(I submitted it yesterday but it didn't get much traction[2], which makes sense given how specific it is)
[0] http://journals.plos.org/plosone/article?id=10.1371%2Fjourna...
This is true. But, unfortunately, it is the current standard for many applications.
For example: although a luminance-varying colormap is theoretically better, if my doctor has been analyzing MRI scans using the jet palette for 30 years, I want him to use that color palette when he analyzes mine, instead of using a new one.
I have similar problems in my work, when representing crystallographic textures. The standard way of representing them has multiple problems (not only color choices, but also the use of Euler angles to represent orientations is a huge headache). However, if I publish a paper using a different (objectively better) method, nobody will understand my figures. I have tried including both, but I think almost everybody just ignores the new ones.
That article/post reached some audience and was referenced quite well at the time. Keep spreading the word I guess.
[0] http://medvis.org/2012/08/21/rainbow-colormaps-what-are-they...
Luminance inevitably conveys a certain ordering.
A dark black line will be automatically identified as more as important than a light grey line.
This can be desired (and abused), but often I want to present my categories as neutral as possible. I experimented with balancing lightness with other features (like line width) but not with much success.
https://web.archive.org/web/20180225102505/https://www.resea...
GitHub displays the PDF just fine on desktop but fails on mobile.
(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.
(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a colormap which has a strong luminance variation across the data range.
(3) For interval and ratio data, both luminance- and saturation-varying colormaps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.
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[1] https://www.research.ibm.com/people/l/lloydt/color/color.HTM
or as pdf:
https://github.com/frankMilde/interesting-reads/blob/master/...