Data Visualization and Color Vision Deficiency

Color Vision Deficiency

What is Color Vision Deficiency? It is typically referred to as color blindness and can be defined as the inability to distinguish certain colors, or any colors at all. Statistically, 8% of men and 0.5% of women have been diagnosed with Color Vision Deficiency (CVD). There are seven main types of CVD. Four of them struggle with distinguishing red and green.

  • Deuteranomaly- makes green look more red and for the most part is mild and more common form of CVD.

  • Protanomaly- makes red look more green and is also typically mild for the most part.

  • Protanopia and Deuteranopia- makes red and green indistinguishable from each other.

Two types that struggle with distinguishing blue and yellow.

  • Tritanomaly- makes it hard to tell the difference between blue and the difference between yellow and red; however, this is relatively rare.

  • Tritanopia- makes blue and green, purple and red and yellow and pink indistinguishable.

Lastly, monochromatic CVD

  • Achromatopsia- the total or partial absence of color; therefore, only being able to see in black, white and shades of gray.

Many people when creating data visualizations fail to consider those who are color vision deficient.

The Importance of Color Colors are a compelling medium for communicating messages and meaning. Different colors and shades can be used to convey separate messages and provoke specific emotions; using certain colors together can have effects on each other, complimenting the colors or juxtaposing them. Colors can capture the attention of the audience and highlight important data. Well-chosen colors reduce the time to understand messages and important information. Colors can turn a dull visualization into a thought-provoking data story.

Common Mistakes

When constructing data visualizations, creators put a lot of thought into their color choices; however, they commonly forget to think about how their visualizations will look to those who struggle to see certain colors. When creating data visualizations choosing the correct colors is imperative to an effective visual. Through colors you can evoke emotions, illustrate relationships between different types of data, highlight important information, convey a specific message, and much more. When the colors you choose are not perceived in the same way by the individual looking at your visual, it inhibits its ability to display the message/information you were trying to convey.

Commonly, people use colors such as red and green or blue and yellow in close relations to each other, making it hard for CVD viewers to distinguish differences on the graph. The figure below represents a data visualization mistake and how certain CVD viewers would interpret the visualization. Some of the biggest navigation applications, Google Maps and Waze, both do not offer a change in color scheme that would make it easier for CVD users to distinguish colors. These applications use colors such as red, green, blue, and yellow in their maps, the main colors that people with CVD tend to struggle identifying. These applications are most commonly used while driving, making it more difficult for CVD users to distinguish colors they already find difficult to identify just by glancing at the application.

Another mistake is contrast. Colors that may appear to contrast initially may blend together for those with CVD. The image below is an example graph of student achievement.

To the common eye this graph looks as if it has distinguishable colors that contrast each other.However, the graphs below display how this same graph would look like to someone with one of the four most common types of CVD.

As you can see the graph's contrast has been lost and some of the colors are much harder to distinguish, thus increasing the time it takes for the view to understand the data or message trying to be conveyed and decreasing the overall effectiveness of the visualization.


When choosing colors for data visualizations a simple solution is to contemplate who your audience might be. Creating color blind friendly color palettes does not have to be difficult. Some data visualization tools such as Tableau and PlotTwist supply users with a pre-assembled color-blind friendly color palette. The figure on the left below displays the Tableau palette and the figure to the right displays the PlotTwist color palette.

However, if the data visualization tool you are using does not provide a preassembled color-blind friendly palette, then a great alternative is to find a color-blind simulator. This would allow you to select your potential color scheme and be able to view it similarly to that of someone who has CVD, allowing you to wisely choose your color scheme. Attached below is a helpful simulator that you are able to upload your own photos and get accurate results!

Coblis — Color Blindness Simulator – Colblindor (

Another great solution is adding texture and shapes to your visualization. This eliminates the need to distinguish color all together and makes your visualization more readable for both those with and without CVD.


When creating data visualizations, the majority of people fail to consider those who do not see colors the same way most people do. This greatly restricts the potential of your visualizations. Messages, emotions, and key data points can be miscommunication or not communicated at all to the viewer. With this being said there are very simple solutions. The first being using data visualization tools such as Tableau and PlotTwist that have built in color blind friendly color palettes; however, if you are using a specific data visualization tool that does not provide color blind friendly color palettes then a great aid in creating your own color blind friendly color palette is a color blind simulator where you are able to view colors the way your viewer with CVD would see them. This would allow you to assess and adjust accordingly. With these simple solutions you can make sure that your data visualizations are effective and visually appealing to all viewers.

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