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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.