Infographic design sins in meme form

Visual editor Xaquín G.V. recently used the distracted boyfriend meme to represent our attraction to novel visualization methods when a simple and visually sound method is right there at our disposal.

Then he ran with it to illustrate his professional sins as an editor for a news desk.

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Posted by in design, meme, sins

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Criticism vs. Creation

Filmmaker Kevin Smith talks about making things versus critiquing them. He’s talking about movies, but you can so easily plug in visualization. I just kept nodding yes. [via swissmiss]

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Data exploration banned

Statistician John Tukey, who coined Exploratory Data Analysis, talked a lot about using visualization to find meaning in your data. You don’t always know what you’re looking, so you explore it visually. Etyn Adar, who teaches information visualization at the University of Michigan, makes a good case for banning the phrase in his students’ project proposals.

For all the clever names he created for things (software, bit, cepstrum, quefrency) what’s up with EDA? The name is fundamentally problematic because it’s ambiguous. “Explore” can be both transitive (to seek something) and intransitive (to wander, seeking nothing in particular). Tukey’s book seems emphasize the former — it’s full of unique graphical tools to find certain patterns in the data: distribution types, differences between distributions, outliers, and many other useful statistical patterns. The problem is that students think he meant the latter.

I see this sort of thing in my suggestion box too. Data exploration with visualization is good, but when someone describes their project as an exploration tool, it often means it lacks focus or direction. Instead it looks like generic graphs that don’t answer anything particular and leave all interpretation to the reader.

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Use dual axes with care, if at all

Dual axes, where there are two value scales in a single chart, are almost never a good idea. As a reader, you should always question the source when you see a chart that uses such scales. Zan Armstrong explains with a recent example.

One of the best descriptions I’ve heard for data viz is that: when the data is different, the viz should look different and when the data is similar, the viz should look similar.

If you allow yourself to have two y-axis for the same metric, with both a different scale on each axis and a different base value, then you can make a lot of charts with the exact same data that look very different.

If there’s a direct transformation between the scales, say between metric and Imperial units, then okay, that’s fine. In almost all other cases, people use dual axes to overemphasize a relationship between two variables, and you should wonder why the maker did that.

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Data bias at every step

Lena Groeger for ProPublica describes when the designer shows up in the design, not just in the visualization part but also in collection, selection, and aggregation. Our perspective always comes to play.

The effects may be subtle, but if we pour so much of ourselves into the stories we tell, the data we gather, the visuals we design, the webpages we build, then we should take responsibility for them. And that means not just accepting the limits of our own perspective, but actively seeking out people who can bring in new ones.

It’s common to think of data and analysis as unbiased fact. Concrete. You can’t argue with numbers. However, that’s rarely the case. We analyze and visualize with preconceptions, and that drives many aspects of whatever comes next.

Analysis is a process driven by experience. Technically, this means learning new methods as you look at various data types and situations. Contextually, this means forming conclusions based on what you know about the subject matter. If there are knowledge gaps technically or contextually, you run into issues.

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Posted by in Bias, design

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Interactive visualization is still alive

Phew. Close call.

New York Times graphics editor Gregor Aisch noted during a talk that 85 percent of readers didn’t click on the buttons of a popular interactive. So Dominikus Baur pondered the usefulness of interaction. The answer was yes. It’s all about purpose.

To clarify, Aisch recently came back to the 85 percent figure.

Knowing that the majority of readers doesn’t click buttons does not mean you shouldn’t use any buttons. Knowing that many many people will ignore your tooltips doesn’t mean you shouldn’t use any tooltips.

All it means is that you should not hide important content behind interactions. If some information is crucial, don’t make the user click or hover to see it (unless you really want to). But not everything is crucial and 15% of readers isn’t nobody.

Aisch then gives a handful of good reasons for interaction in news graphics. The gist, and what I see over here on this modest site, is that most people who come to interactive graphics on the web won’t care enough to click on things. However, for the 15 percent of people who do, it’s worth the added extra effort.

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Motion design patterns

Issara Willenskomer talks in detail about the use of animation in UX design with a focus on twelve specific patterns. Different types of motion can represent different things. It’s easy to see how this applies to visualization.

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Posted by in design, motion

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Interactive or not to interactive visualization?

In a recent talk, New York Times graphics editor Gregor Aisch noted that only 10 to 15 percent of readers who visit an interactive visualization on their site actually click on anything. That’s a lot of people who don’t get everything that New York Times interactives have to offer, which begs the question: Is it worth the time and effort to make these things?

As with most design-related things, it depends on the goals and the audience of your visualization. Dominikus Baur explains in detail, drawing experiences from his own work.

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Visualization choice depends on the data and the questions

When you don’t know where to start with a dataset, try to come up with a question. It’ll point you in the direction you want to go, as this guide by Frédérik Ruys shows you. [via @maartenzam]

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Pie charts are okay

There were some ripples in the space time continuum recently about a pizza and a pie chart. It looked like a pie chart but was actually just a pizza with numbers around it. Those numbers didn’t sum to 100 percent, so there were pitch forks and burning and like I said, ripples in the space time continuum.

Here, have a look for yourself:

It’s social media filler content, so whatever. And yeah, the chart, if you want to call it that, isn’t any good. But let’s not lose sight of the big picture here, and that is that pie charts are okay sometimes.

Plus, we might not even understand how people read pie charts from a perception point of view anyways.

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