In Now, the latest XKCD cartoon, Randall Munroe provides a handy, continuously updated way to visualize the current time all over the world. I happen to know that Munroe is an inveterate long-distance driver who likes to pass the hours on the road by calling friends; I imagine that a wheel like this would be handy for helping him figure out which continent he should be searching for in his address-book in order to find conversational partners at any hour of day.
Gabriel Michael, a PhD candidate at George Washington University, subjected the IP Chapter of the secret Trans-Pacific Partnership, leaked by Wikileaks last week to statistical analysis. The leaked draft has extensive footnotes indicating each country's negotiating positions. By analyzing the frequency with which the US appears as the sole objector to other nations' positions, and when the US is the sole proponent of clauses to which other nations object, Michael was able to show that TPP really is an American-run show pushing an American agenda, not a multilateral trade deal being negotiated to everyone's mutual benefit. Though Canada is also one of the main belligerents, with even more unilateral positions than the USA.
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Wait But Why has a fantastic series of graphs that aim to help us wrap our heads around the enormous timescales on which forces like history, biology, geography and astronomy operate. By carefully building up graphs that show the relationship between longer and longer timescales, the series provides a moment's worth of emotional understanding of the otherwise incomprehensible.
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OpenCorporates has a data-visualization tool for peering into the corporate tax-evasion structures of big corporations -- subsidiaries nested like Russian dolls made from Klein bottles:
In Hong Kong, there's a company called Goldman Sachs Structured Products (Asia) Limited. It's controlled by another company called Goldman Sachs (Asia) Finance, registered in Mauritius.
That's controlled by a company in Hong Kong, which is controlled by a company in New York, which is controlled by a company in Delaware, and that company is controlled by another company in Delaware called GS Holdings (Delaware) L.L.C. II.
...Which itself is a subsidiary of the only Goldman you're likely to have heard of, The Goldman Sachs Group in New York City.
That's only one of hundreds of such chains. All told, Goldman Sachs consists of more than 4000 separate corporate entities all over the world, some of which are around ten layers of control below the New York HQ.
Of those companies approximately a third are registered in nations that might be described as tax havens.Indeed, in the world of Goldman Sachs, the Cayman Islands are bigger than South America, and Mauritius is bigger than Africa.
Tim Harford's 2011 book Adapt proposes an ingenious regulatory solution to this problem, explaining how it might have been applied to companies like Lehman, whose complex structures drew out the post-bankruptcy mess for years and years. He suggested that if banks were stress-tested to determine how long they'd take to sort out after a bankruptcy, and then required to keep reserve capital necessary to run all operations through that whole period, they would be strongly incentivized to have the most simple, transparent corporate structures. Otherwise, they'd have to tie up billions of dollars in escrow to keep the doors open in the event that it all collapsed.
OpenCorporates | How complex are corporate structures?
Stephen LaPorte and Mahmoud Hashemi's "Wikipedia Recent Changes Map" plots anonymous edits to Wikipedia on a world-map in realtime, based on the location of the user (only anonymous users are identified by IP address, so they're the only ones whose locations can be estimated). It's a hypnotic view into Wikipedia's casual users and vandals, as well as unobservant users like (I often forget that I'm logged out until after my edit, and have to go back and add an attribution).
When an unregistered user makes a contribution to Wikipedia, he or she is identified by his or her IP address. These IP addresses are translated to the contributor’s approximate geographic location. A study by Fabian Kaelin in 2011 noted that unregistered users make approximately 20% of the edits on English Wikipedia [edit: likely closer to 15%, according to more recent statistics], so Wikipedia’s stream of recent changes includes many other edits that are not shown on this map.
You may see some users add non-productive or disruptive content to Wikipedia. A survey in 2007 indicated that unregistered users are less likely to make productive edits to the encyclopedia. Do not fear: improper edits can be removed or corrected by other users, including you!
This map listens to live feeds of Wikipedia revisions, broadcast using wikimon. We built the map using a few nice libraries and services, including d3, DataMaps, and freegeoip.net. This project was inspired by WikipediaVision’s (almost) real-time edit visualization.
Wikipedia Recent Changes Map
Cartography and data analysis nut Brandon M-Anderson put together this impressive zoomable map of the United States with one dot for each of the 308,450,225 people recorded by the 2010 census: oddities revealed include people living in "abandoned" areas or parks. A Redditor stitched the tiles into a huge image.
Fred sez, "My lady, Thessaly La Force, recently published a book with the artist Jane Mount called 'My Ideal Bookshelf.' In it, Thessaly interviews over 100 people and Jane paints their bookshelves.
As I observed Jane and Thessaly compile the book over the last year, I couldn't help but think about all the fun opportunities I could have exploring the data behind the shelves.
Each of the 101 contributors Thessaly interviewed picked as many books as they thought represented their ideal bookshelf, and I knew some of them would pick identical books.
What would the most popular book be (it was Lolita)? On average, how many books did people choose? What would a taste graph linking contributors to each other using the books on their shelves look like?
So I pulled the data together into a set of graphs an interactive 3D plot that visualizes the relationships of the contributors based on the books they choose."
