After a chat with his color deficient friends about how Vincent van Gogh's paintings seem to appeal to all eyes, Kazunori Asada used visual filters to see how the paintings looked to the colorblind. The experiment produced some interesting results and musings:

Was van Gogh partially color vision deficiency (anomalous trichromat)? Perhaps using a strong color vision deficiency (dichromat) simulation was the wrong approach. How about carrying out the simulation by removing the middle portion of normal color vision, maybe then I could see van Gogh’s pictures in a better light?

The color choices for van Gogh's popular paintings seem less out there with the filters. The greens in the sky of Starry Night, for example turn to yellows.

A colorblind van Gogh though? Probably not. Either way, don't forget to pick your colors wisely. Asada has an easy-to-use tool to see what your own images look like to others.

[Asada's memorandum]

Amanda Cox, the chart marker, on how her work and goals have changed over the years:

At one point — I call it my impressionist phase — I was really interested in making things abstract but interesting and beautiful. And then I had a "curves are fun" phase for a while where I was really into curved things. And then I had an "intentional simplicity" phase for a while, like, how stripped down can you make something and have it still be interesting? I don’t know what my current phase is, but it's kind of an "aspirational reporting" phase. I'm not that great of a reporter yet, but I'm thinking a lot about how we can stop using the same information that's already on the Internet and just remix that. I want to start working with more, deeper information, information that's harder to surface.

This is coming from someone who has won an international award for being the best. So much to learn, I have.

[Substratum]

Obvious statement: there are infographics that are horribly made. Some are way too big for the information conveyed and others are useless because the creator had no idea what he was doing. Some infographics are both. Here's the thing though. There's plenty of suck of everything online, and yet somehow we manage to find the good resources, applications, and sources of endless entertainment.

A couple of years ago, infographics spiked and even what seems like subpar work now, passed as amusing at the least. It's like the time on the Web when it was pure awesome to have a site decked out with animated GIFs, blinking backgrounds, and delightful MIDIs that were a treat for the ears. Sites like this still exist — some just as an archive of the past and others by someone learning HTML with a book they checked out from the library — but you'd never mistake one of those sites as an example of great Web or interaction design.

Or how about blogs? It's as easy as ever to start one and you can write whatever you want. Some are bad and others are good. That's how it works when people make things.

Think of infographics/visualization/etc as a medium instead of a method and it should be much easier to unruffle your feathers. There are textbooks and there are novels. There are documentaries and there are slapstick comedies.

So I don't think a flood of low-quality infographics is going to obscure the work that is actually worth looking at. If anything, it's going to be easier to find the standouts.

I've seen this through my inbox already. Readers used to send me a lot of work that well, wasn't the best, but they thought it was interesting. That doesn't happen so much anymore though, because I think we've learned what was made as commercial linkbait and what deserves our attention. Sometimes they are one and the same, which is fine by me. Even if a piece looks beginner, it's easy to tell when a person is interested in the data or is eager to learn how to make things. I'm all for that and am more than happy to offer my two cents when someone asks me. After all, I'm still learning, too and I'll be producing plenty of schtuff in the process.

People can (or will learn to) spot the B.S., and the bad stuff doesn't get shared and doesn't spread.

When the low-quality infographics stop is when we should be most concerned. It means people have lost interest. That's not going to happen any time soon though.

When we say on the phone "I'll be there in half an hour" it’s quite likely we'll arrive sometime in the next 25 to 35 minutes. But for the context of meeting up with a friend "half an hour" will do. If you said "see you in 27 minutes" that would raise a laugh being an odd level of precision for the given context. The same ideas apply to numbers in journalism.

Important in both accurate representation of data and readability.

Written by Amy Balliett of Killer Infographics, the post in question is basically tips for how to create linkbait that doesn't work. Or at least I hope it doesn't.

Let's take it from the top.

Infographics are visual representations of information, or "data viz" as the cool kids call it these days. The term "data viz" comes from "data visualization," which implies that sets of data will be displayed in a unique way that can be seen, rather than read.

