We’ve seen what happens when you turn on a Roomba and track its vacuum path with long-exposure photography. The LED on top provides a point of focus, and the visual represents an odd blend of chaos and order. Above is what happens when you set different colored LEDs on seven Roombas and let them loose. Don’t miss all the other (clean) messes in the Flickr pool. [via Radiolab]

The Open Knowledge Foundation launched the Open Data Index, so you can see what data countries provide to their citizens.

An increasing number of governments have committed to open up data, but how much key information is actually being released? Is the available data legally and technically usable so that citizens, civil society and businesses can realise the full benefits of the information? Which countries are the most advanced and which are lagging in relation to open data? The Open Data Index has been developed to help answer such questions by collecting and presenting information on the state of open data around the world – to ignite discussions between citizens and governments.

Based on community editor contributions, the index assesses the availability of datasets such as transportation timetables, election results, and legislation, and provides a single-number score. The higher the score is, the more data a government makes available to the public. Of the 70 participating countries, the UK leads the way, followed by the United States and Denmark.

We know that millions of Americans move to different counties every year, and when you look at the net totals, you see a pattern of people migrate from the midwest to the coasts. However, look at migration across demographic categories, and you see more detailed movement. This was the goal of researchers at the University of Wisconsin-Madison, and they recently released their estimates, in map form.
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The Centers for Disease Control and Prevention released their most recent cancer data a few days ago. It’s the numbers for 2010, which feels dated. However, the annual data goes back to 1999, across demographics and states, which makes this data worth a look. You can download the delimited files here.

A browser accompanies the release, as shown below. It’s really just that though, leaving analysis up to you, and it’s rough around the edges.

So if you’re looking for a weekend project, this is a good place to go. I’d probably start with the age breakdowns and work from there.

There comes a time late at night when your screen grows fuzzy and the code runs together. Mistakes happen, and with visualization, the bugs often manifest themselves into abstract images that sort of resemble data. The Accidental aRt tumblr highlights these visual mistakes through the eyes of buggy R code. Ooo, infinite rainbow.

The Simply Statistics unconference just started a few minutes ago. Tune in live below. (Or, catch the recorded version if you’re late.)

With a $250,000 grant from the Knight Foundation, Waldo Jaquith pushes forward with the U.S. Open Data Institute, an effort to link government data sources and organizations over the next year.

I’m convinced that we already have many of the right people, organizations and businesses working on open data in the United States. They just don’t know about each other. (The organization certainly won’t duplicate any of the efforts of the folks in this space.) And we have nearly all of the necessary software, but so much of it is only known within its narrow domain, despite its broad applicability. The institute will connect all of these entities, promote the work of those who are leading the way and provide supportive, nonjudgmental assistance to those who need help. We don’t have all the answers, but we know the folks who do. We want to amplify their message and connect them to new collaborators and clients.

This could be fun.

Betrand Russell: “Mathematics, rightly viewed, possesses not only truth, but supreme beauty — a beauty cold and austere, without the gorgeous trappings of painting or music.” Yann Pineill and Nicolas Lefaucheux demonstrate in the video above. An equation appears on the left, a diagram in the middle, and the real-life version on the right.

A Distributed Denial of Service (DDoS) attack attempts to disable a site or web service by sending a ton of requests from multiple sources. Essentially, the server buckles under the pressure. Sometimes this is done to silence sites that the attackers disagree with, or they might try to take advantage of business backends.

The Digital Attack Map, a collaboration between Google Ideas and Arbor Networks, shows current attacks and serves as a browser for past attacks around the world. Color and size indicate the type of attack and movement represents origins and destinations.

Looking for a job in data science, visualization, or analytics? There are openings on the board.

Research Scientist in Visual Analytics at the IBM Smarter Cities Technology Centre in Dublin, Ireland.

Senior Data Visualization Engineer at Netflix in Los Gatos, California.

Senior Data Scientist at dunnhumby in Cincinnati, Ohio.

Senior Data Scientist at KIXEYE in San Francisco, CA.

Data Analyst at KIXEYE in San Francisco, CA.

Nice one, xkcd.

A quick animated look on the evolution of western dance music, a mixture and blend of various styles and cultures over time.

To make it easier to trace the threads of music history, we’ve created an interactive map detailing the evolution of western dance music over the last 100 years. The map shows the time and place where each of the music styles were born and which blend of genres influenced the next.

