Selecting Semantically-Resonant Colors for Data Visualization
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and Blackwell Publishing Ltd.
Abstract
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.
Description
@article{:10.1111/cgf.12127,
journal = {Computer Graphics Forum},
title = {{Selecting Semantically-Resonant Colors for Data Visualization}},
author = {Lin, Sharon and Fortuna, Julie and Kulkarni, Chinmay and Stone, Maureen and Heer, Jeffrey},
year = {2013},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/cgf.12127}
}