Today, the Economic and Social Research Council (ESRC), my funding provider, are heading to the University of Surrey for their annual visit to the South East Doctoral Training Centre (SEDTC), my funding organiser/deliverer. Whilst I will not be in attendance at the event, all researchers funded through the SEDTC have been asked to produce a poster about their work for the visit.
As I am still in my Master’s year, I have no doctoral research yet to showcase so my poster is about what I intend to research, how I intend to research it and its potential impact. Essentially a re-jigged and simplified version of my PhD proposal, it is provisionally titled – Running the City: Movement, Meaning, Experience and is available below (click to enlarge picture) or from the Talking tab.
I will be very interested to see how the project changes over its course and what the differences between what I proposed and what I actually do are. The proposal is very broad, leaving much room to follow the avenues I find exciting, to be inspired and generally explore this research topic – which is both daunting and thrilling.
If you have any questions about this proposal or any more of my work, then get hold of me through the Contact tab.
Jographies 
Fascinating idea for a PhD project. Have you thought about crawling e.g. Strava or Garmin Connect for additional data on running routes etc? This would help to get sufficient power for statistically interesting GIS analysis, and also evaluate potential Hawthorne effect.
Thanks Matt. Yes I am working towards doing that. Previous work has used MapMyRun but other sites I think offer more data. I also need to radically improve my GIS skills but I have 3 years to try and do so. The issues I’ve had previously are some ‘routes’ containing more data than others – i.e. some were mapped before of after the run, so just contain location data; whilst others were recorded on the go (via watches, apps etc) so contain speed, altitude, HR etc.
There is also an issue of relying on data gleaned from strava Nike+, Garmin etc. in that only certain types of runners use these technologies whilst many don’t – whose ‘geography’ would I be privileging, which runners are being excluded by doing so?
Yes – hard and interesting problem. If you could find out somehow the distribution of strava users in e.g. different socio-economic groups, and compare that with the socio-economic distribution of runners in general, you could use sample weights to correct for the mismatch.
Yes that sounds possible. I may go beyond socio-economic groupings though and start think about running identities/ways of running. So how does the use of strava etc differ between novice runners/fun runners/competitive runners/commuting runners/social runners etc. In many ways mapping and running ‘data’ is part of self-quantification – some runners will be more partial to this than others.
But many interesting questions. Looking forward to getting stuck in.