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Selecting Semantically-Resonant Colors for Data Visualization

Sharon Lin, Julie Fortuna, Chinmay Kulkarni, Maureen Stone, Jeffrey Heer
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.


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.

materials and links


Sharon Lin, Julie Fortuna, Chinmay Kulkarni, Maureen Stone, Jeffrey Heer
Computer Graphics Forum (Proc. EuroVis), 2013
PDF (481.6 KB) | Best Paper Award