Urban Areas 4 : Derivation from OpenStreetMap using road density

Another variation on the theme from the last post: this time looking for some measure of road density.

Comparison of Urban Areas derived using "block method" and gridded road density.
Only grid squares with over 500 m of road included.
The area shown is around Butler, Butler Co, PA

The easiest way is to sum road lengths in individual cells. The cells have to be quite small (say 250 metre square) to achieve the resolution desired. I've excluded link roads and motorway & trunk highway classed in this calculation.

Urban Areas 3 : derivation from OSM using residential blocks

View S from the Cathedral of Learning in Oakland, Pittsburgh,
showing some urban areas used as tests in this post.

The incised valley of the Monogahela in the background contained railways and steel works. The plateau beyond has residential suburbs of Pittsburgh. To the left foreground are the woods and ravines of Schenley Park, with a residential area beyond. Source: Zack Weinberg via Wikimedia Commons CC-BY-SA

One of the obvious features of the highway network for the USA on OpenStreetMap is that road density is much higher in built-up areas. I started looking at how to measure this, when I recalled a method for identifying city blocks introduced to me by a Brazilian user of OpenStreetMap data.

Residential Areas for Butler Co, Pennsylvania, identified with the block method
from OpenStreetMap data. Orange line outlines Butler County.

My idea was simple, a greater road density implies smaller areas for the polygons enclosed by a set of roads. By choosing some maximum polygon size, one should be able to pick out urban areas.

The method itself is also really quite simple:

• Take the main road network for some area and make a union of it (which will be a MULTILINESTRING).
• Polygonize this data, and decompose to individual polygons.

In Lucas' implementation the first step is done by municipal areas. I wanted to try the approach for a whole state without using administrative area data. I therefore once again turned to my trusty standby of using a gridded method.

Urban Areas 2 : Derivation from OpenStreetMap using Residential Roads

Street corner, Retiro, Buenos Aires
(Libertad/ Juncal)
CC-BY-SA, the author

Following on from my last post I have now been looking in more detail at how one might start using OpenStreetMap (OSM) to create a global dataset of Urban Areas. As OSM does not have any widely used notation for urban areas I have been looking at several ways in which other OSM data can be used to identify such areas prospectively. In this post I look at the use of residential roads (and I'm not the first to do so). Later posts will look at other techniques.

Buenos Aires and hinterland, showing comparison between urban polygons
derived from OSM (green) and the Natural Earth data (light brown).

I have chosen the following places as suitable test areas for these investigations:

• East Midlands of England. Not only my home turf, but also a well-mapped area with extensive use of landuse tags, and in excess of 99% of all residential roads. In addition Ordnance Survey Meridian 2 Open Data contains a layer corresponding to urban areas which provides an excellent control for checking results from this area.
• Pakistan. Not only one of the most populous countries in the world, but one of the least well mapped in OpenStreetMap. Pakistan is a likely candidate for cities which are barely mapped. I would also expect other very populous Asian countries (notably China, India and Bangladesh) which are poorly mapped to be similar to Pakistan.
• Nigeria. Similar criteria to Pakistan: the most populous country in Africa. The .pbf file for Nigeria is approximately 50% larger than that for Pakistan, but both are smaller than that for Lesotho with a population of 2 million compared to 180 million (Nigeria) and 200 million (Pakistan).
• Côte d'Ivoire. Close to Nigeria, but a place which I know has an active OSM community. Quite a number of mapping activities. (Note to Geofabrik, it's not called the Ivory Coast any more).
• Argentina. Latin American cities are often laid out in a grid, nowhere more so than in Argentina. The prevalence of the grid system, and my believe that the urban road system is largely complete were reasons for choosing this as a Latin American example. My own experience of travelling in Argentina after SotM-14 suggests that, for the most part, urban road systems are mapped. One known gap, the newer western suburbs of Ushuaia has recently been rectified by the kind provision of aerial imagery from the Argentine National mapping agency.
• Pennsylvania. It was essential to include some US data  because of the TIGER import problem: all rural roads being tagged residential. Since I spent part of my childhood in Pennsylvania it is also a place I know and which I have edited (sporadically) to improve the rural road network.

Briefly I expected the following: good urban areas for the East Midlands and Argentina (i.e., better than Natural Earth (NE)); middling to poor for the three developing nations (gaps relative to NE, but in some cases better precision); hopeless for Pennsylvania.

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.