Wow, Manuel Lima, Senior UX Designer at Bing, got through a world of information in this 11 minute RSA Animate video. He spoke about the topic for which we all know him - networks. Beginning with the tree as a symbol of relationships (e.g., Aristotle's Tree of Knowledge), Manuel then quickly sweeps through many concepts through the centuries to finally land on a modern day approach to relational information, the web or network. As trees are no longer capable of representing the complexities of the modern world, we have to find new ways to visualize these structures or perhaps even find a universal structure. His talk is loaded with beautiful examples of trees and networks.

If this fast paced animation is above your processing capacity, you can view the more austere real world video of Manuel instead. It has the bonus of an interesting interview with him in the last 6 minutes.

PhD student Adrien Friggeri demonstrates a new clustering algorithm with a visualization of the agreement groups within the United States Senate over time.

As you might imagine, there are two obvious groupings, Republican and Democrat. It gets interesting though when you look at Democrats classified as Republicans and vice versa. For example, the 11 Republicans placed in the Democratic group of the 110th Congress:

Most of whom are either moderates or closer to the Democrats than to their own party. Charles Hagel was critic of the Bush Administration which he described as "the lowest in capacity, in capability, in policy, in consensus — almost every area" of any presidency in the last forty years. George Voinovich has been known to oppose lowering taxes and frequently joined the Democrats on tax issues. John Warner is a moderate Republican and has centrist stances on many issues, to the point that he once faced opposition of other members of his own party when he decided to run for re-election.

Be sure to click on the gray boxes to follow the trajectories of different cohorts.

Making use of data from the Federal Election Commission and The New York Times Campaign Finance API, ProPublica takes a closer look at where campaign contribution is going.

Many have been detailing the vast sums being raised by the presidential candidates and the super PACs supporting them. But where are all those millions being spent? Among other things, the answers can provide hints on potential improper coordination between campaigns and super PACs. Here are the 200 biggest recipients of spending by the major campaigns and most of the major super PACs.

It's a sankey diagram with campaigns and Super PACs on one side and payees on the other. (I rotated the image above clockwise.) Select a campaign to see what they've spent their money on, or select a payee to see who's paying them. As I browsed through payees, my next question was what these companies, organizations, and people do since $377,222 from Obama for America to a company called PDR II DBA Share Share doesn't mean much to me. I haven't looked at FEC data in a while, but I vaguely remember a way to categorize spending.

Find more information on the making of this graphic here.

For the MIT Sloan Sports Analytics Conference a few weeks ago, Stanford biomechanical engineering student and Ayasdi analyst Muthu Alagappan presented his work on redefining basketball positions.

After studying players like LeBron James and Blake Griffin, many analysts are now suggesting that there are new positions, which are simply hybrids of the one's we already had. For example, some players are now labeled "point-forwards" or "combo-guards." But what if we were wrong about our initial five positions. Maybe a "Center" is just a label for people over a certain height, and there are actually three different types of big men in the NBA.

An analysis, done with data exploration tool Ayasdi Iris, provided 13 possible positions, as shown above. Nodes and edges are colored by points per minute on a blue (low) to red (high) scale.

So for example, those typically classified as centers or power forwards are classified as scoring rebounders, paint protectors, and scoring paint protectors. Dirk Nowitzki might be considered a scoring rebounder, whereas Joakim Noah is a paint protector.

The point? Hopefully teams can use this information to make better decisions about who to trade and draft. Of course, I'm sure scouts know about these fuzzy positions already, so I think the next step is to look at what positions the best teams have and had, and more importantly, how a "one-of-a-kind" player can change everything.

Related

• Geography of the basketball court
• Jeremy Lin is no fluke
• March Madness power rankings

With NCAA March Madness in full swing, the basketball graphics are out in full force. This one by Angi Chau, shows the probabilities of teams winning each game, and eventually the championship, based on simulated bracket rankings. Done with D3, each node represents a game and teams are circled on the outside. Roll over a team, and get all the probabilities for that team going to the end or roll over a game to see the probability of teams winning that game. Sorry, Colorado. You have a 0% chance of winning it all. You, too, Vermont.

