Color is the most powerful perceptual channel available for exposing and communicating data. Most visualizations are rendered in one of a handful of common colormaps - the rainbow, cool-warm, heat map and viridis. These maps meet the basic criteria for encoding data - perceptual uniformity and reasonable discriminatory power. However, as the size and complexity of data grows, our need to optimize the potential of color grows. The ability to expose greater detail and differentiate between multiple variables becomes ever more important. To meet this need we have created ColorMoves, an interactive colormap construction tool that enables scientists to quickly and easily align a concentration contrast with the data ranges of interest. Perceptual research tells us that luminance is the strongest contrast and thus provides the highest degree of perceptual discrimination. However, the most commonly used colormaps contain a limited range of luminance contrast. ColorMoves enables interactive constructing colormaps enabling one to distribute the luminance where is it most needed. The interactive interface enables optimal placement of the color scales. The ability to watch the changes on ones data, in real time makes precision adjustment quick and easy. By enabling more precise placement and multiple ranges of luminance one can construct colomaps containing greater discriminatory power. By selecting from the wide range of color scale hues scientists can create colormaps intuitive to their subject. ColorMoves is comprised of four main components: a set of 40 color scales; a histogram of the data distribution; a viewing area showing the colormap on your data; and the controls section. The 40 color scales span the spectrum of hues, saturation levels and value distributions. The histogram of the data distribution enables placement of the color scales in precise locations. The viewing area show is the impact of changes on the data in real time. The controls section enables export of the constructed colormaps for use in tools such as ParaView and Matplotlib. For a clearer understanding of ColorMoves capability we recommend trying it out at SciVisColor.org.
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- 1916 Data and information discovery;
- 1920 Emerging informatics technologies;
- 1992 Virtual globes;
- 1994 Visualization and portrayal;