Spotted on Wired, the developers responsible for Last.fm's 'VIP playground' have announced the launch of a visualisation service that plots subscribers' listening habits over a certain time period onto a 24 hour clock graphic. This follows other last.fm graphic developments such as the listening trends streamgraph (read more about streamgraphs here) and music universe.
I have previously praised radial plots when writing about the graphical history of the FTSE produced by Jeremy Christopher. However, I have also found reason to identify failings with this approach, like the GE 'Cost of Getting Sick' chart (read more on EagerEyes).
Once again, I don't think radial plot approach works particularly well as an approach to effectively communicate the information. This shortcoming is probably best explained by the apparent motivation behind these graphics:
A bit less than a year ago we launched the VIP zone on our Playground, with the promise that we would keep adding fancy visualizations to it as a special treat for our loyal subscribers.
To interpret the visualisation, the red and green bars represent weekday and weekend listening, respectively, and the longer the clock's hands the more the listening was focused around the time to which they point.
To be fair it is clear where the clusters of peaked bars are in relation to the 24 hour clock. However, the overlaid plotting of weekday and weekend activity is messy and detracts the user from being able to intuitively judge the respective bar heights. Accuracy of reading is not a necessity here, it's more about the patterns hence the lack of scale, but the lack of subtle concentric grid lines makes it difficult to compare values, should you wish, at either side of the clock face. The clock hands are an unnecessary and confusing addition presumably representing a peak of listening activity that could have been derived from the relevant bar trends anyway.
Unquestionably, last.fm have created a visualisation that is 'fancy' but it would be far more effective to plot both series onto a small multiple pair of bar charts or area charts. I'm sure I'll be labelled a killjoy but if you're going to produce something like this to help users explore and unearth insights from their listening behaviours, a potentially interesting dataset, I think it is disappointing to undermining this opportunity through inefficient visualisation approaches.