To mark the 10th anniversary of scrobbling, my colleague @stefansperber and I created this infographic showing how the top music genres have changed over the last 10 years. Stefan ran the data queries and produced vectors for the stream graph, I did the final infographic.

My dear colleague @dawhiting had the idea of looking at the scrobble graphs for the top 10 Christmas songs leading up to the 24th Dec. Over the last few years there has been a consistent spike. I did a bit of “pretty” work and now we have this year’s Christmas card design.

Continued from my previous post, my data team colleague Mark Levy has adjusted the mood chart to only show the percentage of moods in total music listening per week, removing the scaling that showed how much music a user was listening too. This makes it easier to see the variance in mood.

Here is my Moodmongr report or you can give the Mood report a try yourself.

Sketch for an idea for a hack day at Last.fm. The theme is “user profiles”. I’m working on a project with Mark Levy, who leads the Music Information Retrieval team, to chart the mood of a user’s listening over time using new audio analysis tagging. We will be testing it on the team first!

I call it MOODmongr

The results

In general, the moods were pretty even across the year’s listening of my test group; my team mates. This was largely due to the fact that we all have varied music tastes and use radio steams and playlists for variety. However there were a few spikes and points of interest:

Last.fm can already recommend the most compatible festivals based on your current music taste, but what about discovering new music? We decided to use a bit of 10% time* to see if Last.fm listening data could be used to recommend the best festivals for seeing the future stars of summer 2012.

@Omar711, Last.fm’s data scientist, started by looking at new artists playing in festivals this summer to see which have a high “hype score”. Hype is our measurement of how fast an artist’s audience is growing over a short period of time. Then Omar looked at historical data for all festivals over the last few years to see how many artists had become successful (i.e. grew in audience) directly following the festival. This gave us a ranking of how influencial festivals were in growing new artists. We pulled out the top 10 for our infographic, and then highligthed the artists with the most hype.

As we tend to call artists that have big audiences “stars” I thought I would use stars in my infographic (I find these dazzling leaps of lateral thinking exhausting). The hype scores would be represented as the brightness of the star. However, when I tried to convert the hype scores into percentages to scale the circles in my infographic, some were massive and other came out microscopic. So I called Omar over and he said “ah yes, skewed distrubution. Just use log or square root”.

Eh?

It must be strange for Omar to be working so closely with an idiot. A short math lesson later and I had a nice range of percentages to play with (and I felt a bit smarter, almost ready for my own PHD ;).

* staffers are given 10% of their time to work on self-driven projects, providing the work is related to music data (I have been told off for spending too much time working on a diorama of Jabba’s palace for my Star Wars figures)

My initial idea for the infographic was based around a map, however when the data was ready it turned out the festivals at the top of the list were mostly in the UK and Belgium.

It may not seem like much, and it’s no yellow pencil, but this means more to me as I LOVE the Guardian Datablog. It’s a shame the colour fooked up in the screenshot they did. Don’t now what happened there.

Some good tweet feedback too

“We don’t have the time for psychological romance”
– Larry Blackmon, Cameo

As my missus will testify, I’m not very romantic and greetings cards make me nauseous. So I wasn’t looking forward to designing a feature for Valentine’s Day.

Then I realised it might be interesting to use music data to see if anyone else felt the same or if Valentine’s day was full of hopeless romantics playing “Somebody To Love” by Jefferson Airplane back-to-back like saps.

So I went to see Omar…

Omar the Oracle

I don’t pretend to understand what Omar does, I like to think his job involves “running things through the computer”. He is always very patient with me, even when I ask him silly questions like: “Do you think David Hasselhoff’s audience was affected by the drunken cheeseburger + floor-as-plate incident?” (it did, the Hoff gained an extra 400 scrobbles that week). Omar was more than happy to dig into the Valentine’s Day stats, especially when I said I wanted to compare the music tags “romantic” with “sex”. I’m always running the word “sex” through the computer and it never takes long.

To get a clean set of Valentine’s data to analyse, Omar compared the listening behaviour on 14 Feb over a number of years to the behaviour on any other day of the year, normalised it to remove erroneous “new release” spikes, thereby sifting out the tracks unique to Valentine’s Day. Then we went to work with the location and genre tags.

This gave us a list of cities ranked from Sexy to Romantic and the proportion of sexiness for each.

Looks like I need to head to Fresno on 14 Feb!

See the full feature on last.fm