Sharing the same collaborative principles as OpenStreetMap, a wiki-based map for the real world, OpenGeofiction is an experiment in mapping an imaginary world.

Opengeofiction is a collaborative platform for the creation of fictional maps.

Opengeofiction is based on the Openstreetmap software platform. This implies that all map editors and other tools suitable for Openstreetmap can be applied to Opengeofiction as well.

The fictional world of Opengeofiction is thought to be in modern times. So it doesn’t have orcs or elves, but rather power plants, motorways and housing projects. But also picturesque old towns, beautiful national parks and lonely beaches.

As you zoom in to the map, you can see many details, from roads, bodies of water, to greenery, have already been added. Some areas look like densely inhabited cities connected by highway, whereas others are miles of forest and nature.

Browse long enough and you forget you’re looking at a fake world.

New Scientist quickly covers three theories of space and time in an informational video.

— When you deal with data, you can think like a statistician, even if you don’t know the math (although it will certainly help a lot). Jonathan Stray brings up fine points to draw conclusions from data, as does Jacob Harris in a detailed case study on distrusting your data.

— Learning data science still seems like a fuzzy, abstract idea. Trey Causey offers advice on getting started with the bubbling field.

— Is college still worth it? Yes.

— Coding isn’t easy. If it were, everyone would do it.

— R gotchas.

I’m not sure how I just came across this now, but the Truth Facts comic by New Creations is right up my alley. It’s essentially a collection of charts and illustrations that finds humor in mundane, everyday stuff. I’ve felt the above all too well.

I liked this sports bar name generator too.

Lots more in the archive.

For a graduate project, Michael Barry and Brian Card explored the Boston subway system through a set of annotated interactives that show train routes, usage, and scheduling.

Through publicly available data, we have the tools to understand the subway system better than we ever have before. We have seen how the system operates on a daily basis, how people use the system, how that affects the trains and also how this ties back to your daily commute. To see a real-time version of this data, check out mbta.meteor.com for up-to-the-minute congestion and delay information.

I like how they keep a subway map in view throughout. It helps you efficiently figure out what each chart means and is a good common factor as you move through the facets.

Drew Roos made a thing that lets you move the poles of the Mercator projection to anywhere in the world.

As you probably know, map projections all have their pros and cons since there are challenges that come with transforming a globe onto a two-dimensional surface. The Mercator projection, one of the most well-known, distorts as you approach the poles. The scale approaches infinity actually, which is why we’re used to seeing a Greenland that is bigger than Africa. (It’s not.)

Above shows the pole shifted to Washington, D.C. Trippy.

Edyn, a new project on Kickstarter, aims to make gardening easier by tracking water and soil conditions and automatically adjusting water schedules based on the data.

Edyn is there to take the guesswork out of gardening. Inserted in the soil, the Edyn Garden Sensor gathers and analyzes data about changing weather and soil conditions. The Edyn App displays this data as a real-time snapshot of your garden, and pushes alerts and suggestions to maximize plant health. A separate component, the Edyn Water Valve, uses the data collected by the sensor to smartly control your existing watering system, watering your plants only when needed.

I want this for lawns.

There are a lot of fake, spammy accounts on Twitter that come in a variety of forms. Some tweet links to junk, some serve as retweet and faving bots, and others exist purely to boost follower counts. Gilad Lotan, a data scientist at betaworks, was curious about that last type, so he bought 4,000 followers for five bucks and looked closer at his new found friends.

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Jeremy Ashkenas and Alicia Parlapiano for The Upshot just plopped this interactive sucker on to the web. Each line shows change in job count for an industry. Horizontally, they’re organized by average salary, and vertically, they’re organized by relative change since the end of the recession. Green represents growth and red represents decline.

My initial reaction was along the lines of what-the-heck, but then you see the axes and get the mouseover actions for details. Scroll down, and you get highlighted subsets. By the end, you’ve learned something.

The gross domestic product for the United Kingdom rose by 5% seemingly overnight, after spending on cocaine and prostitution was (roughly) accounted for. Naturally there’s been a bit of fuss over the new estimate. Tim Harford explains why the new count isn’t such a travesty.

We need to understand three things about gross domestic product statistics. First, GDP itself is ineffable — an attempt to synthesise, for practical purposes, something that defies description. Second, the national accounts are not designed to give a round of applause to the good stuff and a loud raspberry to the bad stuff. They are supposed to measure economic transactions. And, third, anyone who thinks politicians try to maximise GDP has not been paying much attention to politicians.

After you read that, it’s also worth listening to the Planet Money podcast on GDP from a few months back. Fuzzy estimate.

Football players are getting bigger. Noah Veltman, a developer for the WNYC Data News team, shows by how much through an animated heatmap. Scrub the slider back and forth quickly for maximum effect.

In the beginning, the league clustered in the bottom left. No one was over 300 pounds, and everyone was 6 feet and 4 inches tall or shorter. These days, player height weight are spread out more and shifted towards the top right.

Justin Palmer and his family have lived in a dense urban area of Portland, Oregon for the past seven years, but now they’re in the market for somewhere more spacious. He narrowed his search down to two main criteria — walking distance to a grocery store and walking distance to a rail stop. The search began with open data.

I defined walking distance as ~5 blocks, but ~10 blocks is still a pretty sane distance. I want to be close to a grocery store and close to a MAX or Streetcar stop. Unfortunately, none of the real estate applications I tried had a feature like this so I decided to create what I needed using open data that I had already been working with for some time now.

Code snippets and explainers follow for how Palmer found his target zones, using a combination of the data, a database, and TileMill.

North by Northwestern looked closer at degrees awarded by their university over the past decade. Simply enter a degree to see the trend. As the makers note, the number of degrees is a lagging indictor of major popularity, since people pick their major and graduate three years later.

Be sure to keep scrolling past the interactive for some explainers. Also, you can download the time series data for your own perusal via the link in the footnote.

Using data from the National Heritage List for England, the London Evolution Animation shows the historical development of London. Mainly, it depicts roads and protected buildings, starting with the first road network built in 410 and into the present. The notes at the bottom provide a fine timeline quality rather than a hey-look-at-London-change video.

[via kottke]

In 1998, artist Eliot R. Brown created a map of Gotham City for the Batman No Man’s Land series. Brown describes the process of making the map, meant to look something like Manhattan with a lot more villains and a way for the federal government to blast the bridges and tunnels to the outside world.

[via Smithsonian]

Some letters in the English language appear more often in the beginning of words. Some appear more often at the end, and others show up in the middle. Using the Brown corpus from the Natural Language Toolkit, David Taylor looked closer at letter position and usage.

I’ve had many “oh, yeah” moments looking over the graphs. For example, words almost never begin with “x”, but it’s quite common as the second letter. There’s a little hump near the beginning of “u” that’s caused by its proximity to “q”, which is most common at the beginning of a word. When you remove “q” from the dataset, the hump disappears. “F” occurs toward the extremes, especially in prepositions (“for”, “from”, “of”, “off”) but rarely just before the middle.

Next step: letter proximity.

As a fun learning exercise, Rob Scanlon made a dashboard that shows GitHub and Wikipedia changes in the style of graphics in Tron: Legacy.

Hello User. This is a reproduction of the graphics in the boardroom scene in Tron: Legacy. If you have not seen that movie, check out this background material on the making of that scene before proceeding.

To make this a bit more fun, the boardroom is configured to visualize live updates from Github and Wikipedia, with more streams to come. Click on a stream in the window to the right to continue.

Type “cd github” and “run github.exe” for maximum pleasure.