All news is connected in some way or another. News Dots from Slate shows just that.

News Dots scans all articles from major publications—about 500 stories a day—and submits them to Calais, a service from Thompson Reuters that automatically "tags" content with all the important keywords: people, places, companies, topics, and so forth. Slate's tool registers any tag that appears at least twice in a story.

Bubbles are sized by how much the corresponding topic is written about, and connections are made when topics are mentioned in the same article. Click on a topic to see the matching articles in the sidebar.

How everything is placed I'm not exactly sure. I'm guessing distance represents some abstract measurement of relatedness. You guys have any better guesses?

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World Progress Report - 4 days left to order

Anatoly Zenkov provides this nifty tool (Mac and PC) to track your mouse pointer. Really simple. Just start it, let it run, minimize the window, and carry on as usual. In the end, you get this image that looks something like a Pollock. Circles show areas where the pointer didn't move while the tracks show movement.

The above is my own activity during the past couple of hours. I was just randomly browsing while watching the Laker game, so it's kind of all over the place. Run while using a single application for a while, and you might see something like Zenkov's tracks on Photoshop.

What do your tracks look like?

[via feltron]

Update: Anatoly has setup an actual site for the app instead of it sitting in the dropbox boonies of boontown. Go there for the most up-to-date info.

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World Progress Report - 4 days left to order

Are you using the most efficient typing technique or are your fingers jumping all over the keyboard? If it's the latter, I implore you - there is a better way. Your arms don't have to be tired after typing for ten minutes, and you just might finish that novel before the end of the decade. See these finger movement diagrams form Weather Sealed if you don't believe me.

The above shows the amount of finger movement with the two-fingered peck. The thicker the line, the more your fingers travel to and from the end points.

Here's the diagram for touch typing, the beginner technique that you learn from Mavis Beacon. You always return to that home row in the middle.

Forget the return home, and you're a typing wizard.

See the diagrams for all the other techniques over on Weather Sealed.

In other news, I just found out Mavis Beacon isn't even a real person. What the heck? I feel cheated.

[Thanks, Steve]

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World Progress Report - 4 days left to order

Bestiario, the group behind 6pli and a number of other network projects, released their most recent project - Canvi & Temps - that explores the complexity of science since the early 1920s.

The first part, Temps, is two linked charts stacked on top of each other. On top are individual articles and on bottom are tags that provide context to those articles. You'll probably be tempted to read the visualization as a stacked area chart, but that wouldn't quite work. Bar height doesn't really mean anything other than there were more articles or tags in a year when the bars are small. Instead, color indicates count. Roll over an article and see the related tags over time or vice verrsa.

It reminds me a lot of Bestiario's earlier Atlas of Electromagnetic Space.

Then there's Canvi, which is a network view of tags and links:

I'm not completely sure what I'm looking at with Canvi. It's more artistic expression than utility, I think. From the Bestiario blog:

The uniqueness of Canvi lays in the fact that it explores new node positioning techniques as well as their spatial relations. In particular Canvi combines different techniques (geometric paradigms) subtly alternating between nodes and their relation. This means that the representation of the network is in constant movement (a breathing sort of movement), this way offering a broader perception of the local network.

Yeah, I'm still not sure, but it's nice to look at. Ideas anyone?

[Thanks, Jose]

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World Progress Report - 4 days left to order

your.flowingdata got a couple of cool updates recently. One is based on your interactions with others on Twitter and the other helps you find relationships in your actions.

Twitter Mentionmap

The first is the Twitter Mentionmap created by Daniel McLaren. It's a network visualization (above) that lets you explore how you (or other Twitter users) interact with others.

It's not focused on the data that many of you are used to seeing on YFD, but it's always been my plan to bring in other data sources. So when I saw Daniel post the original Mentionmap, I jumped at the chance to get a version for YFD. It seemed like a good first step to branching out. Get it? Network, branching out. Oh nevermind.

By the way, Daniel used his constellation framework to build this. It's called asterisq. It's worth a look if you're looking to visualize network data. Daniel can also help you with customization and design.

Correlating Actions

Finally, some actual statistics starts to enter the picture. Using cross-correlation, this visualization shows you how your actions are correlated. Remember, correlation doesn't mean causation, but it can hint where to look in your data. Correlation also becomes more meaningful as you enter more data, so keep that in mind as you use YFD.

What do you think?

You can find the correlation viz along with the Twitter Mentionmap in the exploration section of YFD. Check 'em out now and let me know what you think in the comments below. This is a thesis-related project I've been working on for some time now, and every little bit of feedback helps a lot.

