This is one of those things that you do, then you are not quite sure whether it was worth it or not.
I calculated the road density for each of the "Pay scale areas" that arrived with the OSM Naptan import. I thought that these would be fairly thickly populated areas, so a low road density would highlight places where there were roads missing from the map, and hence help to prioritise more attention.
Road density is calculated as the length of roads in km, divided by the area in sq. km. I've included motorways (and links), primary, secondary, tertiary, unclassiifed and residential roads. I've not included cycleways, paths and bridleways, etc. The data extract I used is actually a few weeks old, but that shouldn't make a lot of difference (unless someone has added a mass of new roads in a particular area).
The highest quartile of areas (by road density) are shown in blue, then the following quartiles in green, orange and red. In other words red areas don't have many roads for the area, blue areas do.
Looking at the result, things obviously are not as simple as I expected. Anyway, for what it's worth, this is what it looks like. Maybe someone else will spot a way of making use of the information.
I calculated the road density for each of the "Pay scale areas" that arrived with the OSM Naptan import. I thought that these would be fairly thickly populated areas, so a low road density would highlight places where there were roads missing from the map, and hence help to prioritise more attention.
Road density is calculated as the length of roads in km, divided by the area in sq. km. I've included motorways (and links), primary, secondary, tertiary, unclassiifed and residential roads. I've not included cycleways, paths and bridleways, etc. The data extract I used is actually a few weeks old, but that shouldn't make a lot of difference (unless someone has added a mass of new roads in a particular area).
The highest quartile of areas (by road density) are shown in blue, then the following quartiles in green, orange and red. In other words red areas don't have many roads for the area, blue areas do.
Looking at the result, things obviously are not as simple as I expected. Anyway, for what it's worth, this is what it looks like. Maybe someone else will spot a way of making use of the information.
2 comments:
I think it's a nice analysis which could be very useful to assess the completeness of the map.
However, your assumption that the Naptan areas indicate urban, high-density areas does not always hold. For example in Edinburgh, the Naptan area extends way beyond the city, giving you a low road density. Edinburgh in fact is well mapped out.
It does work for, for example Telford (needs mapping badly) versus Birmingham (quite well mapped out).
One could of course, use another dataset indicating urban areas and re-do the calcs.
Thanks for that, and I agree, but I hadn't really thought it through, and didn't anticipate the problem until I did the analysis. Edinburgh is certainly a good example of where it breaks down.
My previous attempts to do this on urban areas haven't worked too well, but it's worth revisiting. Any good ideas on how to get outlines of urban areas? Also there are various other ideas coming up on the Talk GB lists.
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