The Noisy Work of Uncertainty Visualisation Research: A Review
This work addresses confusion in the visualization community by clarifying uncertainty representation, which is incremental as it synthesizes existing literature rather than introducing new methods.
The paper tackles the problem of conflicting results in uncertainty visualization research due to unclear definitions, and provides a review with workable definitions and examples to guide future methodology and experiments.
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.