There’s a cartogram in the background and lines connect countries and styles. It reminds me of those dance step charts with the feet on them.

You’ve probably heard of the six degrees of Kevin Bacon. The idea is that you can name any actor and trace back to Kevin Bacon through actors who have worked together. Ben Blatt for Slate applied this idea to sports and put together an interactive that finds the number degrees between athletes. The fun part is that you can enter two athletes from different professional sports: basketball, football, and baseball.

What’s even more remarkable is that it’s possible to connect players who didn’t even play the same sport. Cross-sport athletes like Deion Sanders and Bo Jackson are exceedingly rare, and some combinations of sports are hardly seen at all. Of these 18 athletes, all but one—Bud Grant—played baseball as one of his two pro careers, proving either that the stars of the diamond are athletic enough to master other sports or that anyone athletic enough to play basketball or football can also handle baseball. Hockey is the opposite, as there has never been a pro hockey player who also played top-level basketball, football, or baseball. As a result, hockey is a closed system. But once you get off the ice, it’s possible to link every pro baseball, basketball, and football star.

I like how it only takes 18 players (well, actually probably fewer) to pull double-time to make this possible. To link Yao Ming (basketball) and Joe Montana (football), it only took six hops, with Mark Hendrickson as a link between basketball and baseball and Deion Sanders as the link between baseball and football.

Surprising? Kind of, but then again, in 2011, almost all pairs of people on Facebook could be linked with just six hops, too. The barebones interactive is still a lot of fun to play with though if you follow sports.

Reuben Fischer-Baum looks at the most popular girl names by state, over the past six decades.

Baby naming generally follows a consistent cycle: A name springs up in some region of the U.S.—”Ashley” in the South, “Emily” in the Northeast—sweeps over the country, and falls out of favor nearly as quickly. The big exception to these baby booms and busts is “Jennifer”, which absolutely dominates America for a decade-and-a-half. If you’re named Jennifer and you were born between 1970 and 1984, don’t worry! I’m sure you have a totally cool, unique middle name.

Like the trendy names and unisex names explorations, this series of maps is based on data from the Social Security Administration, which is surprisingly formatted and ready to use. If you’re looking to play around with time series data and simple state geography, the SSA site is worth a bookmark. [Thanks, John]

Peter J. Rentfrow, et al. studied personality clusters across states using data from five surveys, totaling responses from about 1.6 million people. They recently published their results in the Journal of Personality and Social Psychology [pdf].

There is overwhelming evidence for regional variation across the United States on a range of key political, economic, social, and health indicators. However, a substantial body of research suggests that activities in each of these domains are typically influenced by psychological variables, raising the possibility that psychological forces might be the mediating or causal factors responsible for regional variation in key indicators.

They found three main clusters, mapped above: friendly and conventional, relaxed and creative, and temperamental and uninhibited.

The maps suggest that states belong only to specific clusters, but I suspect it’s a more continuous scale. For instance, a state might be partially part of cluster 1 and 2, not really 3, as opposed to just cluster 1. Still though, it’s an interesting start. Now if only the data they used were more easily accessible.

A challenge these days with visualization is that a piece might look great on a computer monitor and then break on a tablet or phone. However, if you design your software with that in mind so that it adapts to the device it’s on — so that it’s responsive — your audience loves you more for it. Chris Amico explains how to get started in D3.js: responsive maps, charts, and legends.

It’s been continuous tracking and monitoring for the past couple of days and nights, but none of that matters now. We get to bring him home today.

Those who use the ggplot2 package in R and do everything else in Python will appreciate this Python port of the package from yhat.

Excel makes some great looking plots, but I wouldn’t be the first to say that creating charts in Excel involves a lot of manual work. Data is messy, and exploring it requires considerable effort to clean it up, transform it, and rearrange it from one format to another. R and Python make these tasks easier, allowing you to visually inspect data in several ways quickly and without tons of effort.

The preeminent graphics packages for R and Python are ggplot2 and matplotlib respectively. Both are feature-rich, well maintained, and highly capable. Now, I’ve always been a ggplot2 guy for graphics, but I’m a Python guy for everything else. As a result, I’m constantly toggling between the two languages which can become rather tedious.

Once you get the Python library installed (and its dependencies), you’ll be able to use the same layered graphics approach as the R package, with a similar syntax.

Check out this awesome new thing called MAP. It’s made of 100% sustainable material, easy to share, unbreakable, fits in your pocket, and most importantly, shares none of your information.

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