The Data Behind My Ideal Bookshelf
Stamen, a design firm in San Francisco, was commissioned to study the private transport networks that run from San Francisco down to Silicon Valley. The traditional commuter dynamic for cities is suburbanites coming into the city to work, but in San Francisco it runs both ways, as city-dwelling tech workers catch a variety of semi-luxurious, WiFi-equipped buses with power outlets and work tables to tech campuses down the peninsula. I watched this with some amusement when I was in San Francisco this summer, observing how a crowd of googlers with Android handsets would magically converge on a corner near Dolores Park just as a big black Google bus pulled up and whisked them away (A friend at Google tells me that his bus has its own mailing list where they recently had a kerfuffle when some enthusiastic people proposed a weekly festive party-ride on Friday afternoons, to the horror of the more sedate riders).
Fun fact: apparently Twitter employees refer to the entire Mission district as "the campus" (though I assume that this is ironic).
We enlisted people to go to stops, measure traffic and count people getting off and on and we hired bike messengers to see where the buses went. The cyclists used Field Papers to transcribe the various routes and what they found out, which we
recompiled back into a database of trips, stops, companies and frequency. At a rough estimate, these shuttles transport about 35% of the amount of passengers Caltrain moves each day. Google alone runs about 150 trips daily, all over the city.
We wanted to simplify that, to start thinking about it as a system rather than a bunch of buses, so we began paring down the number of stops by grouping clusters where the stops were close to each other.
The subway map is the end result of that simplification; it's not a literal representation, but it's much more readable than the actual routes. We also wanted to show the relative volumes, so the map segments are scaled by how many trips pass through them; you get a sense for just how much traffic the highways get, and how the routes branch out from there to cover the city. We only mapped San Francisco shuttles, many of these companies operate additional routes in East Bay, the Pennensula, and around San Jose, including direct routes from Caltrain stations to corporate campuses.
The work was commissioned by ZERO1 and partly funded by the James Irvine Foundation.
The City from the Valley (2012)
(Thanks, Fipi Lele!)
James Cheshire (Department of Geography, UCL) produced a series of interactive maps of London that show the relationship of common surnames to different neighbourhoods:
This map shows the 15 most frequent surnames in each Middle Super Output Area (MSOA) across Greater London. The colours represent the origin of the surname (*not necessarily* the person) derived from UCL’s Onomap Classification tool. The surnames have also been scaled by their total frequency in each MSOA.
(via Sociological Images)
Matthew Epler's Grand Old Party project takes the approval-rating curves of GOP presidential hopefuls and turns them into 3D solids, then turns those into buttplugs.
Grand Old Party demonstrates that as a people united, our opinion
has real volume. When we approve of a candidate, they swell with
power. When we deem them unworthy, they are diminished and left
hanging in the wind. We guard the gate! It opens and closes at our
will. How wide is up to us.
In an age of information, we rely on hard facts. Each of the shapes
you see here come directly from poll data collected by Gallup. This
data reflects approval ratings for each GOP candidate among registered
Republican voters from December 10, 2011 to April 1, 2012.
Each shape’s girth is a reflection of popularity while their height is a
reflection of time.
The contours of these delightful shapes conjure up the waves of
amber grain and those lapping at the rim of our great nation spanning
from sea to shining sea. As the battle for the Presidency rails
on, we must remember that Americans may may have achieved
freedom through war, but they are also a people of love. After all, in
the end all we have is each other.
3D Printing and wonders of the Internet
Update: Derp. It's a dupe.
Here's a diagram that shows the relative size of a great grey owl's body to its feathers. It's hosted on Wikimedia commons, labelled "Cross sectioned taxidermied Great Grey Owl, Strix nebulosa, showing the extent of the body plumage, Zoological Museum, Copenhagen."
File:Strix nebulosa plumage.jpg
(via Beth Pratt)
Dr Ben "Bad Science" Goldacre sez, "I did a really sophisticated and complex data visualisation. I think you might enjoy it. There's definitely a pattern in there, I just need to decide what statistical tests will best extract the signal from the noise."
Who is, and is not, invited to Cameron's emergency NHSbill summit? A data visualisation.
Mike from Mother Jones sez, "For our upcoming "dark money" print package, we chartified the known
galaxy of outside political spending groups by their size. As you can see,
we ended up with red giants and blue dwarfs."
If Citizens United was the Big Bang of a new era of money in politics, here's the parallel universe it formed: rapidly expanding super-PACs and nebulous 501(c) groups exerting their gravitational pull on federal elections. A group's size in the chart below is based upon all known fundraising or spending since 2010…so keep an eye out for dark matter. Come back for regular updates.
The Crazily Expanding Political Money Universe
Occupy George presents data about US wealth disparity as a series of data-visualizations that are intended to be overprinted on US dollar bills. The visualizations are available as templates to turned into rubber stamps, or inkjet-printed overtop of US currency that is first lightly affixed to sheets of paper.
(via Beth Pratt)
Mike Kneupfel, a student at NYU's Interactive Technology Program, made a 3D model showing the keys he presses most frequently when typing, composed of raised keys on a keyboard. It's a fun and eye-catching way of visualizing data by using the thing whose data you're analyzing.
Conclusions - This was just a first go at trying to create a data driven 3d sculpture. I wound up scaling the keys a little bit too much in the vertical direction. The weight of the tall keys caused the towers to tilt at an angle. I plan on showing this prototype to a few people that will hopefully give me more ideas for new data sets to look at. I want to try and use the CNC for future data driven sculptures. I also want to try and include color into the sculpture somehow.
Keyboard Frequency Sculpture