Hopefully, Chau keeps updating throughout the tournament. And maybe some color-coding to indicate probabilities would be useful here. Now excuse me while I go place some educated bets. (One million on Colorado.)

The Iliad is an epic poem by Homer with a lot of characters and story lines going on at once. I vaguely remember reading bits and pieces in high school and getting totally lost. Santiago Ortiz explores these relationships in his latest work, which draws on the connections i.e. character sentence co-occurrences.

There are two views. One is a network diagram (above), with characters sized according to number of connections with others, and a matrix view accompanies. The network has sort of a fisheye effect as you mouse over, which I think is there to make it easier to browse, but as it goes with these sort of visualizations, there are still some challenges as you try to get more details or look at smaller nodes.

The second view is a streamgraph, with a stream for each character.

I had trouble getting excited about the content, but it's fun to play around with the interactions.

When you use the same password for every online account, there could be trouble down the line if one of those sites was breached. You gotta mix it up these days. As part of their Watchdog initiative, Mozilla released an add-on to help you see how you're reusing passwords, and to hopefully keep your personal information secure.

Ever been told not to reuse the same password across different websites? With this add-on, you can visualize your passwords and the sites you use them on. By looking at this visualization, you can get a quick idea of which passwords you've been using the most, and the kinds of sites you're using them on. As you continue to change your passwords and update your password manager, the picture will improve!

Personally, I don't save any of my passwords. The risk of my computer getting stolen and some random person gaining access to my online accounts is too much for me to handle. Of course as a result, I have to put up with the craptastic experience of trying to remember passwords with a variable number of capital letters, symbols, and digits.

[Mozilla]

Related

• Analysis of passwords in Sony security breach
• Open Call to Designers: Visualizing Mozilla Community
• Mozilla Labs Ubiquity Plugin Makes Mashups Easy

While SOPA and PIPA are no laughing matter (join the strike), the reaction from those on Twitter who don't know what's going on is great entertainment. Do a search on 'wtf wikipedia' for tweets from confused individuals who are trying to find information on stuff. I'm just going to leave Twitter trackers Revisit and Spot, by Moritz Stefaner and Jeff Clark, respectively, open all day. "OMG I'm doing homework and Wikipedia is blacked out wtf !!!!!!!!!!!!!!!!!!!!!"

Whenever you look at an item on Amazon, the site recommends related items that you might be interested in. So in a way, these items are connected by how people buy. Artist and designer Christopher Warnow uses the metaphor to create a network of Amazon products, where each node represents an item, and connections, or edges, represent common bonds of recommendations. Simply enter an Amazon link, and Warnow's software generates a network.

For example, the image above is the network for Edward Tufte's Visual Display of Quantitative Information, although Stephen Few's Information Dashboard Design seems to have more connections for some reason. My quick guess is that book's that are less niche have more connections, because when I entered Visualize This, the network was pretty small. Although I would've thought that Tufte's book would have a larger network than Few's.

In any case, the application and Processing code is free to play with. Warnow uses Gephi for network connections and grouping. Or if you don't feel like downloading a 60mb file, you can just watch it in action in the video below.

You might also be interested in Yasiv. It's a web app with a similar idea, but not quite as slick of an implementation.

[Christopher Warnow via Datavisualization]

Related

• Why network visualization is useful
• Explore your LinkedIn network visually with InMaps
• Amazon Gets In On the Public Data Arena

Twitter is an organic online location, full of retweets, conversations, and link sharing. Jeff Clark tries to show these inner workings with his newest interactive, Spot. Enter a query in the field on the bottom left, and Spot retrieves the most recent 200 tweets. You then can choose among five views: group, words, timeline, users, and source.

Each tweet is represented by the tweeter's profile picture, and they rearrange themselves as you switch between views. The latter three views, timeline, users, and source, arrange tweets into bar charts. Fairly straightforward.

Spot gets interesting with the first two views though, groups and words. Tweets are arranged based on the words they use.

Above, for example, is the word view on the search "flowingdata." Tweets cluster around words like world and data. Below is the same search, but with the groups view. Users who tweeted similar text (usually retweets) are grouped together. What jumped out at me was the group on the bottom with a single user's face. That turned out to be a spammer.

Give it a try for yourself here.

[Neoformix]