Update: New version of YFD iPhone app

I just realized that the iPhone app for YFD [iTunes link], by Jose Castillo, also got an update. It's now easier to timestamp your data:

Give it a try. Tell us what you think.

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World Progress Report - 4 days left to order

There's this branch in computer science and statistics for vision research. Normally, if you ever hear about it in the news it's in the context of spotting terrorists in security tapes or facial recognition checkpoints (you know, like what they have in movies in front of giant steel doors). That is of course not the only application.

Google (and many others) has been playing around with this stuff for a while. Most recently, they released Google Image Swirl in their labs section, which utilizes computer vision to find similar images.

Above is my search for happy cat. The initial search result is what you're used to. It's a matrix of thumbnails. Click on one of them, and you'll get similar images clustered as a network graph.

Google Image Swirl: the new way to find if someone is plagiarizing your work.

[via information aesthetics]

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World Progress Report - 4 days left to order

After yesterday's weirdness, I'm in the mood for something light.

The show about nothing lasted nine seasons, during which Jerry, George, Kramer, and Elaine interacted with a whole lot of people. Ricky Linn, a graphic design student, mapped all the relationships over the years.

Connecting lines are color-coded by type of relationship. It looks like Kramer was more about making friends while Jerry and George were more the dating type. I guess Elaine kept a tighter circle of friends.

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As far back as I can remember there's always been a mystique around the Kennedy family. It's almost like if you bear the Kennedy name, you're destined for greatness. With the recent passing of Eunice Kennedy Shriver, Patterson Clark of The Washington Post maps out the famous family tree. The tree starts with the marriage of Joseph Patrick Kennedy and Rose Elizabeth Fitzgerald and branches out to current family members and what they do for a living.

[via DataViz]

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Contrary to what a lot people might think they know from the movies, the X-Men universe stretches out quite a ways with lots of characters and lots of relationships. This super detailed relationship map for all X-Men characters from UncannyXmen shows just that.

Connections are color-coded to show the type of relationship between a pair of characters. For example, green is a one-sided infatuation, pink is a flirtation by both parties, and a dashed line signifies one of the characters is from an alternative reality. Wolverine sure gets around.

[via VizWorld]

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This is a guest post by Martin Krzywinski who develops Circos, a GPL-licensed (free) visualization tool that can help you show relationships in data. This article is based on a longer writeup which you can find here.

Suppose that you are reading an article and the text refers you to a table on the next page. Before you turn the page, what are your expectations of the table? Chances are, you would like it to communicate trends and patterns. Chances are, too, that it will fail and simply deliver numerical minutiae. You are left hunting around the numbers for a while, only to return to the text in hopes that the table's data trends will be communicated elsewhere.

Imagine if, instead, the table were replaced by a visual representation that was agnostic to the data domain, sufficiently quantitative to identify patterns and descriptive statistics, and made no assumptions about the kind of patterns that might exist. In this article, I outline one such representation.

Tables are Visual Obstacles

As the saying goes - it's not the table, it's you. We are notoriously bad at evaluating quantitative information when it is presented in its raw numerical form. We reach our limit in the ability to glean trends from a table very quickly. Consider the five tables below - the 1x1 table is trivial to interpret and the 5x5 table impossible. Somewhere in between is where you reach numerical overload.

Unfortunately, most published tables are larger than these examples. Due to their size, many fail to effectively communicate their information. They provide the numerical minutiae from which visual representations can be genreated, but on their own they make opaque any patterns that might arise in such representations.

An Uninterpretable Table

Even prestigious journals are not exempt from poorly communicated data. Frequently it is not an issue of poor communication, as much as no communication. The reader is left frustrated, without a sense of what is important in the data and which differences are meaningful.

Consider the table below (Horvath, J. E. et al. Development and application of a phylogenomic toolkit: resolving the evolutionary history of Madagascar's lemurs. Genome Res 18, 489-99 (2008)), which suffers from two extremes of the same problem: inappropriate amount of information.

On the left half of the first table there is nearly no information - almost all values are 1.0. On the other hand, the right half of the table is packed so tightly with numbers as to make them visually unparsable. The second table is even worse, suffering not only from information overload, but also from both poor layout, and inconsistent precision (e.g. 7 (4.74-9.24)).

Poorly designed tables can suffer from visual noise (lots of ink, but no information), obscured statistics (descriptive statistics are hidden in numbers), unparsable content (too much information), misguided sightlines (poor row and column spacing), and burden of significance (reported precision is much higher than required for visual inspection). Such tables do not help understand the scale and tolerance inherent in the data and leave the reader faced with a deluge of numbers, to fend for themselves.

