John Tukey wrote, “The greatest value of a picture is when it forces us to notice what we never expected to see.” Not everyone wants to see though.

FastCharts is the public version of the Financial Times’ in-house solution for making charts, uh, fast. Load some data. Get the chart fast. FastCharts. Kachow.

To connect servers around the world, there are actual cables that run under the ocean. The New York Times mapped current and future cables, with a focus on the ones owned by Amazon, Facebook, Google, and Microsoft. “Content providers like Microsoft, Google, Facebook and Amazon now own or lease more than half of the undersea bandwidth.” Sure. Totally fine.

TwoTone, by Datavized and supported by the Google News Initiative, is a straightforward tool to sonify a dataset. Upload your data, select the metric, speed, and instrument, and you get a tune output.

If you thought visualization was tricky perceptually, then you’re in for a treat with sonification. The two most useful examples I can think of off-hand were event-based, so maybe start with something like that.

I’m thoroughly enjoying the work coming from graphic designer Scott Reinhard as of late. He combines modern techniques with vintage feels. In his most recent, he provides a “look at what the lower 48 states of the United States would look like if it were flipped inside out.” Grab the print.

While we’re on the subject of distributions, Fathom used a collection of beeswarm charts to show documents about the Mueller investigation over time and connections between individuals. It’s called Porfiry. Filled circles represent documents that represent connections, and circle size represents the number of documents.

Right in my wheelhouse, Russell Goldenberg and Amber Thomas for The Pudding looked at where top high school basketball recruits end up in the NBA (if they’re drafted at all). I like how you get the distributions at each level and the path of each player. The distributions build using animation, which is something I’ve been interested in as of late.

Histograms for a general audience can be a challenge. I’m curious if building up the distributions over time can be helpful.

Timo Aho and Pekka Niittyvirta used sensors, LED lights, and timers to display future water lines:

By use of sensors, the installation interacts with the rising tidal changes; activating on high tide. The work provides a visual reference of future sea level rise.

The installation explores the catastrophic impact of our relationship with nature and its long term effects. The work provokes a dialogue on how the rising sea levels will affect coastal areas, its inhabitants and land usage in the future.

Love that a single line of light can represent so much.

John Snow, who often gets the credit for showing the geographical patterns of a cholera outbreak in London in 1854, wasn’t the only one visualizing data at the time. James Cheshire put together a collection of other charts made at the time.

[I]t wasn’t just Snow producing innovative maps and charts to support his cause. Snow was part of an arms race to get the best data communicated by the most compelling maps/ charts, to evidence his side of the debate against his contemporaries – people like William Farr who was also a master data visualiser.

Here’s a fun thing to try in R. Jean Fan posted some code snippets where you can load an image file and the result uses a hatching technique to recreate the image with shapes.

See also: Using the traveling salesman problem to re-sketch images.

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.