Tony’s Top Twenty Tracks for 2008

I think we’re past the point where you can do reviews of 2008 (look forward, chaps, forward) but for the sake of posterity I’m posting my top 20 most listened to tracks of that year, according to

Image: My top 20 tracks for 2008
Image: My top 20 tracks for 2008

So, basically, a big year (in my head) for M83. Burial, Crystal Castles, Santogold and The National were my big discoveries of the year (the latter two don’t feature in his list, but may have done if the rest of December’s data was in the mix). Portishead’s return was utterly triumphant – lovely…

Actually this chart goes up to December the 6th, which is when my MacBook Air issues became so bad I stopped using it (more or less) and so my iTunes stopped reporting back my listening habits. The MBA’s still with the fellas at the solutions-inc support centre (in fairness they’ve only had the machine since just before Christmas) who are consulting with the dark masters at Apple about what’s plaguing the thing.

: : I’m Blipping out the tracks on my profile if you fancy a listen… Very self-facilitating-media-node, I know… data visualisation

It’s been a while since I posted some cool data visualisation things here, so here’s a treat from Anonymous Prof, who has been busy mapping


The post about the networks is worth a read. Anonymous Prof used the API to gather his data and started to build visualisations of the networks, including some interesting observations about “outliers”, small networks of users isolated from the main community.

And his ambitions don’t stop there:

I’m also beginning to collect the listening history of users (again thanks to the wonderful API) and hope to examine music listening patterns as they relate to the network. That’ll be a much bigger problem because of the volume of data. I collected a small amount just to see what it would look like and for the 183 users that I checked, I already have 1,179,480 track plays. Scaling up to ~300k users is a bit much. Regardless, I may use the friends data to identify a sub-network of friends and track their listening patterns to see how they influence one another.

Now that could be seriously interesting – I’m subscribed and look forward to hearing more about his adventures with that network.

Via Data Mining