Studying the Separability of Visual Channel Pairs in Symbol Maps
2026-02-23 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how well people can separate different visual features like color, size, shape, and orientation when used together to show two pieces of information on a map. Through a crowdsourced experiment, they found that color and shape work best together, while size and orientation work the worst for helping people quickly and accurately understand the data. They also discovered that some features are easier to notice depending on which one is more important for the task. Their work helps us understand how to better design maps that show multiple data points clearly.
visualizationmultivariate datavisual channelscolor encodingshape encodingsize encodingorientationseparabilitybivariate symbol mapscrowdsourced experiment
Authors
Poorna Talkad Sukumar, Maurizio Porfiri, Oded Nov
Abstract
Visualizations often encode multivariate data by mapping attributes to distinct visual channels such as color, size, or shape. The effectiveness of these encodings depends on separability--the extent to which channels can be perceived independently. Yet systematic evidence for separability, especially in map-based contexts, is lacking. We present a crowdsourced experiment that evaluates the separability of four channel pairs--color (ordered) x shape, color (ordered) x size, size x shape, and size x orientation--in the context of bivariate symbol maps. Both accuracy and speed analyses show that color x shape is the most separable and size x orientation the least separable, while size x color and size x shape do not differ. Separability also proved asymmetric--performance depended on which channel encoded the task-relevant variable, with color and shape outperforming size, and square shape especially difficult to discriminate. Our findings advance the empirical understanding of visual separability, with implications for multivariate map design.