✚ Visualization for One

Welcome to issue #202 of The Process, the newsletter for FlowingData members that looks closer at how the charts get made. I’m Nathan Yau, and I’m visualizing data for one person and hoping for the best.

Become a member for access to this — plus tutorials, courses, and guides.

✚ Debating About Visualization – The Process 172

Welcome to issue #172 of The Process, the newsletter for FlowingData members about how the charts get made. I’m Nathan Yau, and this week I was thinking about… spirals? What.

I was going to grace you with my 10,000-word thought piece on the nature of spiral charts and their relationship to life itself, but I’m placing it on the back burner for now. Sorry.

Become a member for access to this — plus tutorials, courses, and guides.

✚ Connecting Data to Practicality – The Process 166

Welcome to issue #166 of The Process, the newsletter for FlowingData members that looks closer at how the charts get made. I’m Nathan Yau, and this week my attention span is hanging on to dear life as we head towards Thanksgiving here in the U.S. I’m also thinking about the often missed next step to making the numbers more than just numbers.

Become a member for access to this — plus tutorials, courses, and guides.

✚ I Want to Visualize Aspects of the Data – The Process 162

Welcome to issue #162 of The Process, the newsletter for FlowingData members that looks closer at how the charts get made. I’m Nathan Yau, and this week I’m less interested in the data and more interested in aspects of it.

Become a member for access to this — plus tutorials, courses, and guides.

✚ 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.

Tags: ,

✚ 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.

Tags: ,

✚ 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