Urban Areas: a meditation on why simple global geographical datasets are so poor

Puerto Vallarta, an aerial view of an urban area missing many roads on OpenStreetMap.
The area in the middle distance away from the sea was particularly lacking.
Fortunately the centre of the hurricane didn't pass over this area.
Source: Wikimedia Commons, (c) CC-BY-SA

The other night, as Hurricane 'Patricia' bore down on the Pacific coast of Mexico, I had a twitter conversation with Bill Morris and others regarding how well mapped Puerto Vallarta was on OpenStreetMap. (BTW: I'm sure it's much better mapped now).

Latin America is a weak spot for @openstreetmap. A town in the #Patricia track: https://t.co/pk4UYHHan3 pic.twitter.com/3t7ZQkWkWz

— Bill Morris (@vtcraghead) October 23, 2015

Of course, OSM is about fixing things, so I carried out the conversation in between adding around a hundred streets to the city. However the really interesting question was this one:

@vtcraghead @hotosm @openstreetmap Would there be some sort of algorithm that can be run to detect built up areas from imagery not mapped?

— Antonio Locandro (@antoniolocandro) October 23, 2015

Whilst at breakfast I thought a little more about this. I decided it ought to be possible to do something fairly simple with data which already exists.

Retail Outlets on OpenStreetMap: Cartograms, and Patchwork Quilts

I enjoyed the process of creating a cartogram from OpenStreetMap data a couple of years back, even if it was somewhat tedious. However two things stopped me from taking it further: the QGIS plugin I was using does not work with later editions, and I really wanted something a little more refined.

Cartogram of Local Authority areas in Great Britain based on numbers of pubs on OpenStreetMap
Created using ScapeToad, this is a simple, and naive, cartogram.