Okay, so term has started now and it seemed an appropriate place to stop data collection, with some 28 people having kindly taken part – thanks to all. Since then I’ve been busy starting work on the analysis. On the Digbeth project I left a lot of this to Cosmic, but this time round I’ve been the one who has grappled with NVivo. For those who don’t know, NVivo is a piece of software which allows you to identify patterns, topics of conversation and themes within interview transcripts. Well, actually it does a whole lot more than this, but that’s what I’ve been doing with it.

I’ve not used NVivo since I took a training course back in, heck, early 2001. Back then I kinda saw the point, but didn’t really have an application for it. I got the latest version onto my shiny new computer and put all the interview transcripts in to start work. Almost instantly I was a complete convert – it’s an amazing piece of kit. Unfortunately it does require that you spend a lot of time sitting working through your transcripts. This is mostly pretty tedious – you start to decide a bunch of different categories, highlight text and ‘code’ it into that category. Categories might be, say ‘traffic’ or ‘senses’. Then you refine these, breaking them down into subcategories. So for ‘traffic’ I talked about things like pedestrians, other cyclists, danger and so on. By the time you’ve gone through all your transcripts you’ve got a bunch of different themes identified and you go back through making sure that the first few you did are coded against the themes you’ve identified by the end.

Like I said, pretty tedious.

What you get at the end, however, is a really interesting breakdown of what people were talking about and the common themes that keep emerging. This is particularly interesting on a project like this one where I didn’t give the participants any guidance about what to talk about aside from “what’s going through your head as you ride”. It’s a good way of starting to find out what kinds of things are really important when cycling. Or when commuter cycling at least. You can also analyse these things against the characteristics of your participants. This allows you to answer questions such as whether women and older people talk about ‘danger’ more than young men.

I haven’t done this bit yet and it may not turn out to show anything particularly interesting. One of the things about having this kind of software is that you do tend to play around a bit, looking at a whole bunch of different things because you can do quickly what once might have taken a couple of days so you might not have bothered. I have, however, finished the initial coding, a mere week and a half after I started on it (amazing how much you can get done if you close your door and turn your email off). Here’s a snapshot of the particularly interesting themes which came out strongly from the 28 participants:

Theme Number of participants commenting Number of comments
Comparison to other transport modes 17 27
Cycle infrastructure 24 116
Road surface 20 59
Animals 17 56
Weather 28 98
Hills 25 68
Accidents 7 10
Speed 22 48
Pleasure 23 49
Smells 9 17
Traffic danger 21 61
Junctions 24 76
Other cyclists 23 45
Pedestrians 26 81

Now there’s no getting around the fact that reducing complex, dense participant narratives down to a series of themes runs the risk of oversimplifying and concealing major parts of the story. Clearly one has to return to the quotes to get at the real depth of the material, but it’s useful to be able to zoom out – to use a mapping metaphor – and get a sense of the broader patterns at work.

On the subject of maps, I’ve reworked the cycling maps page on the site. All the transcripts have moved to a separate page and the top maps page is playing host to the analysis. So far there’s only three things there to look at: places where people rang their bike bells; places where people talked about different animals; and comments people made about pavements (mostly about riding on them). I particularly like the animals map downloaded into Google Earth, seeing the names of different animals floating across the landscape. As always with the ‘public geographies’ approach I’m posting up the analysis as I do it.

Got to get a shimmy on with the analysis too, as I’m presenting preliminary findings at a departmental lunchtime seminar on 26 October. This seems like a ways away, but you’d be amazed how many utterly pointless meetings I have to sit in between now and then which get in the way of analysing/thinking/writing. Ah, the joys of a real job…