Data byline: “How does the London Marathon compare to other races worldwide” – The Guardian

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I wrote some of this and also put together the basis for the Tableau visualisation at the bottom.

What I found quite interesting was how the larger marathons seemed to spread by continent. They were being founded in the US sporadically for the first half of the twentieth century and then suddenly there was a rush in the late 70s and early 80s in Europe. A similar thing happened later on for Asia.

The sample size is not that big because obviously there are not that many marathons with over 10,000 people running but it’s still quite a nice thing to watch on the map. I could get a Tableau animation in worksheet view but not in dashboard view. Anybody else have this problem?


My first data-vis

This is my first attempt (except through the wonderful of converting data into an informative data-vis.

I’m a student on the Interactive Journalism MA at City University. Part of the course includes a module taught by Paul Bradshaw on Data Journalism. We’ve already learnt about scraping (covered marvellously by my course mate Henry Taylor) and have been encouraged to blog about our further forays into Data Journalism.

I thought I’d hit the ground running with a basic data-vis using Gephi – a network visualisation app. I got the tech and the idea from Paul’s Online Journalism Blog, where he also linked to this excellent walkthrough. Please forgive that the data-vis – so closely resembles the one on – it’s my first time.

Using the netvizz app lets you rip the data from any Facebook network you are a part of and I opted for navel gazing by analysing one of my university ones.

I figured that the general “City Journalism Postgrads 2012” group would yield the most interesting visuals. The journalism course includes numerous pathways (Newspaper, Interactive, Broadcast and more), so it would be interesting to see who is connected to whom within the wider group.

So here we go:

The connecting lines denote friends and the groups are clustered together within the networks in which they have the most connections. The larger dots represent those who are best networked. Further out, you can see the few small lonely souls that have not added anyone (the smallest dots). There is also a four colour scale with green representing the most connections, then white, then pink, then grey for the least.

Gephi allows you to include the name of everyone (and edit the positioning to make it more viewer friendly). I opted against this for two reasons. Firstly, it unfairly showcases those of my peers who have opted against connecting with others on Facebook. Secondly, I couldn’t really see any immediate analytical purpose in doing so.

I wanted to spend more time running through the data before I came to any real conclusions about the visualisation, but one thing sticks out: several mini-networks have gathered together which correspond well to the different pathways on the course although some pioneering spirits have reached across the divide – I’m sure they know who they are.