Colin Morris culled common misspellings on Reddit and made the data available on GitHub. For The Pudding, Russell Goldenberg and Matt Daniels took it a step further so that you too can see how bad you are at spelling celebrity names.

Throughout the month I collect new tools for data and visualization and additional resources on designing data graphics. Here’s the new stuff for February.

Here’s a fun spin on the name analysis genre by Mary Zam. She compared the distribution of names used in movies against names used in real life:

Thousands of babies are called Sophia or Abigail, Mason or Dylan every year. But writers do not rush to call the main characters with such names. According to the statistic, almost all of the top names are much less common in the film industry rather than in real life. Seems that they just don’t want to use too ordinary names in their scenarios.

However, there are always some exceptions from this rule such as Jack (up to x3), Maria (x3), Peter (x2) or Sarah (x4.5). But such names makes only about 5-10% of top each decade. On the other hand, there is a bunch of “cinematic” names such as Simon (20 times more often in movies & tv than in real life) and Kate (20 times more often) which you won’t find in the real life top lists.

Rosenfeld, et al. from Stanford University ran a survey in 2009 for a study on How Couples Meet and Stay Together. Dan Kopf and Youyou Zhou for Quartz used this dataset to estimate the probability that you will break up with your partner, given a few bits of information about your current relationship.

The Stanford data page says a 2017 release is on the way. I’m curious how, if anything, has changed in relationships between 2009 and now.

Speaking of 3-D usage on maps, here’s a map of bus routes in Singapore stacked one on top of the other. I’m not sure it’s especially useful to find individual routes as intended, but the overall distribution of routes seems like it might be interesting to someone familiar with the area. Or, maybe it’s world’s greatest roller coaster.

Based on commuting data from the Census Bureau, researchers Matthew Hall, John Iceland, and Youngmin Yi tracked segregation during the day and night. Alvin Chang for Vox mapped their results:

They found that when white people go to work, they are around only slightly more people of color than when they’re in their home neighborhoods. But for everyone else, going to work means being exposed to many more white people — and far fewer people of their own race.

Browse the map to see the results for your own city.

It appears that the 3-D map is making a small comeback (Thanks, Matt Daniels), to the delight of some and perhaps to the chagrin of others. I’m ready for it. I wonder when data graphic résumés are coming.

[arve url=”https://www.youtube.com/watch?v=F9H9LwGmc-0″ /]

For SB Nation, Jon Bois takes a statistical deep dive in the search for the saddest punt in football. It’s an hour long. It’s a surprisingly fun watch. At the very least, even if you don’t like football, you can glean some analysis and storytelling lessons from it.

“How do I make my data come alive? I want it to sing. I want it to dance.” Here are some ways to achieve that.

Leslie Roberts uses paint to encode text as colors and geometry:

My paintings translate words into visual language. These panels with texts and accompanying abstract structures might be called illuminated manuscripts of the everyday.

Written in these recent paintings are collections of ambient found language: fragments from street signs, junk mail, end user licensing agreements, email, labels, subway ads, receipts, newspapers, and instruction manuals. Transcripts of fine print from the relentless flow of information surrounding us are used to derive a personal abstract vernacular.

The United States is doing pretty poorly in reducing emissions. For The New York Times, Brad Plumer and Blacki Migloiozzi, show the current status and what could happen if the U.S. adopted more drastic plans already in place around the world.

The moving particles underneath the trend line is a nice touch to bring the abstract closer to what the data represents. Contrast this piece with Plumer’s piece from a couple of years ago to see the shift in focus.

Lucas Kwan Peterson for the Los Angeles Times ranked fast food french fries. That is all.

After a most unforgiving dust storm on Mars, NASA ended the 14-year mission with the Opportunity rover. It was originally only planned to last 90 days. Jonathan Corum, for The New York Times, mapped the little guy’s journey over the years.

Figure out the useful bits and get rid of everything else.

For The Upshot, Kevin Quealy continues on his path looking at the words used by Donald Trump. This time Quealy examines descriptions of the wall and who will pay for it, pre- and post-inauguration.

Gustavo Turner for Logic on his experiences covering the porn industry and the introduction of data into the stream:

Over the course of these experiences, I learned about a major new force reshaping the industry: data. That day on set, the director’s instructions came directly from the production company, which decided on the topic and vetted the script. And the company based its creative direction on specific fantasies proposed by paying customers on an online forum that it owned. […]

In the pre-internet days, a producer might notice that a particular kind of porn movie sold well, and then try to make more like it. Today, a corporate porn conglomerate can analyze a continuous stream of information from online viewers, who supply feedback in the form of comments, and leave behind a data trail as they travel around porn sites.

What a time to be alive.