But we have to be careful with this. Changing facial expressions is not the same as increasing the height of a barplot, we're relating features with expressions and the visualization might express things that you don't want.
There is a very famous example for this in "Life in Los Angeles" (1977) by Eugene Turner . Maybe you can infer the data well but in the end this just ends up being a map of angry black people. The choice of features and how to visualize them is clearly racist.
That map colored black people dark like their skin, and encoded misery as unhappy faces.
The result accurately showed happy white people and unhappy black people. How is it racist to acknowledge the racially biased distribution of suffering?
There are a number of other lesser reasons, including conflating regions of high urban stress as "Evil" or "Angry" instead of just unhappy. The face visualization just implies too much of a value judgement on the data -- too correlated with the issue (yet misrepresentative) to be a good idea imo.
: Even happy/unhappy may not reflect well at all this variable (again because that's a complicated human emotion, not a simple function of "Health crime and transportation factors").
Edit: I think it's also not necessary to call the map creator (or maybe the map itself) racist, this implies some kind of intentional discrimination (and is quite strong imo), but it does have to me grave mentioned problems.
The legend uses the words "Low/High" and not "Bad/Good." It's a quantitative measure, not a moral/aesthetic judgment.
The conclusion of this graphic would be - There is more stress in South Central LA, which has a higher proportion of Black Americans. Or possibly, the least stress is towards the West, where there are relatively fewer Blacks.
I guess if you are concerned it will read as, "In South Central LA, there are angry black people, don't go there!" - that leap of faith will be made regardless of how you visualize the data. "Poor people are naturally lazy/violent/immoral," is a stereotype that has existed for far longer than any widely accepted attempt at data visualization.
This is definitely not true -- yes it uses Low/High, but different variables have different qualities for Low/High -- and "Good" is always on top. "Low" unemployment is on the top while "High" affluence is also on top (i.e. not purely numeric). That should be obvious because the positive emotions are being associated with positive ("good") variable ranges.
I mean, if a quantitative measurement were the main focus here surely we shouldn't be using faces which are known to be loaded with emotions.
You did not seem to address my main concern, which is specifically that low white percentages are portrayed as negative in the legend. (I'm trying to avoid the Motte/Bailey here) -- do you disagree?
> Poor people are naturally lazy/violent/immoral
I think it's more excusable to equate "Poor"=="Bad" than "Certain race"=="Bad", because it's generally accepted being poor is undesirable, while you can't change your ethnic background (and generally I don't think you should)
Re how faces are emotionally loaded, that is somewhat the point of using Chernoff faces as a visualization method, though I understand your concern that therefore it SHOULD not be used as such a method because it will convey ideas not implied in the data because of how we interpret faces.
Fundamentally though, to go to this data set in particular and away from the merit or otherwise of Chernoff faces, there's always going to be a tension to depicting the correlation between race and wealth in the US. One way or the other, you have to say the same thing - Blacks are poorer; and/or Blacks have lower factors of general well-being (though somewhat unintuitively, not lower levels of hopefulness.) And you can't avoid the fact that if you have a data visualization that really brings home the correlation then someone is bound to assume causation in the wrong direction and feel the data validates their racist feelings.
But that doesn't make the attempt at using that visualization a racist one.
The other comment noted those metrics are "objective" and only "High"/"Low", but in all cases the least desirable situation (i.e. "bad") is the lowest. I read charts and benchmarks all the time and a usual cognitive shortcut you look for is good/bad (e.g. you see a decreasing graph -- if it's latency that is "low"=="good"; if it were profits it's "low"=="bad"), I'd be surprised if people didn't make similar quick assessments. Note that high white rate is also up, which is usually good too (profits, growth, etc.).
I guess no representation is perfect, but I think at least ethnicity could/should be separated here.
1. We humans are actually _ABYSMAL_ at distinguishing faces (https://en.wikipedia.org/wiki/Cross-race_effect)
2. The ability to differentiate between two things and the ability to translate attributes into metrics are fundamentally so different from each other that any possible truth to the idea instantly becomes wildly irrelevant. https://eagereyes.org/criticism/chernoff-faces
That's a very successful application if you ask me. It shows unhappy black people. They are displayed as unhappy because they are unhappy. The focus should be on how to make them happy, not 'racism'. It is displaying the effects of racism for all to see.
It is implemented in D3 and React and the source code is here:
I implemented my own version long ago (for MS-DOS) and I am quite surprised that there is still some interest in the topic.
Blindsight is wonderful. A bit difficult to follow at a time due to the 'unique' writing style. In the same novel it tackles uploading consciousness, vampires, artificial intelligence, aliens, psychology (heavily) and a few more things.
I've yet to find a better description of what would be an actual alien lifeform. Even if it is biased a bit on marine biology given the author's qualifications, the oceans are host to the most 'alien' environments we are aware of that still contain life.
I still can't wrap my head on the concept of the Icarus Array though.
Anyway, for the uninitiated: https://rifters.com/real/Blindsight.htm
and this book is it as Manga girls.
One quote from the paper taken from 'The Craft of Information Visualization' (Bederson and Ben Shneiderma): 'Humans can recognise the spatial configuration of elements in a picture and notice relationships among elements quickly.'
I think your contempt is a serious misfire.
Edit-I see people may take this as a joke. Sorry, not funny. Especially not right now.
> "One of my favorite¹ concepts for multi-dimensional data visualization is the Chernoff Face"
> 1: "Favorite" might be code for "useless," going with the theme of this blog"
Don't act like this project will have actual real world uses and ramifications. No, it won't literally teach people to associate good or bad qualities with facial features, because nobody will ever use it for practical purposes. No, there's no liability to promote prejudice, this is just some programmers ML side-project.
> How is that not objectifying?
It literally is. The generated faces are mathematical objects. They're not real people.
Right now what? Sounds like you're jumping at shadows. In general false positives are good for finding errors in systems. However, they can go too far and lead to pathological patterns like human immune system self-alergies.