Are you eating right?
The Wheel of Nutrition is a dining plate that reminds us of the fundamental values of nutrition.

Obesity is a severe problem in some of todays commuities. Using visualization to support healthier eating habits is a great opportunity to ignite change in this area. The plate collection “Wheel of Nutrition” created by Hafsteinn Juliusson, Rui Pereira and Joana Pais, is one attempt to tackle this problem.

I think we all agree that the charts are rather jokey than scientific — pizza slice-shaped steak, anyone? Nonetheless I like the idea behind it even if the execution fails to convey useful information.

Are there any working examples of information visualization to support a healty diet beside the standard facts table? I would love to see examples of compelling ingredients tables, nutrition facts and the likes.

While I think the heatmap does a fairly good job at giving an overview at a given timestamp it is hard to really grasp varieties on different dates. The playback feature helps in this regard but for deeper analysis some sort of direct comparison would be crucial.

Gapminder.com with it’s “traces” mode comes to mind while thinking of possible ways on how to combine timeline based animation and direct comparison over multiple timestamps. Another way would be to introduce a split-screen solution, which I feel would be a inferior solution in terms of usability. Are there any concepts out there that let’s a user compare different states of a heatmap? I would love to hear your thoughts on this.

By releasing your chart, instead of meaningfully educating the public, you willfully obfuscated an already complicated proposal. There is no simple proposal to solve this problem. You instead chose to shout “12! 16! 37! 9! 24!” while we were trying to count something.

The refined chart is still complex but tries it’s best by meaningful hierarchy, subtle colors and clear typography. One more time we’re reminded of how important a clear communication of information is and how a simple graphic can influence the perception of a system.

The initial thoughts for the application arose from the discussion about the spread of an epidemic. Travel information could make predictions about a possible geographical spread caused by infected travellers. The idea for the usage of Twitter as the medium was ignited by the fact that we’re using this and other social networking media on a daily basis unlike specific application mainly built for the purpose of tracking people’s movements.

Jer Thorp has crafted a good looking visualization whereas the usefulnes of the gathered insight is still to be evaluated. As much as we’re impressed by the result we love the documentation of the process of creating the applet he has published on his blog. It’s once again a complete explanation about the applications, libraries and APIs he used to scrape, process and enrich the used data.

Images on Twitter

Over the day I’ve collected around 5000 15’000 geo located Tweets mentioning the swine flu. After having them mapped, we see Great Britain beeing one of the most active cities in Europe (it’s important to take the timezones in account). Berlin and Paris and of course Barcelona are the most active centers of the other countries.

I’ll continue to gather the data and extend the visualization to represent different days on different daytimes. Combined with the data of the cases the correlation between media coverage and actual thread could emerge.

The feed url is: http://pipes.yahoo.com/wiederkehr/extendedswineflu
The source can be cloned at Yahoo Pipes

The data set

The Guardian Data Store keeps the data stored in a public spreadsheet on Google Docs making it available to everybody. If you need geo data on the entries to map the data, you can use the extended feed we’ve created with longitude and latitude to all the entries.

NY Times Interactive via @flowingdata on Twitter