I'm not going to get into the semantics. That's for people who are smarter (and cooler) than me, but the obsession with a visually unique result is overdone. Sure, you want your graphic to be compelling, but that comes from the data or the information. You're just trying to make crap look like cake if you do it any other way: it looks good from far away, but as soon as you take a taste, you realize you have poop in your mouth.

Next up, which refers to the graphic above:

What’s wrong with this infographic? It breaks the first rule right out of the gate. When you have an opportunity to display information visually, take it. Here, the tweets per second could have at least been shown in a bar graph.

What? We all know the first rule of infographic design is to not talk about infographic design. I guess that's implied. After that though, the first thing wrong is that there are numbers? What about that gauge on the left without any labels? What does it mean?

Most run-of-the-mill infographics take a few data points and make it look like a lot with big things. This is both a reflection of the data on hand and the creator's data comprehension.

It gets worse.

Two alternatives to the above are offered. Do you choose the utterly boring, horribly regular graph A or the awesomely, cool, and round graph B?

Balliett's suggestion was to use the speedometer, basically because it looks awesomer. Something else comes to mind.

I hope you didn't answer graph A. If so, you're wrong.

If you answered Graph B, you’re catching on. Of course, not all data lends itself to creative and unique graphs. Graph A might work very well if the rest of the infographic shared a similar aesthetic. Sometimes you just have to bite the bullet and produce a traditional bar graph or pie chart; nevertheless, always consider ways to dress it up [...]

It's that obsession with unique again. The problem isn't the bar chart. It's that there's only five data points occupying a lot of screen space. Plus, the speedometer makes tweet rate look way more than it really is, because the method uses arc length as its visual cue.

A more useful graphic would provide more context to the events that these numbers describe. What made the Women's World Cup Final so much more exciting than Super Bowl 2011? Which final was it? Was it really more exciting? When were these measurements taken? Are they the peaks of each event or is it a mix? Were there more tweets because there were more Twitter users at the time? Was it because the time of the day each thing happened? Tell people something — anything — more about the data.

The second half of the post covers mostly general design tips such as color and focus, so it's less rough, but the examples aren't any better.

Bottom line: when it comes to information graphics, it should always be data and information first, and then you design around that. For that to happen, you have to learn more about data — how to work with it, where it comes from, and what it represents in the real world.

Now how about some real dos of data and information design? Leave a DO in the comments below by the end of this Friday, and a signed copy of Visualize This goes to a randomly picked doer.

During all the meetings, there were recurring themes about what visualization is and what it is used for. Some people really got it, but others were new to the subject, and we ran into a few misconceptions that I think are worth repeating.

Here we go, in no particular order.

Visualization is for making data flashy

This is probably the most common one. It's easy to look at a lot of the best visualization projects and want your data to look and feel the same way. So people ask, "I have such and such data. Is there a visualization technique that I can use to make it look cooler?"

Well, maybe. Not if you only have five data points though. You can spend a lot of time with icons or fancy print, but the graphics are interesting because the data that the visuals represent is interesting.

For example, I mapped the growth of Walmart a while back (It's amazing how much mileage I get out of this graphic.), and people seem to like it because of the organic growth pattern. It starts in one area and spreads outwards like a virus.

Okay, compared to Toby Segaran's original, I did add some interactive flourishes, but even without, the growth pattern is what makes the animation interesting.

For example, here's a map with the same style as my Walmart one, but it shows the spread of Target. It's not nearly as fun to watch, because Target took a more opportunistic approach of expansion. Locations pop up kind of randomly at times. It's mostly interesting as a contrast to the Walmart map.

It should always be data first. Certain graphics get eyeballs because they show something that wouldn't be seen in a table.

Software does everything

There are a lot of options for visualization, and the "best" one will change depending on who you ask.

Personally, I use a lot of R and have a lot of fun in Illustrator. More recently, I've been working with JavaScript. Flexibility is a huge plus for me, and I like to have full control over how my graphics look and how the interactive ones work. Most of what I do though is to present data to a wider audience. If I were an analyst tasked with digging through a large dataset, I might take a different route before I make something custom.

