Color Theory

Why is Color Important?

One of the best examples of poor color choices is the use of the very common ‘jet’ color map.

The jet color bar

Just presented as a color bar it may be hard to recognize, but most will be familiar with it as the red and blue color scheme from weather reports, still in use as of 2020:

The areas with more cloud density are in red and as it gets less dense it becomes blue
The above image was taken directly from Australia’s Bureau of Meteorology, 24/10/2020.

It is even used in medical imaging, such as the imaging of arteries below:

The appeal of this color map is that it appears at a glance to offer a lot of contrast and it has historically been in common use. However, this color palette actually distorts data because there are too many peaks of brightness, it lacks a sense of uniformity and is also unfriendly towards various forms of color blindness.

In other words: You can kill people by making incorrect color choices. choosing the wrong colors can lead to technical errors, potentially costing lives, or less sales, potentially costing the business.

Making Better Color Choices

Making better color choices in part depends on your data.

Sequential Data

Viridis

Inferno

Diverging

Cyclic

Qualitative

Advanced

Color theory as a subject matter can be as advanced as you might like, as there is a lot of room for optimization. One great example of this is the development of the Viridis colour scheme.

Reference

https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html

https://99designs.com.au/blog/tips/the-7-step-guide-to-understanding-color-theory/

https://jakevdp.github.io/blog/2014/10/16/how-bad-is-your-colormap/

https://www.youtube.com/watch?v=xAoljeRJ3lU

http://iis.seas.harvard.edu/papers/2011/borkin11-infoviz.pdf

https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html

https://www.youtube.com/watch?v=XjHzLUnHeM0