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

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.

本文为构建适配现有可视化框架与工具集的不确定性传递可视化指南迈出了第一步。本指南的首个组成部分，由一组适用于表征算法机制中面域不确定性（areal uncertainty）的可视化变量构成。此类可视化可向用户展示数据点在分类空间中的分布情况，并帮助其理解自身数据与对应算法的拟合优度（goodness-of-fit）。这对于可视化分析（Visual Analytics）应用场景尤为重要——此类应用将信息可视化与信息挖掘技术相结合，应用于交互式决策流程之中。由离散分布数据点引发的模型不确定性，需通过可视化手段呈现，以便用户调整分析流程并优化分析结果。

为充分利用已有的认知与既有含义，我们探索了地理空间可视化（geospatial visualisation）领域中用于表征面域不确定性的主流可视化变量，是否同样可有效表征K均值（K-means）聚类与线性回归（Linear Regression）算法机制可视化中的不确定性——这两类算法均基于数据点的空间分布实现运算。在一项包含500名参与者的用户研究中，我们发现：整体而言，透明度（opacity）这一可视化手段的表现最优，其次为纹理（texture）；但网格（grid）与模糊（blur）或许并不适用于不确定性的量化表征。等高线（contour lines）的表现似乎取决于所采用的算法可视化方案。依托本次研究，我们将地理空间可视化领域的若干特定领域研究结论的有效性，拓展至算法机制可视化场景中，并以此构建了不确定性表征跨学科可视化指南的首批基础模块，为后续研究奠定了颇具前景的基础。

本次研究收集的定性与定量数据将通过逗号分隔值（CSV）文件呈现，且将按不同算法可视化方案分别归档。具体而言，这些文件展示了用户在借助透明度、模糊效果、纹理、可变网格与等高线这些可视化手段区分不同程度不确定性时的偏好与能力表现。