What's wrong with grayscale? Why do we need to add confounding variables?

Pointing that luminance is the best channel to encode spatial frequency is akin to encouraging greyscale.

It’s just that you can do it with a hue and a non zero saturation if it suits the style you are looking for.

Plus sometimes you have more than on me set of data mixed and it’s useful to be able to distinguish both by using multiple colours.

When you want to plot a categorical distribution you need to encode category by colour, but you want them to appear maximally distinctive.

A whole lot of work has been done more recently on "perceptually uniform" color palettes. See the Brewer color palette family, the "scientific colourmaps" by Crameri, and the Viridis color palette family, the latter of which was famously adopted by Matplotlib several years ago.

Note that most modern palettes are designed with various forms of color blindness in mind. For that purpose, they artificially restrict the range of colors, which in turn reduces the visual information they can carry for the vast majority of people who have normal color vision.

I'd be super interested to see a study that examines how color blindness-aware design affects people without color blindness. Such design often completely avoids red hues, which are of huge importance as visual cues to normally sighted people. It's hard to imagine this doesn't have a negative impact somehow, yet I've never seen this discussed anywhere.

Careful with that kind of talk. Accessibility trolls haunt these forums and will call you a ableist and threaten you with lawsuits and bad names.

Its interesting, but I suspect its too close to wrongthink for anyone to touch. Current trends in design space are all about how catering for hyper-edge cases creates a better overall product; "normally sighted" is a microaggression.

Finding that you can set up a colour range that is better for the people who can see all the colours is inevitable; but the implication is that you will now have to decide between "best" and "inclusive".

Aren't something like 1 in 20 or so people color blind? I'd hardly consider that as an edge case. That's roughly the odds of being over 6ft tall in the US.

You might not know this based on your response, but almost 10% of men are colorblind.

This is ignorant and wrong. You are assuming that colorblind-friendly palettes are worse than others, but that isn't the case. In my experience, colorblind-friendly palettes actually tend to be nicer looking.

It's not wrongthink, but you might just be a jerk.

My subjective experience is that good color palettes don't have too many colors anyway. Colorblind-friendliness tends to mean avoiding pairs of complementary colors that don't necessarily convey information well anyway.

Remember that we are talking about data visualization, not color palettes in general. Red is not particularly important in data viz.

Consider that many non-color-blind Dota players use the colorblind settings because they like the colors better.

> Red is not particularly important in data viz.

Traditionally, red is used to indicate problematic or dangerous values. This is backed up by a long cultural history, at least in the Western cultural context, that associates red with danger.

I'd say this fact alone makes the color red extremely important in data visualization. The current trend of "but some people can't see red, so let's just use yellow and blue everywhere" seems utterly inadequate for addressing both the importance of red and the problem of color blindness.

They're designed with color blindness in mind for good reason. Prevalence in males is between 5-10% but in my experience it's higher in software industry, for whatever reason.

Of course restricting available colors affects the diversity of palettes, but often designs can accommodate both handily.

It's nearly 2023 and many, many charts are interactive. There's little excuse to at least having a color blind mode be opt-in for such things. If we're all looking at charts on our personal devices, then local accomodations render your entire argument moot.

You can have your cake and eat it too.

> but often designs can accommodate both handily

Where is the research to back this up? While I've seen tons of articles that go into great detail on how to simulate and accommodate various forms of colorblindness, the idea that those accommodations have no negative impact on the rest of the population appears to be taken as an axiom that somehow requires no data to support it.

Most charts don't have a ton of data. Choosing four accessible colors is not challenging. Most UIs are monochromatic.

I'm not sure I need data, most interfaces or designs you see already accommodate color blindness via simplicity. Those that do not can almost always do so by adjusting the palette subtly, with almost no perceptual difference for those who are not colorblind.

Obviously simulating colorblindness on a general graphics level is less effective, and I don't think anyone is suggesting we apply such filters universally.

The center of my argument is that it is easy with software, which has consumed the information world, to simply offer alternative viewing modes, of which colorblind modes are not very complicated.

Great and fun talk on the development of Viridis: https://www.youtube.com/watch?v=xAoljeRJ3lU

> (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.

Your inference doesnt follow from the first point. Hue is bad for magnitude because you dont see a different hue as 'more' or 'less' just 'different'. Hue is great for representing difference without defining one as a focus or preference over the other.

>Indeed for her kind of data the rainbow palette works okay. But if you have to visualize more complex / denser data, beware of the pitfalls of the rainbow color palette. It is not always the best choice.

There is already a trap in this palette, the first color looks awfully like the last. It's fine if you just want a palette for a bunch of lines but using it for heatmap could be a bit misleading in edge cases

I have a pet theory that certain high spatial frequencies bleed into being perceived a colors anyway. The reason being that the red, green, and blue cones in the retina each have a different amount of space between them but are otherwise organized in the same pattern. Thus if the brain sees something with the same spacing as the blue cones, its going to assume that's caused by it being a blue object (rather than a black/white object which happened to have the same spatial freq as the blue cells). You seem to know the literature here, is there anything validating or invalidating my pet theory?

I'm no expert, but that sounds a lot like how halftone and dithering work.

I've never noticed such happening by eyes, but I've definitely seen distant ocean reflections (tiny randomly-shifting specks of light) being misinterpreted as colors by Bayer matrix camera sensors.

full disclosure, this pet theory is entirely based on observations of people on LSD. High frequency spatial patterns consistently get distorted in colorful ways.

This is called metameric failure

This is a whole area of research (as you probably know). I personally only like HSV colourmaps for visualisations of phase, as it has no boundaries at the edges and therefore when you wrap over 2π again you don't get a hard discontinuity. Often you're not actually interested in what the "number" is, but how quickly it is cycling – something that it gets across quite well.

I agree that trying to use hue for encoding magnatude information is not great, but man, how many times have I given up on someone's infographic that decided to use four different shades of blue and purple you can't tell apart when the markers are only eight or sixteen pixels on the screen.

I should hope the 12-bit rainbow palette would be adopted for those kinds of visualizations.

What is often needed in visualizations is showing several parameters next to each other. Think not color maps that showvariation of one value, like a heat map, but line or bar charts, or area maps with various pars marked.