In his dConstruct talk, Steven Johnson used Jane Jacobs’ phrase “the eyes on the street”. He was speaking about Outside.In, and the experience of being away from home, but of knowing what was going on in your area. His example was being on holiday, but of home not ceasing to exist, because you can see (or see evidence of) other people being there, talking about it, photographing it.
Of course, all this evidence takes the form of data, which made me think about the city as a metaphor for the data itself. There are areas where there are many eyes on the street – the populous areas that are mentioned on services like Outside.In, and the data we see and are asked for every day. But there are also the unseen areas – maybe shortcuts only a few people know, maybe places that aren’t a shortcut to anywhere you want to go, so you’d have no real reason to see them.
Except, there is a reason to see them. I grew up near Edinburgh, and early this decade quite a large part of the city caught fire. Main streets were inaccessible, and everyone had to find a new walk to work – probably one that involved tiny side streets that had never been intended to hold the volume of people that were using them. What had previously been seen only once or twice a day became a main thoroughfare and a new part of people’s map of the city.
In the same way that a lot of people found a city they thought they already know, I’m interested in what happens when we apply that idea to a dataset we think we already know – what are the limitations that force us to explore usefully? What are the weird, seemingly unimportant data that can join up the areas we already know, and how do we know where to look for it? In order to be truly useful eyes on the street, we need to be able to take the scenic route, or shortcuts, or any other route that will be fun or illuminating for us and the people we speak to.