Categorical Colormap Optimization with Visualization Case Studies

Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a se...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 23(2017), 1 vom: 03. Jan., Seite 871-880
1. Verfasser: Fang, H (VerfasserIn)
Weitere Verfasser: Walton, S, Delahaye, E, Harris, J, Storchak, D A, Chen, M
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2017
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital
Beschreibung:Date Completed 30.07.2018
Date Revised 30.07.2018
published: Print
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506