December 4th was International Open Data Hackathon. Groups around the world got together to see what they could do with open data: scrape, viz, reimagine, play, tinker. Anything you could imagine, using any kind of open data.
Around 30 people gathered at the offices of White October for the Oxford event. I went along with my Data Viz hat on, armed with laptop and Tableau v6. A summary of all the projects we worked on can be found on Tim Davies’ blog. I’m going to focus on the work I did. Zarino Zappia (@zarino) and I first tried to do something with the Mozilla browser usage competition, but the datasets were just too big. Sure, Tableau can handle it, but our laptops didn’t!
Instead, I got together with Iain Emsley (@iainemsley) and Al Power (@alpower) to work on Arts Council Funding Data. Working with data from data.gov.uk, we got to work. By the end of the day, I’d come up with the following viz:
(note – this is a static shot – click to see the full screen version; I didn’t have time to resize it to fit to the blog):
What did I learn?
1. Open data is fun
Hacking open data is great fun. My view blended the arts council data with a full list of constituency names and latitude/longitudes that we found on Google Fusion.
2. Open data is frustrating
There’s so few standards, and often so many hoops to jump through before you can get going.
3. Seriously, Tableau 6 is bloody amazing
I know I’m a Tableau lover, but, really, it was a perfect tool for this. I had most of the viz up and running within a couple of hours. Blending the funding data with constituency data was instant. And just about everything I did was with the mouse. Some of the other groups created some great stuff, using code libraries and their excellent programming skills, but Tableau just lets you explore, shows your results instantly. There’s no script to code, no alt-tab-refresh to test every CSS tweak you make, and no wizard-type work where you change 5 parameters before seeing a result.
4. This viz could be just the start
Now we have the constituency locations, it would be trivial to start blending these results with any other metric (population, employment, etc) to start to gain some huge insights.
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