The Noisy Work of Uncertainty Visualisation Research: A Review

TL;DR

This review critically examines the current state of uncertainty visualization in data graphics, clarifying definitions and highlighting conflicting findings to guide future research and methodology.

Contribution

It provides clear definitions of uncertainty, illustrates them with examples, and discusses the challenges in achieving transparency in statistical graphics.

Findings

Conflicting results due to unclear uncertainty definitions

Need for standardized terminology in uncertainty visualization

Guidance for future graphics methodology and research

Abstract

Better representation of the uncertainty in a data visualisation is a focus of recent research activity. A problem with the current literature is that there is a lack of clarity about the definition of uncertainty and what it means to represent it in a plot. This confusion results in a significant amount of conflicting results in the literature, especially in experiments that assess the effectiveness of different uncertainty representations. In this review, we summarise the current literature, provide workable definitions, and illustrate these definitions with examples. In doing so, we ask what it really takes to achieve transparency in statistical graphics. It is hoped that it will be useful for guiding new graphics methodology and experimental research.