As the weather improves we are all going to be out and about tracing roads for OSM. So there are various discussions, and work under way to help find, prioritise and then fill the most important gaps in the map. Larger towns and bigger areas that need attention are fairly well known, but experience suggests that it can be more difficult to find smaller and more local places where a short session could make a difference quickly.
So this is an attempt to help, at a bit more of a micro-level - http://www.reedhome.org.uk/Documents/osmembed.html?kml=KML/osmcategory.kml (to view the same thing on Google maps use this http://maps.google.co.uk/maps?f=q <http://maps.google.co.uk/maps?f=q&source=s_q&hl=en&geocode=&q=http:%2F%2Fww w.reedhome.org.uk%2FDocuments%2FKML%2Fosmcategory.kml&sll=51.525483,-0.74680 2&sspn=0.01287,0.033023&ie=UTF8&z=6> &source=s_q&hl=en&geocode=&q=http:%2F%2Fwww.reedhome.org.uk%2FDocuments%2FKM L%2Fosmcategory.kml&sll=51.525483,-0.746802&sspn=0.01287,0.033023&ie=UTF8&z= 6 ) Broadly - green areas look as though they are already fairly well covered, red areas look as though they would benefit from some attention, and the rest are grey. WARNINGS This is very much a first cut, pre-beta, etc. We know there are places missing, some gaps in the data, and we are aware that it doesn't always get things right (we think it does often enough to be useful). It really cannot tell how well an area is mapped from the data. It can only try to find areas that look a bit thin - and some areas look thin when they are not. At the moment we are concentrating on roads, not the full list of features in areas that are most thoroughly mapped. As a result this sometimes flags up an area as "poor" when in reality it has been perfectly well mapped. For example, there are two areas near here that a lot of work has gone into, where the classification is not right. So if your favourite areas shows up wrongly, please don't take it personally. Still, it seems to point in the right direction quite a lot of the time, and it's offered up on that basis. In my own area (which is already fairly well covered) it has flagged up some towns that I already knew were a bit thin, and another half dozen options that I can reach fairly easily, but didn't know about. If it looks as though it will prove useful then future plans will address some of the current limitations by refining the borders, filling gaps, correcting errors in the underlying measures, and tweaking the arithmetic. However, some mis-classification will always be inevitable. Read on to see why. THIS IS HOW IT WORKS: We start with a list of about 1,600 UK settlements, and a figure for the population that lives there. Baring a few errors and omissions, the settlements are the same ones that Cyclestreets uses for local areas - http://www.cyclestreets.net/area/ We then try to find a boundary for each of these settlements. This is based either on the local authority admin area where there is one, the naptan pay-scale area if there is one, or if all else fails, a guesstimate of how big the settlement must be based on population density. (FWIW we already realise that some of the pre-defined areas are too big, some of guesstimates are off-centre, or the wrong size, and the guesstimates don't work well on the coastline. But it's a start, and most settlements don't look too far out.) Where we know the actual length of roads in a settlement from Department for Transport data we use that to classify the area, but this only works for bigger towns and cities, so for the rest we are trying to figure out how well they are covered without knowing the true length of roads on the ground. At the moment we do this using various ratios. Within each settlement boundary we measure the length of roads in the OSM database, and from that we calculate three measures: the length of roads per sq km (the road density within the settlement), the length of road per head of population ,and the proportion of roads that are major (primary, secondary and motorway). The underlying hypothesis is that a thoroughly mapped area should have a relatively high road density, plenty of road per head of population, and a relatively low proportion of major roads (because it's the unclassified and residential roads that tend to be missing, not the major roads). However, there are anomalies - some areas are thinly populated, some are at the intersection of a lot of major roads, some have more tightly packed houses (so a high road density), while others have big gardens (and hence a low road density). So inevitably our hypothesis sometimes breaks down. To avoid tripping over some of the more extreme cases, we therefore highlight as "good" only those areas that fall into the top quartile on at least two of the ratios, and to be classified as "poor" we pick only those areas that fall into the bottom quartile on at least two ratios. Everywhere else is in the middle. That covers complete settlements. On top of that we have plotted all the bigger residential areas (landuse = residential) where there don't seem to be many roads. We can't know the population of these areas so we just highlight those with a low road density (road length / area). If the road density in a residential area is above the threshold, or if it is a very small residential area (where the measures can be unreliable), then we just ignore it. We have only plotted the bigger, low density residential areas. One final caveat - the base data on which this is all based was extracted several weeks ago, so it's a little bit out of date. An updated version will follow when time permits. IN CONCLUSION We have several ideas on how this can be improved, based on earlier suggestions. We welcome more comments and ideas. Even more importantly, we welcome more roads on the map. So most of all we hope this helps members of the community find some handy places where they can quickly make a difference by plugging a few gaps. Remember that it's all a bit hit-and-miss though. It's probably a good idea to check some other sources as well before rushing out with the GPS.
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