被噪声蒙蔽的色彩
Colour Blinded by the Noise
AI总结：
本研究针对不确定性可视化评估的矛盾问题，提出将不确定性作为噪声评估的新方法，改造石原色盲测试为新型测试，评估五种可视化方法并建立相关基础理论。
AI中文摘要：
不确定性可视化对于数据透明度至关重要，尤其是在数据常被聚合处理的地图可视化场景中。尽管该领域十分重要，但评估不确定性可视化的研究尚未达成共识，且结果相互矛盾。本研究引入一种新的不确定性可视化评估方法，尝试将不确定性作为噪声而非信号进行评估。我们评估五种不确定性可视化方法：标准 choropleth 地图、数值/方差双变量地图、抑制数值的不确定性调色板、叠加采样、像素化采样地图。基于隐式测试原则，我们对经典石原色盲测试进行“不确定性可视化”改造，创建了一种新颖测试，能将不确定性作为噪声进行评估。我们在不同群组分离水平下，将信号可见性与传统假设检验进行比较。通过将实验设计建立在成熟图形理论之上，我们分离出有助于成功抑制信号的绘图组件，并为不确定性可视化的感知建立基础理论。
英文摘要：
Uncertainty visualisation is important for data transparency, especially for map visualisations where data is often aggregated. Despite the importance of this area, studies evaluating uncertainty visualisation lack consensus and produce conflicting results. This work introduces a new evaluation approach for uncertainty visualisation that attempts to assess uncertainty as noise, rather than signal. We evaluate five methods of visualising uncertainty: standard choropleth maps, value/variance bivariate maps, value-suppressing uncertainty palettes, overlaid sampling, and pixelated sampling maps. Built on principles of implicit testing, we put an 'uncertainty visualisation' spin on the classic Ishihara colourblind test to create a novel test that is able to evaluate uncertainty as noise. We compare signal visibility to conventional hypothesis tests at various levels of group separation. By building our experimental design on top of established graphics theory, we isolate the plot components that facilitate successful signal suppression and establish foundational theory for the perception of uncertainty visualisation.
