Data for: Towards a Visual Guide for Communicating Uncertainty in Visual Analytics

Published: 25 February 2020| Version 1 | DOI: 10.17632/553mwyxb9z.1
Karsten Seipp, Xavier Ochoa, Katrien Verbert, Francisco Gutiérrez


This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the ``goodness-of-fit'' of their data to the algorithm. This is important for Visual Analytics applications, which combine information visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work. The CSVs show the qualitative and quantitative data collected in the study, separately for each algorithm visualisation. In particular, they show users' preference and ability to differentiate various levels of uncertainty using the visual means opacity, blur, texture, variable grid, and contour lines.



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