Stupid Fight compares fans on Twitter using “stupid indicators” like “OMG” and number of exclamation points. Congratulations, @flowingdata fans. You are smarter than @justinbieber fans. However, I regret to inform you that you are in the yellow. OMG.

[via Waxy]

Some colors represent different things in different parts of the world, while others are universal. For example, in movies, villains are almost always portrayed in black and passion is going to bring out the red. But what about death? American culture usually calls for black. Hindu calls for white. David McCandless and Always With Honor explore these differences.

[Thanks, @augustjoki]

In much of the same spirit of the recent Cartographies of Time, the BBC is running a series on The Beauty of Maps. They’ve got two branches. The first is historical, which is an exploration of some of the world’s oldest existing maps. As a complement, the second is a study of digital worlds, or maps of virtual spaces.
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Because you can never get enough Twitter visualizations, Jason Stirman takes a look at the tweets among Twitter’s first 140 employees. It’s called 140 Characters [pdf]. Ha. Get it?

Much like Chris Harrison’s Bible viz, 140 Characters represents connections, or in this case mutual follows, with arcs. Employees are sorted by account creation date, and larger arcs represent an older employee linked with a newer one. The bar graph shows the number of tweets, relative to everyone else. For example, the engineers @al3x and @evan are quite active, along with newer employee @tiger. Good stuff.

140 Characters is meant only as something pretty to look at, but to take a step in the more analytical direction, I’d try sizing the bars by relative number of mentions between the employees. For one, you’d see who’s most “popular” and second, you might start to see the conversations within the group. I’d expect @ev and @biz to have a lot of @mentions, especially during presentations. Placing employees by creation time, instead of just order, could be interesting too.

[Thanks, @MacDivaONA]

Here we have a case of worthwhile data and an interesting story about obesity trends. People are getting heavier younger. The graph was made to show this; however, it’s hard to understand and kind of makes things more confusing. Can you redesign the above, using the same data, to tell the story more clearly?

The Data

This is what we know about the data. It comes from a study that took place between 1971 and 2006. We have obesity rates, separated by when study participants were born. So for example, in the above graph, the orange line shows the obesity trend for people who were born between 1956 and 1965. When that group was in between 30 and 39 years old, about 27% of them were obese.

It’s a little confusing at first, but let it simmer for a little. It’s actually not too bad.

The Question

Okay, now the question: are people getting fatter faster? The original graph suggests that yes, people are, but the story isn’t as clear as it could be. Plus, it took 19 powerpoint slides to tell it. Your job is to put it all in one graphic. Are you up for it? I think so. Leave your suggestions and links to remakes in the comments below.

One more time – here’s the data [csv], and you can find more info about the study here.

[via FD forums]

There is no way to think up an original and extraordinary design—it can only come as a result of pursuing a given task. In the same way running down a list of words is different from making a narrative.

— Artemy Lebedev, Designer’s block, February 16, 2010

This applies to visualization too. When you don’t have a question to answer or a simple wonderment about something, you end up staring at a bunch of numbers with no clue what to do with them. Want to test this out? Go to data.gov and make something useful.

[via @Coudal]

Almost a year and a half ago, Infochimps, the data repository slash marketplace, released a giant scrape of Twitter data representing 2.7 million users, 10 million tweets, and 58 million connections. Twitter soon requested that they take it down while they figured out how they wanted to handle licensing, privacy, etc.

That was in 2008, before Twitter really started booming. Fast forward to now. Twitter and Infochimps have figured out what they want to do, and the Twitter census data is back up. It’s no longer a measly 2.7 million users anymore though. The population has grown to 35 million.
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It’s finally here. Indiemapper brings easy and flexible thematic mapping online. I’ve been looking forward to this app ever since I got a glimpse of what was to come over a year ago, through the eyes of Indieprojector. The guys at Axis Maps have taken the core functionality of advanced GIS, simplified the work flow with a well-designed interface, and made it it super easy to create beautiful maps.
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In reference to the above, Gen. Stanley A. McChrystal, leader of the American and NATO forces in Afghanistan, joked during a meeting, “When we understand that slide, we’ll have won the war.” The rest of the NYT article goes on to describe the suck that is Powerpoint.

Is Powerpoint really that bad though? I know we like to poke fun sometimes, but is it a limitation of the software, or is it users’ lack of design skills? I mean there’s a site dedicated to beautiful slide decks. Browse through those, and you start to think maybe it’s the latter.

