Category Archives: Example
I’m a member of the American Statistical Association’s “Statistics in Sport” section (http://www.amstat.org/sections/sis/) and I’m also British by birth, so Andy Murray’s success at Wimbledon this year was interesting to me for two reasons. I took a look at some of the data on Murray (collected by IBM’s SlamTracker initiative — http://2013.usopen.org/en_US/slamtracker/ ) with a view to doing a little visual analysis, so now I have another reason to be interested …
I found some data on his performance over a few years leading up to Wimbledon 2013 and wanted to look at trends. Now usually I prefer to create several linked visualizations and look at them together, but for this data I found that several of the stats I was interested in worked nicely when plotted in the same system. Here’s what I came up with:
Wikipedia Recent Changes Map shows a good example is a good, clean, simple implementation that addresses the question:
“How is Wikipedia being Edited right now?”
Some of the features of this visualization that work:
- Filtered data — the potential data size is huge, and grows as we wait, so the display only shows the most recent events, both on the map and the list below it
- Multiple linked views — data is shown geographically on the world map, and as a list of events below. This is preferable than trying to have one combined view as each view supports a different set of tasks, and combining them would complicate those tasks (WHERE are the changes coming from? WHAT is being changed?)
- Not using graphics — the report on what has changed is a simple scrolling text view; since the dat is textual, and it is ordered, a simple list of text makes sense.
- Different fade-out rates — Using the color for the country to show the most recent changes, and then fading that out in synch with the text description, focuses attention on changes very well. Leaving the dots behind for the changes allows us to keep a longer-term trend in mind.
As a map geek, I might prefer a different projection for the whole earth map; maybe WinkelTripel?
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.