aims to demonstrate entropy as a metric and expand the vocabulary of uncertainty measures for visualization.