I was interested this morning to see Last.fm’s visualisations of the effects of physical events on their listening data (courtesy of Flowing Data).
The graphs show events such as the death of Amy Winehouse or the birth of Jay-Z and Beyonce’s daughter Blue Ivy Carter prompting spikes of interest in these artist’s work that show up in the listening data of services such as Last.fm.
These are obviously aggregated and talk about events in the lives of the artists concerned, but they’re constructed through the listening habits of thousands of individual listeners and such data would make fascinating portraits of individual lives.
In this post visualising his own listening habits, Gavin Wray provides both an example and instructions on how to produce visualisations from data collected by Last.fm.
This example simply visualises counts, but could tell us more about patterns in his life through his music listening habits. Similar data could be provided by other services such as Netflix or Spotify. Facebook Timeline represents an already aggregated source of data on all these services.
These are all opportunities for longer term studies that design researchers could accomplish relatively easily and relatively inexpensively, and could go some way to alleviating the ‘snap-shot’ nature of what is often practically required from design projects. They also obviously raise a number of issue for how we think about and negotiate participation with those whose lives we are researching.
I don’t believe these issues are irresolvable however and I’ve setup the Last.fm scrobbler on my iTunes this morning to start gathering my own data. I’ll post back in a couple of months when I have some interesting stories to tell with it!