Monthly Archives: April 2013

Chord Display (Music)

ITunes Music with a RAVE Chord visualization

ITunes Music with a RAVE Chord visualization

I took the data from my last post, aggregated up some fields and made a Chord Diagram for it, using RAVE. I was lazy and didn’t do a stellar job on rolling up years, so the year indicated is actually the center of a 4-year span — so 2007 is actually [2005.5, 2009.5] which is a little odd.

No big insights here — podcasts are all recent; alternative music is mostly recent too (Eels and Killers are artists with a large number of songs in my library). Interesting that I didn’t buy a lot of music form around 1999 …

I thought there were more packages that could do chord visualizations, but was only able to find some D3 examples.

iTunes Music to Data, via Python

Music Treemap

8000+ iTunes songs by genre and artist, colored by rating (ManyEyes version)

The track information stored in iTunes is pretty interesting from a visualization point of view, as it contains dates, durations, categories, groupings — all the sorts of things that make for complex, interesting data to look at.The only issue is … it’s in iTunes, and I’d like to get a CSV version of it so I can use it in a bunch of tools.

So, here is the result; a couple of Python scripts that use standard libraries to read the XML file exported by iTunes and convert it to CSV. It’s not general or robust code, just some script that worked for me and should be pretty easy to modify for you. I’m not a Pythonista, mostly doing Java, so apologies for non-idiomatic usage. Feel free to correct or suggest in the comments as this is also a learning exercise for me.

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From the Vaults: Maps are Just Another Element

For the Grammar of Graphics language-based approach to visualization, and therefore in the RAVE visualization system, maps are simply another element that can be used within the grammatical formulation.

Although most people consider a map a very different entity from a bar chart, all that really differs between a bar chart and a map of areas like the one included here is that instead of representing a row of data by a bar, we use a polygon (or set of polygons) on a map. Otherwise their properties ought to be the same — we can apply color, patterns, labels, transparency. We can set a summary statistic when there are multiple values for each polygon to reflect min, max, mean, median, range, or any of the regular sets of items. We can flip, transpose and panel the charts. Essentially, from the grammatical point of view, if you can do it to a bar chart, you can do it to a map. The only limitation is that whereas the sizes of the bars can be set or determined by data, the map polygons cannot, so setting sizes on the map polygons has no effect.

US Chorlopleth

Orthogonality is also important — so we can say we want a point element instead of a polygon, as in the above where we’ve added a second element to a RAVE US Map conveying different data as well as being a good place to put labels

From the Vaults: How to Speak Visualization

In English, we use many different words to describe the same basic objects. In one survey, researchers Dieth and Orton explored which words were used for the place where a farmer might keep his cow, depending on where the speaker resided in England. The results include words like byreshipponmistallcow-stablecow-housecow-shedneat-house or beast-house. We see the same situation in visualization, where a two-dimensional chart with data displayed as a collection of points, using one variable for the horizontal axis and one for the vertical, is variously called ascatterplot, a scatter diagram, a scatter graph, a 2D dotplot or even a star field.

There have been a number of attempts to form taxonomies, or categorizations, of visualizations. Most software packages for creating graphics, such as Microsoft Excel focus on the type of graphical element used to display the data and then sub-classify from that. This has one immediate problem in that plots with multiple elements are hard to classify (should we classify a chart with a bars and points as a bar chart, with point additions, or instead classify it as a point char, with bars added?). Other authors have started with the dimensionality of the data (one-dimensional, two-dimensional, etc.) and used that as a basic classification criterion, but that has similar problems.

Visualizations are too numerous, too diverse and too exciting to fit well into a taxonomy that divides and subdivides. In contrast to the evolution of animals and plants, which did occur essentially in a tree-like manner, with branches splitting and sub-splitting, information visualization techniques have been invented more by a compositional approach. We take a polar coordinate system, combine it with bars, and achieve a Rose diagram. We put a network in 3D. We addtexture, shape and size mappings to all the above. We split it into panels. This is why a traditional taxonomy of information visualization is doomed to be unsatisfying. It is based on a false analogy with biology and denies the basic process by which visualizations have been created: composition.

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