Visualizing estimates by race/ethnicity: color gradients vs. discrete colors

Hello all! My team is working to standardize our health-related Tableau data vizzes. I tend to use color ramps/gradients to visualize estimates of disease by demographics (including race/ethnicity - fully acknowledging these large/aggregated racial/ethnic categorizations are highly problematic - but that’s another issue) Others on my team like using discrete colors. My understanding is that colors should intuitively contribute to the meaning of the data viz - light colors corresponds to lower values, darker colors correspond to higher values. I’ve seen good examples of both strategies (color ramps and discrete colors). Any input, including any unintended consequences of using one over the other is appreciated!

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I’ve heard from a few of my colleagues who are colorblind that color gradients are really difficult to differentiate for them though I don’t know if that’s true for everyone. However, I think I’ve heard Heather recommend asking a focus group of people being represented by the data how they would like data to be visualized and to explore what colors may indicate to them, etc. I’d love to hear what you decide!


I’m team different colors, or using color to highlight a main point and gray for the other categories (aka using storytelling). I think using a gradient for nominal data would be confusing-- like are the colors about the values, or are they about the categories themselves? I think you could even run into it looking like you’re trying to have the colors represent skin tone if you use a gradient and white people have the lowest disease burden. The Do No Harm Guide from the Urban Institute has some other great examples of unintended consequences with color choice:

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