My main point is that there is no one piece of software that will do everything for you.

Some software is good for analysis, some is good for specific types of analysis, and some is good for storytelling.

The more information in a single graphic, the better

A misstep a lot of people take when they're trying to advance "beyond Excel" is to layer too much information on top of their basic graphic. I'm all for providing context and highlighting interesting spots in your data, but at some point it's better to split your one chart into two or three charts.

Some people try to be clever by using multiple axes on a single plot or multiple visual cues in a single chart to save space. Again, this works sometimes. A lot of the time it doesn't. Oftentimes, simple and clear is better than clever and compact.

My favorite test is to show a graphic to someone who doesn't know the data and isn't a visualization expert and see what they take away from the visual.

Visualization is too biased to be useful

There's a certain amount of subjectivity that goes into any visualization as you choose what data to show and how to show it. By focusing on one part of the data, you might inadvertently obscure another. However, if you're careful, get to know the data that you're dealing with, and stay true to what's there, then it should be easier to overcome bias.

After all, statistics is somewhat subjective, too. You choose what you analyze, what methods to use, and pick what to point out in reports.

News organizations, for example, have to do this all the time. They get a dataset, decide what story they want to tell (or find what story the data has to tell). Browse through graphics by The New York Times, and you can see how you can add a layer of information that objectively describes what the data is about.

It has to be exact

If you're using visualization to show the exact value of every single data point, along with every standard error, you're probably using it wrong. Accuracy is important. Yes. But visualization is less about the individual values and more about the distribution of them over time and space. You're looking for (or showing) patterns. You're comparing and contrasting.

If all you care about are individual data points, you might as well put it in a table.

Are there other common misconceptions that you can think of?

Have you ever been using an application (not just for data) and wished it could do something else? If you want a new feature, you have to wait for someone else to develop it, but if you program, you could implement your own features.

With a little bit of coding know-how, you gain more flexibility — and a little goes a long way.

I think a lot of people avoid programming, because it seems scary and they have no idea where to start. I felt the same way when I first started learning code in college. I had no idea what I was doing, and it was actually one of the reasons I wanted to get away from my engineering major and jump into statistics. That programming background came in handy though and grew more useful as I played with more data. Now it's hard for me to imagine doing data without having that flexible tool in my back pocket.

Just think of learning code as learning a new language (because, that's basically what you're doing). When you start learning a new language, you're not writing essays the first day. You learn punctuation, grammar, spelling, and other basics, and then you build up to paragraphs, essays, or even books. Same thing with code. You learn the syntax and logic, and then apply what you learn to bigger problems.

It might feel a little slow at first, but another plus is that you can reuse code, meaning you could end up saving time in the long run.

Again, this is not to say you should abandon the other tools and use code exclusively. Rather, it's another tool in your box — a powerful one that increases its utility the more it is used.

In the end, the more ways you can explore, analyze, and present your data the less likely you are to get stuck and the more likely you'll be able to figure out what your data has to say.

The artists are looking for an emotional “wow” moment; our goal is the “ah ha” moment when a user learns something that can lead to productive action. The question that we so often ask: “what can you do about it?” wasn’t a top priority within the Eyeo crowd.

One group is telling a specific story and the other is searching for one. That's not to say that one way or the other is bad, however. Each group can benefit from the other:

Ultimately this art vs. practice dichotomy is natural and healthy. In our work, we are inspired by the fun and energy expressed in artistic visualizations. Data visualization is a tool that can and should be used differently depending on the purpose and the audience. The skill in using the tool can be appreciated equally across these different contexts.

There'd be a lot less ruffled feathers if we could all remember that.

[Juice Analytics]

[UX Magazine | Thanks, Elise]

In regards to a performance chart posted by Netflix, Andy Baio, who along with around 7 percent of men, is colorblind, explains why it's so hard to read the chart. "When doing the right thing is this easy, it's really disturbing when it's dismissed as a waste of time."

[Waxy]