What data do we need?

After yesterday’s workshops (and tasty meal at GBH) we’ve got some good ideas to go forward with.

Central to our project is how we make the dataset meaningful, and to do this effectively we need to look at grading or rating things like gradient, paving surface and kerb height according to how difficult to pass they are.

How we determine this difficulty is helped by the Department for Transport and their document on ‘inclusive mobility’ which gives guidance on things such as the necessary pavement width for two wheelchair users to be able to pass, and how much of a gradient is too much.

When it comes to kerb height, what is desirable? For getting across a street then the lower the better – but what about accessibility at bus stops? There a smooth ride would need a kerb as high as the bus floor – something seen increasingly more often around town. This suggests a need for a degree of personalisation in route planning.

Different types of paving surface present another kind of challenge: how to represent the variety of surfaces on our streets? From tarmac to flagstones to cobbles, these surfaces create differing levels of challenge to anyone attempting to navigate them.

One possible method of classifying these obstacles could be a traffic light system – where red is impassable, amber is tricky-but-doable and green is easy-access – something that would be recognisable, simple and appealing. However, this simplicity would be its weakness also in terms of the kerb and surface issues, where certain levels of ability would not find a problem.

Another way might be to class paths etc on a scale of difficulty from 1 to 10. This would enable the user to set a level of ability and see the routes accordingly. The idea of being able to tune outcomes according to need is certainly an appealing idea, and lends itself well to the concept of accessibility for all.