Posts Tagged ‘visualization’

Islands of Music

Monday, July 26th, 2010
Island of Music

Island of Music from Last.FM's Playground. Visualization of listing habits based on tags

I just stumbled upon this great visualization on Last.fm Playground site. It maps listing habits based on tags as Islands. It’s not beautiful as such, but very informative and good at communicating a lot of data to viewers. Go and have a try with the interactive image.

Last.fm describes it as:

The islands of music playground demonstration is something like a tag cloud where similar tags are located close to each together. The map was created using clustering algorithms.

These algorithms group similar music on islands. Similar islands are placed close to each other. For example, various flavours of metal are located close to each other in the upper right of the map. The map also suggests several more or less continuous transitions. For example, there is a path from folk to doom metal (via psychedelicprogressive rock, and progressive metal).

I particularly like the visualization of the paths between the different tags; “… there is a path from folk to doom metal…”. The big genres or “heavily used tags” and are placed at the edges of the map. Those genres that most people would normally use as an answer to the question, “What kind of music do you listen to?”. Musical clusters, if you like. And I might be miss- or over-interpreting here, but  the smaller islands in the middle seems like commonly used bridges between these clusters (e.g. jazz, new age, soundtrack/instrumental, classical/instrumental). For-example if you listen to hip-hop and psychedelic the chances of you also listing to jazz is quite high.

There is of course a lot of noise in the data (mistagged artist, irish, comedy) like they also point out, but none the less I really like it.

Obesity trends – Makeover

Thursday, April 29th, 2010

A response to the challenge at FlowingData.

The original graph – people are getting fatter, but it is hard to see:

My approach:

My main “beef” with the graph above, is that comparison is difficult, if not impossible. This is of course due to the data gaps, but it could easily be fix with a guideline of some sort. Adding an age-group average, makes it much easier for the viewer to see if the level of obesity is in fact high or low.

Of course the problem with the data gap still exists. And the age-group average will most likely be underestimated here. But now there is some level of comparison.

Final result:

I calculated an index in Excel where 100 =  the age-group average. Then grouped the periods with a stacked column graph. I copied the graph into Illustrator and played around a bit. The final result is multiple indexes/histograms aligned with the different periods.

Regardless of which age-group you choose to look at now, the trend is quite clear: Obesity has increased over time.