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Published on Dec 1, 2016
Visualizations can be clear or obscure depending on the color scheme used to represent the data, and careful use of color can also be attractive. However, colormaps have not generally received the attention they deserve, given their significance. The colors used carry the responsibility of conveying data honestly and accurately. They should generally be perceptually uniform so that equal steps through the dataset are represented by equal perceptual jumps in the colormap. They should be intuitive to help support quick, natural understanding of the data. They should match basic properties of the data, like showing the presence of information (sequential) or anomalies in a field (diverging). Additionally, just as different variables are typically represented with different specific Greek letters when written, different variables should also be represented with different colormaps when plotted. A suite of colormaps called cmocean have been developed to meet the needs of oceanographers, and can be used by any plotter out there. The suite is freely available for many different software packages (including Python and R). You can use these colormaps to help convey your data honestly and accurately.
Kristen Thyng works at Texas A&M as an assistant research professor. She comes from a interdisciplinary background, with a BA in Physics (Whitman College), a masters in Applied Mathematics (University of Washington), and a PhD in Mechanical Engineering (UW), and is now in an oceanography department. Her work is primarily in ocean modeling from a physics perspective — predicting flow fields, and using that information to better understand material transport, like oil and phytoplankton.