Data is Beautiful

4923 readers

454 users here now

A place to share and discuss visual representations of data: Graphs, charts, maps, etc.

DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.

A place to share and discuss visual representations of data: Graphs, charts, maps, etc.

  A post must be (or contain) a qualifying data visualization.

  Directly link to the original source article of the visualization
    Original source article doesn't mean the original source image. Link to the full page of the source article as a link-type submission.
    If you made the visualization yourself, tag it as [OC]

  [OC] posts must state the data source(s) and tool(s) used in the first top-level comment on their submission.

  DO NOT claim "[OC]" for diagrams that are not yours.

  All diagrams must have at least one computer generated element.

  No reposts of popular posts within 1 month.

  Post titles must describe the data plainly without using sensationalized headlines. Clickbait posts will be removed.

  Posts involving American Politics, or contentious topics in American media, are permissible only on Thursdays (ET).

  Posts involving Personal Data are permissible only on Mondays (ET).

Please read through our FAQ if you are new to posting on DataIsBeautiful. Commenting Rules

Don't be intentionally rude, ever.

Comments should be constructive and related to the visual presented. Special attention is given to root-level comments.

Short comments and low effort replies are automatically removed.

Hate Speech and dogwhistling are not tolerated and will result in an immediate ban.

Personal attacks and rabble-rousing will be removed.

Moderators reserve discretion when issuing bans for inappropriate comments. Bans are also subject to you forfeiting all of your comments in this community.

Originally r/DataisBeautiful

founded 1 year ago

MODERATORS

[email protected]

165

FICO Scores In the US (sh.itjust.works)

submitted 1 month ago* (last edited 1 month ago) by [email protected] to c/[email protected]

26 comments fedilink hide all child comments

The image is from a Washington Post article which took the data from an interesting research paper titled Who Pays For Your Rewards? Redistribution in the Credit Card Market.

The research paper is a good read. (A free PDF of the whole paper is available at the link.) It examines how the use of rewards credit cards results in a massive wealth transfer from low-credit-score customers to high-credit-score customers:

We estimate an aggregate annual redistribution of $15 billion from less to more educated, poorer to richer, and high to low minority areas, widening existing disparities.

The Washington Post article attempts to frame the clear north-south split as a result of healthcare issues in the south. That explanation seems too narrow to me. This map looks too similar to maps of poverty and education, and we know health correlates strongly with both of those issues.

Edit to fix a sentence fragment. Sorry; it was late and I was tired.

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 25 points 1 month ago (2 children)

Maybe they're more color-blind friendly than the typical red-green scale? Idk, it's just a guess as to why these colors were chosen.

[–] [email protected] 44 points 1 month ago (2 children)

As a red green colourblind person this map looks awesome and the contrast is excellent

[–] moncharleskey 7 points 1 month ago

Agreed!

[–] [email protected] -1 points 1 month ago (1 children)

How about red and blue, would that look good to you? Because I make lots of data graphs and I often go for red and blue. Sometimes red and green I must admit. But mostly red and blue when using a two color scale.

[–] [email protected] 3 points 1 month ago* (last edited 1 month ago) (1 children)

So most of the time I struggle to identify purple as a colour that is distinct from blue. I think the biggest thing to help anyone see it would be the contrast in values, ie use a light bright red and a deep dark blue as the extreme values so that even someone with monochrome vision could see the difference. This is all just a guess though, when I struggle the most is with dark and desaturated colours where there just isn't a ton of information. With bright colours in good lighting telling purple from blue or bright green from yellow gets a lot easier.

Eta: there are a bunch of colour blind filters you can do on the computer, you could run your images through those to see what looks best

[–] [email protected] 1 points 1 month ago (1 children)

Thanks for your insights! I am not aware of people having monochrome vision though, is that a thing? I use a single-color scale when that is appropriate. But use blue and red often for positive and negative values respectively. No purple. Just shades of blue and shades of red.

[–] [email protected] 2 points 1 month ago

It's very rare.

[–] [email protected] 2 points 1 month ago (1 children)

For fully color blind people I wished they could just do black to white with shades of gray in between.

[–] [email protected] 1 points 1 month ago (1 children)

There are colour scales that combine colours and intensities consistently, so that if you discard (or can't percieve) colour information, you still get a nice black to white scale. For a moment, I though the map used cviridis scale, which has this property and is designed to look as similar as possible to people with various variants of colour blindness. But then I realised that the scale used here has the brightest point in the middle, not on one side.

[–] [email protected] 1 points 1 month ago (1 children)

It doesn’t work well for someone with achromatopsia.

[–] [email protected] 1 points 1 month ago

Cviridis or whatever they used here? Cviridis (and other scales constructed with the same philosophy) does.