Data-Driven Automation of Color Encodings for Data Visualization (NSF CHS 1657599)
Sponsor: National Science Foundation
Abstract: Graphs, charts, and other visualizations of data rely on color both to convey key aspects of the underlying data and to attract and engage viewers. Getting both the accuracy and aesthetics of color choices right, however, is hard, and most existing tools for helping designers focus on just one of the two.
The research team will use designs created by existing tools to construct an initial statistical model of color mappings that captures expert designers' current decision-making. Developing accurate color mappings is even harder because how colors are perceived changes depending on the size and shape of visual marks, lighting and contrast, and a number of other factors. Color ramps will be represented as a set of control points (two end points in sequential encodings and two end points plus a midpoint in diverging ramps) that determine the overall structure of the ramp, and a smooth interpolation path that connecting the control points in colorspace. In addition to developing the specific models and tools around color encodings, the work sets up a broader research agenda of combining automation and interaction, in which semi-automated guidance democratizes effective visualization practice and allows people to leverage prior designs and create new representations without requiring extensive visualization training.
Elliott, M. C. Xiong, C. Nothelfer, & D. Albers Szafir. “A Design Space of Vision Science Methods for Visualization Research.” IEEE Transactions on Visualization, 2021 (to appear).
Reda, K. & Szafir, Danielle Albers. “Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference.” IEEE Transactions on Visualization, 2021 (to appear).
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