The annual Malofiej Awards is the big one for infographics in the news. The 25th one just passed, and you can browse all the winners here. There’s a lot of great work that you should associate with infographics — and not the spammy stuff that fills my inbox.

Plug in any dataset into a magic box and it spits out a lovely visualization you can show all of your co-workers, friends, and family. That’s the promise of a lot of startups, but it doesn’t quite work that way. Ian Johnson explains by comparing visualization the medium to other forms of communication.

I want to take a deeper look at why this pursuit of automation is misguided, and in the process hope to point out potentially more fruitful paths. I intend to do this by looking at how other communication mediums have come about via technology, what the authorship tools look like and how they evolved. We will start with the most recent medium and go back in time, getting deeper into the essence of augmenting human communication with technology.

Some (many?) might argue that automated visualization is a worthwhile pursuit. And I would agree that some parts of visualization certainly should be automatic, such as standard chart types and recurring geometries. Pieces of visualization, such as annotation and axis construction can be automatic. There are plenty of tools to make our lives easier.

But full on automation where insight fountains out from any dataset is farfetched at this point, because this requires automatic analysis. Analysis is context-specific and requires more than boilerplate statistics. The most interesting visualization is context-specific.

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.
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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.
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Mapping at the precinct level of detail is tough because there isn’t a central place where all the geography files reside. If you want a national precinct map, there’s going to be a lot of manual labor involved, and so that’s what Ryne Rohla did.

After spending most of my spare time in 2015 working on a global religion map, the 2016 Presidential Primaries rolled around, and I decided to go for it: I would do everything in my power to create a national precinct map. I didn’t have a team of researchers. I didn’t have aides. I didn’t have much extra money. I didn’t have connections. But for some reason, I thought I could do it anyway.

Hundreds of emails and phone calls and months of work later, here’s what I came up with

Interactive version here.

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.
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For April Fool’s Day, Reddit ran a subreddit, r/place, that let users edit pixels in a 1,000 by 1,000 blank space for 72 hours. Users could only edit one pixel every ten minutes, which forced patience and community effort. This is the time-lapse of the effort.

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.

Uber uses psychology and video game mechanics to encourage drivers to work longer and drive in certain areas. Noam Scheiber for The New York Times details the gray area that Uber resides in since drivers aren’t official employees.

Uber exists in a kind of legal and ethical purgatory, however. Because its drivers are independent contractors, they lack most of the protections associated with employment. By mastering their workers’ mental circuitry, Uber and the like may be taking the economy back toward a pre-New Deal era when businesses had enormous power over workers and few checks on their ability to exploit it.

This probably doesn’t come as a surprise to most, but it’s interesting to hear about it in such detail. It’s also fun to play with the simulations by Jon Huang, which help you better understand the strategies Uber use.

Emily Beam highlights confirmation bias in articles recently suggesting that more millennial men pine for the days when men worked and women stayed at home, based on results from the General Social Survey.

The GSS surveys are pretty small – about 2,000-3,000 per wave – so once you split by sample, and then split by age, and then exclude the older millennials (age 26-34) who don’t show any negative trend in gender equality, you’re left with cells of about 60-100 men ages 18-25 per wave. Standard errors on any given year are 6-8 percent.

Mind your data.

What does a pianist look at while playing? Put a pair of eye tracking glasses on a professional while he plays. Then compare to a student.

Jumpstarted by Elijah Meeks asking why visualization people are leaving the field for less visually-centric industry jobs, there’s been ample discussion about data visualization’s role in companies.

This naturally leaks over to the ongoing discussion about what visualization is and should be. Moritz Stefaner, who’s been at it since before I even knew what visualization really was, chimed in with his experiences and what he’s seen as a freelancer.

Yet, as I argued earlier already, I don’t think we gain much from overemphasizing the (supposedly) fundamental differences between “serious/functional” and “aesthetic/entertaining” data visualizations, or, conversely, diminishing Excel dataviz work as “not really data visualization”.

