Colour Blinded by the Noise

2026-08-18

AI Summary

研究引入了一种新的不确定性可视化评估方法，通过类似Ishihara色盲测试的方式将不确定性视为噪声进行评估，比较了五种不同的不确定性可视化技术。

Abstract

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.

Problem

• uncertainty visualisation
• data transparency
• map visualisations
• conflicting results

Innovation

• uncertainty visualisation
• noise evaluation
• Ishihara colourblind test adaptation
• signal suppression