So we have a chicken-and-the-egg problem. Do people just not know how to use Powerpoint or does Powerpoint push people to the bad side? Sound off in the comments below.

[Thanks, Julia]

Stephen Von Worley’s Weather Sealed is one of my new favorites. In his most recent graphic, income tax brackets for individuals are displayed.
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Air traffic has returned to normal levels in northern Europe, and planes fill up the sky once again. Ito world, who has been doing some great stuff lately, visualizes the reboot of air traffic. We start to see some planes on April 18, and by April 20, everything is back to normal.

There are some holes in the data over France and the Atlantic but you get the idea.

[Thanks, Hal]

Add another piece to the ever-growing list of Twitter visualizations. What makes Moritz Stefaner’s Revisit different is that it focuses on the conversational threads between Twitter users over time. Tweets (symbolized by authors’ avatars) are stacked vertically and organized by time horizontally. Tweets that have more attention via @mentions are closer to the middle.
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Matt McInerney of pixelspread describes the trustworthiness of the people behind their facial hair. You better be careful when I’m around. I’m questionable, border-line unsavory. Don’t worry though. I’m not a werewolf – and I don’t have the ability to grow a Hitler. How trustworthy are you?

See the full version here. [Thanks, @b_beesley]

Statisticians are mad and out for blood. Someone called R an epic fail and said it wasn’t the next big thing.

I know that R is free and I am actually a Unix fan and think Open Source software is a great idea. However, for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On that, R is an epic fail. It does NOT fit with the way the vast majority of people in the world use computers. The vast majority of people are NOT programmers. They are used to looking at things and clicking on things.

How dare she, right? Here’s the thing. She’s right. Wait, wait, hear me out. For the general audience – the people who use Excel as their analysis tool – R is not for them. In this case, the one that appeals to non-statistician analysts, R, as they say, is an epic fail (and that is the last time I will say that stupid phrase).

However, R wasn’t designed to enable everyday users to dig into data. It was designed to enable statisticians with computing power. It’s a statistical computing language largely based on S, which was developed in the 1970s by the super smart John Chambers of Bell Labs. The 1970s. Weren’t people using slide rules still? Or maybe it was the abacus. Can’t remember. Oh wait, I wasn’t born yet. In any case, there’s really no need to get into the whole R-for-general-audience conversation — just like we don’t need to talk about why The SpongeBob SquarePants Movie lacked emotional depth.
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Mapping with R and other free and open-source programs feels clunky and hacked-together at times. The plus-side is that it’s all for free, and once you find the time to wrap your head around it, you can get quite a bit done. Tomislav Hengl provides a free e-book, A Practical Guide to Geostatistical Mapping, that can hopefully help you with such tools (namely R, SAGA GIS, and Google Earth). You can also buy the paperback version on Lulu.

[Thanks, Ryan]

Good Mag put on an infographic contest not too long ago that asked people to design around the idea of neighborhood. Any neighborhood would do, just as long the focus was on local. As you might expect, most of the entries were more design than data, but hey, that doesn’t mean they’re not worth looking at.
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A typeface can make or break your graphic. Use Comic Sans, and no one will take you seriously ever again. Luckily, graphic designer Julian Hansen put together this flowchart for a school project to help you figure out what typeface to use. Most of you will probably be interested in the infographic branch.
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In another step towards open data and all that jazz, the World Bank released World Development Indicators 2010 today, which is meant to serve as a progress report of the world.

The WDI provides a valuable statistical picture of the world and how far we’ve come in advancing development,” said Justin Yifu Lin, the World Bank’s Chief Economist and the Senior Vice President for Development Economics. “Making this comprehensive data free for all is a dream come true.

More importantly though, this comes with the launch of the freely available online database and public API to 1,000+ indicators. There used to be a big fee for this data. I can’t speak for the API, but the website is well-designed. It has profile pages for each country, links to download the indicators in Excel and XML, and hey, are those graphs implemented in HTML5? I spy <canvas> tags.
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Jacobo Zanella makes a color palette every day, based on the clothes he’s wearing.

I observe the colors of the shoes and clothes I wear that day, how much skin is exposed, etc., and reproduce that observation digitally, through RGB combinations. No software or programming is involved in the making of the graphs.

String them all together, and you’ve got multi-colors on top (shirts), a lot of black and blue towards the bottom (pants), and whites and grays all the way on the bottom (shoes).

Too bad he’s not logging it programmatically. That could be an interesting view (well, for him).