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) × shape, color (ordered) × size, size × shape, and size × orientation—in the context of bivariate symbol maps. Both accuracy and speed analyses show that color × shape is the most separable and size × orientation the least separable, while size × color and size × 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.
ACM CHI Conference on Human Factors in Computing Systems