I am thinking back to the time when it was fashionable to “draw lines in the sand” or to attack designers on live TV. The harsh, narrow-minded criticism that novel designs and approaches faced for a while did not always lead to better results, but, in contrast, scared talented folks away from the community. I am really quite happy that, by now, we have a data visualization community that understands the many purposes of data visualization beyond scientific analysis.

Many purposes. That’s the key here.

Visualization can be a tool or a skill set that aids in the overarching goal of understanding data, whether it be quantitatively, qualitatively, or emotionally. Maybe you use the tools. Maybe you make the tools. Maybe you use the tools that you make. You can go as far as you want with any of these routes, and the one you choose brings various job titles.

I’m completely detached from industry. (I mean, I’m one guy running a site from a home office, so I’m detached from a lot of things.) But in my experience, visualization can and should be a stand-alone profession. It’s not a big conceptual jump — if you go far enough — to see how the person who knows how to make charts can become the chart-maker.

Trevor Martin for FiveThirtyEight used latent semantic analysis to do math with subreddits, specifically r/The_Donald.

We’ve adapted a technique that’s used in machine learning research — called latent semantic analysis — to characterize 50,323 active subreddits based on 1.4 billion comments posted from Jan. 1, 2015, to Dec. 31, 2016, in a way that allows us to quantify how similar in essence one subreddit is to another. At its heart, the analysis is based on commenter overlap: Two subreddits are deemed more similar if many commenters have posted often to both. This also makes it possible to do what we call “subreddit algebra”: adding one subreddit to another and seeing if the result resembles some third subreddit, or subtracting out a component of one subreddit’s character and seeing what’s left.

Hm.

Families often move out of the city to the suburbs for more affordable housing (or more space) and better schools for the kids. Quoctrung Bui and Conor Dougherty for The Upshot plot these two things, average price per square foot and school district performance, to compare against the respective city.
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Commuting sucks. Here’s a straightforward map to compare how much or less your commute sucks compared to others in your area. Enter your ZIP code, and you get a simple comparison to the average, based on 2006-2011 U.S. Census American Community Survey.

Using multiple polls as their source data, FiveThirtyEight is tracking approval and disapproval ratings for Donald Trump. The page leads with overall estimates with a couple of bands of uncertainty for projections into the next few months. I still wonder what proportion of readers understand the ranges, but I’m glad they’re there.

Scroll down the page to see how Trump’s ratings compare to past presidents. You can switch between approval, disapproval, and net approval, along with options to change the time span — which is nice for a page that updates for the next four years.

Looking for some data to play with? James P. Curley compiled Scrabble data using computer-played games in Quackle Scrabble. Check out his summary analysis or grab the data for yourself in the R package scrabblr.

Data is a great vehicle for arguments, but the (not just visual) perception can change completely depending how a reader feels. Cognitive neuroscientist Tali Sharot talks facts and emotions on Hidden Brain.

The example at the end is interesting. Tell a person a joke when they’re sad, and they probably won’t think the joke is funny. Make the person happy first, and it’s more likely they’ll see the joke from your point of view. How does this transfer to the communication of data? [via Kim Rees]

Nicky Case, whose projects to simulate segregation and systems with emoji you might recognize, likes to think in systems. Piece together steps and objects, and let them interact with each other using various probabilities and weights. Simulate. See what happens.

Case’s newest project, LOOPY, is a tool to build your own systems. No programming required. Just click-and-drag things and press play.

There are a lot of meteoroids circling around in space. Ian Webster visualized all of the major ones at once.

Meteor showers on Earth are caused by streams of meteoroids hitting our atmosphere. These meteoroids are sand- and pebble-sized bits of rock that were once released from their parent comet. Some comets are no longer active and are now called asteroids.

This visualization shows these meteoroid streams orbiting the Sun, some stretching to the outer regions of the solar system.

Pan and zoom, filter by time, select specific showers, and watch from multiple vantage points. Nice. (It was sluggish in Safari but was smooth in Chrome.)

See also Webster’s Asterank.