A General Approach to Visualizing Uncertainty in Statistical Graphics

TL;DR

This paper introduces a general method for visualizing uncertainty in static 2-D statistical graphics by aggregating distributions over images, enabling both standard and novel uncertainty representations with coverage guarantees.

Contribution

It presents a unified approach to uncertainty visualization that generalizes existing methods and provides practical open-source tools for implementation.

Findings

Standard confidence intervals emerge naturally from the approach.

The method can generate both familiar and novel uncertainty visualizations.

Open-source Python library demonstrates practical applicability.

Abstract

We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees without being explicitly quantified or visualized. As a proof of concept, we implement our approach in the IID setting using resampling, provided as an open-source Python library. Because the approach operates directly on images, the user needs only to supply the data and the code for visualizing the quantities of interest without uncertainty. Through several examples, we show how both familiar and novel forms of uncertainty visualization can be created. The implementation is not only a practical validation of the underlying theory but also an immediately usable tool that can complement existing uncertainty-visualization libraries.