✚ Charting for Others (The Process 086)

There's a new tool-agnostic course now available for members. Check it out now. Read More

The Myth of ‘Dumbing Down’

For The Atlantic, Ian Bogost on communicating complex ideas to an audience:

One thing you learn when writing for an audience outside your expertise is that, contrary to the assumption that people might prefer the easiest answers, they are all thoughtful and curious about topics of every kind. After all, people have areas in their own lives in which they are the experts. Everyone is capable of deep understanding.

Up to a point, though: People are also busy, and they need you to help them understand why they should care. Doing that work—showing someone why a topic you know a lot about is interesting and important—is not “dumb”; it’s smart. Especially if, in the next breath, you’re also intoning about how important that knowledge is, as academics sometimes do. If information is vital to human flourishing but withheld by experts, then those experts are either overestimating its importance or hoarding it.

I struggled with this during my first year of graduate school, because it took a while to get out of my own head and imagine myself as a reader. Or, in the case of that first-year regression analysis course, I was supposed to imagine a policymaker on a tight schedule.

I would crunch numbers or whatever and write reports. My professor told me I had to do a better job explaining the meaning behind the numbers. How should a non-statistician interpret these results? It was my job as the statistician to explain.

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✚ Visualization for Analysis vs. Visualization for an Audience (The Process #59)

The visualizations are used and read differently, which requires that you approach their design differently. Read More

✚ Reddit Follow-up; Chart Like Nobody’s Looking (The Process #57)

Consider your audience. Yes. But at some point in the visualization creation process, you have to disregard all of the feature requests and design suggestions. Read More

Visualization research for non-researchers

Reading visualization research papers can often feel like a slog. As a necessity, there’s usually a lot of jargon, references to William Cleveland and Robert McGill, and sometimes perception studies that lack a bit of rigor. So for practitioners or people generally interested in data communication, worthwhile research falls into a “read later” folder never to be seen again.

Multiple Views, started by visualization researchers Jessica Hullman, Danielle Szafir, Robert Kosara, and Enrico Bertini, aims to explain the findings and the studies to a more general audience. (The UW Interactive Data Lab’s feed comes to mind.) Maybe the “read later” becomes read.

I’m looking forward to learning more. These projects have a tendency to start with a lot of energy and then fizzle out, so I’m hoping we can nudge this a bit to urge them on. Follow along here.

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✚ Make It Mean Something or It Didn’t Happen

Visualization as template-filling content is lazy visualization that no one draws benefit from. Give people a reason to care. Read More

Visualization for an audience

Jonathan Corum, the Science graphics editor at The New York Times, talks about his experiences communicating scientific research to the public. Much of visualization design is about figuring out the audience and making graphics for that audience, so Corum uses a lot of examples that start from technical research papers and finish with a more focused result.

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Using an audience’s own data to highlight both play and security

This is great. Daniel Goddemeyer and Dominikus Baur made Data Futures, which collects multiple choice answers from audience members and then allows the speaker to interact and visualize the results on stage, as well as highlight audience members.

I’m imagining this project restructured in a college statistics course with several hundred unwitting students. Seems like a great learning opportunity.

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