Visualization of Tabular Data

The method presented here provides an alternative to mitigate the problems outlined above. It is a visual approach that uses Circos[http://mkweb.bcgsc.ca/circos] to represent rows and columns in a circular fashion, and ribbons to represent cell values. Does it solve every table's problems? No. It does provide, however, a way to capture the essence of the table and present it quantitatively and attractively.

In this approach, relationships between data elements (e.g. a row and a column) are encoded by ribbons that join segments that correspond to these elements.

The ribbons can have different end thicknesses to represent a ratio between the elements. By coloring the ribbons (and/or adding transparency), such as shown below, the representation can focus on the flow of information in a particular direction (e.g. from A (left), or to A (right)).

In practise, a visualization of a table based on this scheme might look like the figure below. Normalizing the segments to equal size is motivated by whether absolute or relative relationships are important.

Practical Example - Preference for Hair Color in Relationships

To illustrate this visual approach with a small data set, consider how one could visualize dating preference for hair color. You might have information about the relationship history of a large number of individuals and want to visualize the probabilities of transitions between hair colors in successive relationships.

The data might look like this, where each cell represents the number of cases in which someone moved from a partner with one hair color (row) to another (column). For example, 2,868 individuals dated someone with red hair right after someone with black hair.

These data are synthetic (drawn from my own stereotypes) and visually represented in the image below

Several trends, not immediately discernable from the table, are made clear in the figure. Moreover, given that we can simultaneously process more visual details than numerical ones, this image can communicate many patterns at the same time and therefore enhance both interpretation and retention of information.

Practical Example - Reactivity of Chemical Elements in Minerals

The hair color data set was both small and synthetic. Let's turn to something much more complicated to see how a visual representation can help avoid visual burden.

For this example, I used a database of mineral formulae [http://un2sg4.unige.ch/athena/mineral/minppmi.html] to extract all pairwise element ratios from each mineral. or example, Zabuyelite is Li2CO3 and would therefore contribute +2 (Li,C), +2 (Li,O), +1 (C,Li), +1 (C,O), +3 (O,Li), +3 (O,C). The resulting table was a 77 x 77 matrix [http://mkweb.bcgsc.ca/circos/export/mineral-element-ratio-table.txt] of ratios of elements.

To start, I condensed the table by combining elements of the same classification (e.g. alkali metal, transition, etc). In the table below, the counts are in units of 1,000.

The image of the table below presents the trends in the data well. By keeping the segment size for each classification in absolute units, the representation also communicates information about abundance. By using relative tick marks however (every 10%) for each segment, it is possible to quickly evaluate extent of contribution from each ribbon to its segments.

By greying out ribbons that provide minor contribution, and varying the amount of opacity as a function of percentile rank for the remaining ribbons, major patterns can be accentuated (image below, left). Alternatively, ribbons' percentile rank can be mapped onto a rainbow color palette (image below, left).

Now what happens when the data for individual elements are drawn? It is no surprise that the result is a very complicated image.

However, even at this level of detail, the image is visually parsable. First, relative sizes of ribbons quickly indicate which segments provide the majority of contribution to the table. The thin ribbons, which correspond to small values in the table, do not distract the eye to the same extent as a sea of small numbers in a table.

Oxygen's abundance in minerals is reflected in the fact that its segment occupies half of the figure. To explore how oxygen combines with elements as a function of their abundance, the image below shows all segments normalized to equal size (except oxygen, which is shown at 20x) and uses color to focus on pairings between oxygen and other elements.

The manner in which the ribbons transit across the figure, and in places cross, indicates a difference between the order of reactivity and the order of abundance for the elements. For example, look at the ribbon between sulphur (S) and oxygen, indicated by the black arrow. Sulphur is 4th most abundant, but 12th in terms of number of O atoms that combine with it. Similarly, calsium (Ca) is 7th most abundant but 3rd in terms of reactivity with oxygen (red arrow).

Another treatment of the figure is shown below, with the oxygen segment removed, and the ribbons that correspond to element pairs that have the highest relative affinity (strong preference) for one another shown in color.

Conclusion

While, it is possible to apply information design principles to a table to ensure that it communicates its content clearly, sometimes tables are not the best way to present data.

I hope that in this short writeup I have given you ideas that will be useful in your quest to articulate your own data sets.

Martin is a scientist who specializes in bioinformatics at the Genome Sciences Centre in Vancouver. Visit his site for more on Circos and some of Martin's other data musings.

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World Progress Report - 4 days left to order