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The winners of the NSF’s 2010 International Science and Engineering Visualization Challenge were announced today.

See the past winners: 2009, 2008, 2007, 2006, 2005, 2004, 2003.

Link via Wired Science.

MetaOptimize has a forum for asking and answering questions about data visualization, like comp.lang.idl-pvwave for visualization issues:

Where data geeks ask and answer questions on machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization!

Haven’t asked/answered a question yet, but it looks like a good resource.

Link via FlowingData.

I have wanted to create something like this for awhile.

Link via Chart Porn.

The next visualization type summarized by Juice Analytics in their excellent “Better know a visualization” series: motion charts.

Motion charts are essentially animated bubble charts. A bubble chart shows data using the x-axis, y-axis, and the size and color of the bubble. A motion chart displays changes over time by showing movement within the two-dimensional space and changes in the size and color of the bubbles.

Motion charts are used quite effectively by Hans Rosling in his great TED talks.

xkcd just released the results of a color survey it ran for a month. It contains many interesting, and funny, results. Specifically, it reveals a list of 949 color names with matching values that were frequently used by participants.

I’ve added the xkcd color names to my vis_color routine, so that now you can do things like this:

IDL> erase, vis_color('booger', /index, /xkcd)

You will need vis_color.pro (docs), vis_index2rgb.pro, xkcdcolors.dat, and htmlcolors.dat. The default is to use HTML color names if you don’t set the XKCD keyword. If you want the entire vis library, you can grab it via Subversion:

$ svn co http://svn.idldev.com/vis/trunk vis

Another great overview of a visualization technique by Juice Analytics—this time the misunderstood parallel coordinates. I particularly like the clearly written “What problem does this solve?” section of these posts.

I really like this visualization of the US Income Tax Brackets for 1910-2010 (click on the image to the right to get a bigger image). It shows a lot of information in a manner that can be easily used for multiple types of analysis. It uses color well, i.e., using a simple sequential scheme. It uses inflation adjusted dollars. It uses a logarithmic scale. It places sources of information and credit for the work right on the graphic. It even basically shows how marginal tax rate works.

Good visualization doesn’t have to be flashy.

Juice Analytics is running a series of “Better know a visualization” posts about different types of visualizations focusing on practical considerations about using the technique like what problem it solves and what to watch out for when using it. Several examples are also shown.

Up first: small multiples, also known as trellis charts, lattice charts, grid charts, or panel charts.

Phil Gyford has a funny critique of the overwhelming number of infographics that are now available on the internet. I suppose there is always bound to be “bounce” effect when a new technique is encountered, i.e., a lot of people using an exciting new technique when something simpler like a sentence or a small table might be more appropriate.

Link via kottke.org.

This was an April Fool’s joke yesterday, but today it is pure awesome-ness.