Janelle Shane applied her know-how with artificial intelligence to generate new types of pies that the world has never seen:

People wonder about what it would be like if a super-intelligent AI decided to place all of humanity in a realistic simulation. I wonder what it would be like if the simulation were built by today’s AI instead – whose computing power is somewhere around the level of an earthworm’s.

Specifically, what would the pies be like?

Mmmm, pie with cassette tapes.

In this video, Dominic Walliman attempts to illustrate and explain all of the known things in the universe.

[arve url=”https://youtu.be/uniGQrGLEoI” /]

There’s also a poster version.

Every month I collect new visualization tools, datasets, and resources. Here is the good stuff for November.

The oceans are deep. But how deep and what’s down there? Neal Agarwal provides this piece, The Deep Sea, that scales the depths of the ocean to your browser window. Scroll, scroll, and then scroll some more to see what sea life (and other things) reside at various depths.

Agarwal’s Size of Space piece from last month explores the size of space in a similar vein. It’s equally fun.

This is the internet I signed up for.

High air pollution can lead to serious health risks, but you can’t usually see particulate matter floating in the air around you. So we have no base for comparison and only an abstract sense of what’s bad and okay. The New York Times tries to make the pollution more visible.

They lead with moving particles across your screen at a density that matches approximately to what the Environmental Protection Agency defines as “good” air quality. Then the number of particles increases to peak air pollution in your area this year. Then the density increases again for the really bad areas around the world.

So you get a baseline, a relatable point with geography, and then a point of perspective.

Be sure to check out the piece on your phone (only on updated iPhones?) to get the augmented reality view. Whoa.

Inpredictable, a sports analytics site by Michael Beuoy, tracks win probabilities of NBA games going back to the 1996-97 season. When a team is up by a lot, their probability of winning is high, and then flip that for the losing team. So for each game, you have a minute-by-minute time series of win probability.

Beuoy added a new feature that looks for games with similar patterns a.k.a. “Dopplegamers”.

From Google Arts & Culture:

We came together with The Business of Fashion to view their collection of 140,000 photos of runway looks from almost 4,000 fashion shows around the world. If you could attend one fashion show per day, it would take you more than 10 years to see them all. This experiment makes this library easy and fun to explore in one single visualization. By extracting the main colors of each look, we used machine learning to organize the images by color palette, resulting in an interactive experiment of four years of fashion by almost 1,000 designers.

The interactive lets you see all of the color palettes and click through to see photos that match the palette.

You can also upload an image to fetch fashions that match the color usage in the image. So in case you want to match your wardrobe to say, your dog’s fur, that’s totally doable now. Nice.

Robert Bosch likes to use the Traveling Salesman Problem to draw famous portraits with a single continuous line. Nice.

If you want to fall down a Traveling Salesman rabbit hole, be sure to check out the main pages of the site above. You’ll find code, datasets, challenges, and other re-generated art pieces.

Also, if you’re interested in doing something similar in R, Antonio Sánchez Chinchón kicked the tires a while back. [via kottke]

Joshua Rosenberg describes his one-day experience teaching R to 7th graders:

[T]he activity worked albeit, as a very gradual introduction to using R. In combination with starting with modest goals, having the right tools (R Studio Cloud, R Markdown, and a suitable data set), I think, helped to make this work. 7th-graders can (start to) use R. The goal that Alex and I have is for students to be able to analyze data that they collect (and already-collected scientific data).

Lucky kids. All I got was a scientific calculator.

From the Voyageurs Wolf Project, a map shows the travels of a lone wolf over an 11-month period. Check out the animated version for full effect.

For Quartz, Dan Kopf and Jenny Anderson on how time spent with kids changes with age:

In the very beginning, it’s all about physical care, otherwise known as the stuff that makes your arms tired. A fifth of time parents spend with kids before their first birthday is on what could be described as keep-them-alive tasks. At age 1, this falls dramatically and it becomes playtime: peek-a-boo, stack the box, dinging and singing, making art, dancing, hide and seek, jumping in puddles. The share of time spent playing with children peaks around age 1, and then is then slowly replaced by a variety of other activities, including socializing and watching TV. Overall, time spent with children declines as kids get older.

Sounds about right. Although it makes me a bit nervous for the future.

Reddit user harpalss animated hours of day light by latitude and day of year. Just let it hypnotize you. They used this formula to calculate daylight hours.

False.

Geotab made a rough estimate of the quietest route in each state, based on average traffic. The methodology:

To find the quietest road in each US state, we gathered the latest available (2015) traffic count data from the Highway Performance Monitoring System. Quietness was calculated as the annual average daily traffic (AADT, measured in # of vehicles), and routes with the lowest AADT in each state were deemed the quietest. Lengths of routes were gathered from local transport authorities in each state. The data covers Interstates, US Routes, and State Routes over 10 miles long.

I feel like they should’ve normalized by length of route, especially since they had it already. But hey, I’m always down for some peace and quiet.

Summarizing a talk by Xaquín G.V., Natalie Gerhardstein for Delano:

Among González’ takeaways were that, in order to avoid misunderstandings or bias in data visualisation, it helps to be aware of the pitfalls across the lifecycle–from collection through analysis, to the visualisation itself–and, of course, the final story the data is helping to tell. Question, for example, whether correlations being made are legitimate, be transparent and be aware of the visuals aligning with words in the story, he argues.

There are always compromises and possible mistakes upstream before the data comes out as a nicely formatted delimited text file. The more you understand about what happens upstream, the more you can do downstream.

From Tom Fishburne, the Marketoonist. Maybe a dashboard isn’t the answer you’re looking for.