(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.
or as pdf:
(I submitted it yesterday but it didn't get much traction, which makes sense given how specific it is)
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.
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.
GitHub displays the PDF just fine on desktop but fails on mobile.
good_patch = matplotlib.patches.Patch(color='#009F6B', label='good')
marginal_patch = matplotlib.patches.Patch(color='#FFD300', label='marginal')
warning_patch = matplotlib.patches.Patch(color='#FF751F', label='warnings')
bad_patch = matplotlib.patches.Patch(color='#C40233', label='bad')
flag_patch = matplotlib.patches.Patch(color='#0087BD', label='flag')
unknown_patch = matplotlib.patches.Patch(color='#808080', label='unknown')
I wrote about my experience here -> https://mcconnellsoftware.github.io/colourblind-in-software-...
"Viz Palette also makes it easy to check if your colors can be distinguished by colorblind people. A browser extension like Spectrum, an online tool like Coblis or an app like Sim Daltonism can simulate the colors as seen by people with different types of colorblindness, too. In Datawrapper, we integrated a color blindness simulator, so no external tool is necessary."
I'm sure it's not perfect, but it at least helps make things a bit less tricky to get right.
Also, these two are from the "point of significance" column of the said journal which is very informative and good quality. And the column basically covers how to make good figures and present data
Around here a lot of People still like to print physical copies of the morning reports. One thing I've found is that although colors may be distinguishable from each other when viewed on a screen on a print out similar colors all blur together for this reason I like to make sure there is a high contrast between neighboring regions (especially on things like stacked bar charts).
Other big issue is Overhead projectors. It's pretty common for people to review graphs in morning meetings and similar. A lot of projectors are low resolution and also get used in places like control rooms with poor ambient lighting so you can have similar problem with distinguishing color hues.
Using patterns (cross hatching etc) as well as colors also helps distinguish regions from each other.
If you look at industrial control software how dials gauges and the like are represented you can get some good tips on best practices and how to make high contrast easily distinguishable graphics. These things are designed for maximum readability.
There are lots of Calories Per Dollar, Protein Per Dollar, etc... data for specific food items.
The pages were recently updated so the tables are sort-able, but there might be too much data, and reducing the data is bad for the user.
For the viz, at least on chipotle, the colors don't actually show anything? All of calories is green and all of protein is red? I'd just toss it in excel and use their default "high is green, low is red" on each. Then default sort by the most efficient foods. Then maybe test out "high is green, low is white" for price, so we can easily see efficient foods that are cheap, without making expensive foods look explicitly bad.
Chiptole logo needs to be resized. You could try a bar chart also.
If I'd had a dime every time I stared blankly at a data visualisation where accessibility hadn't been a consideration, I'd be sipping margaritas on my own private beach by now...
Oh, and the actual product, that too.
PS: image optimization should be done automatically IMHO