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Selecting Semantically-Resonant Colors for Data Visualization

Sharon Lin, Julie Fortuna, Chinmay Kulkarni, Maureen Stone, Jeffrey Heer. Computer Graphics Forum (Proc. EuroVis), 2013
Figure for Selecting Semantically-Resonant Colors for Data Visualization
Bar charts depicting fictional fruit sales, each using the same backing color palette. The chart on the left uses our semantically-resonant assignment algorithm to pick colors that are representative of the data values. The chart on the right uses a default assignment that does not take color-concept associations into account.
PDF | Best Paper Award
We introduce an algorithm for automatic selection of semantically-resonant colors to represent data (e.g., using blue for data about "oceans", or pink for "love"). Given a set of categorical values and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value-color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert-chosen semantically-resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories.
  title = {Selecting Semantically-Resonant Colors for Data Visualization},
  author = {Lin, Sharon AND Fortuna, Julie AND Kulkarni, Chinmay AND Stone, Maureen AND Heer, Jeffrey},
  journal = {Computer Graphics Forum (Proc. EuroVis)},
  year = {2013},
  url = {https://idl.uw.edu/papers/semantically-resonant-colors},
  doi = {10.1111/cgf.12127}