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Danielle Albers Szafir; Color Perception in Data Visualizations. Journal of Vision 2018;18(10):1353. doi: 10.1167/18.10.1353.
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Many data visualizations use color to convey values. These visualizations commonly rely on vision science research to match important properties of data to colors, ensuring that people can, for example, identify differences between values, select data subsets, or match values against a legend. Applying vision research to color mappings also creates new questions for vision science. In this talk, I will discuss several studies that address knowledge gaps in color perception raised through visualization, focusing on color appearance, lightness constancy, and ensemble coding. First, conventional color appearance models assume colors are applied to 2° or 10° uniformly-shaped patches; however, visualizations map colors to small shapes (often less than 0.5°) that vary in their size and geometry (e.g., bar graphs, line charts, or maps), degrading difference perceptions inversely with a shape's geometric properties (Szafir, 2018). Second, many 3D visualizations embed data along surfaces where shadows may obscure data, requiring lightness constancy to accurately resolve values. Synthetic rendering techniques used to improve interaction or emphasize aspects of surface structure manipulate constancy, influencing people's abilities to interpret shadowed colors (Szafir, Sarikaya, & Gleicher, 2016). Finally, visualizations frequently require ensemble coding of large collections of values (Szafir et al., 2016). Accuracy differences between different visualizations for value identification (e.g., extrema) and summary tasks (e.g., mean) suggest differences in ensemble processing for color and position (Albers, Correll, & Gleicher, 2014). I will close by discussing open challenges for color perception arising from visualization design, use, and interpretation.
Meeting abstract presented at VSS 2018
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