The examples are rudimentary – stuff I've assembled for my own use, rather than robust tools for the wider community. If they have any value I hope it will be as prototypes for something more polished.
There are about 385,000 retail properties in England that are missing from OSM, and the obvious way to help contributors is to point out where they are.
To help achieve the most rapid improvement across the whole country I have tried to find dense retail concentrations that haven't been thoroughly mapped yet.
These are the biggest concentrations of unmapped retail property in England and Wales: about 1,000 of them, each with an average of 100 missing retail outlets across an area of under 2 sq. km.
I've used a mix of Food Hygiene Data, Non-Domestic Rates, population data, and various other statistics to identify concentrations of retail outlets at a local level. I've done this for England and Wales. The same basic technique should work in Scotland because similar data is available, but the structure of the census geography, and data on non-domestic rates for Scotland is quite different, so the process needs tweaking, and I haven't got round to that yet.
My formula for estimating the number of retail premises at a local level can probably be improved, but it will never be perfect. At this stage I don't think it is good enough to reliably identify areas that are almost complete, because that needs more precision. But I think it is good enough to flag up areas that are far from complete. Contributors who are looking for significant concentrations of missing retail outlets should be able to do a quick check on the area. If it still looks empty on the map, they can head there with a reasonable expectation of adding enough new retail outlets to make the trip worthwhile.
Feedback based on local knowledge would be welcome, to help refine this a bit more.
Helping contributors to find nearby concentrations of missing retail outlets is one way to quickly increase the overall volume of data. A different starting point is to assume that thorough retail coverage in some areas has a higher value to data users than adding missing shops elsewhere. On that basis we may want to encourage contributors to concentrate first on mapping areas which we think have the highest potential value.
This example picks out a limited number of smaller towns and cities where OSM data might have high value (e.g. to students or visitors).
- blue already contain more than 75% of my estimated number of retail outlets
- green contain 50-75% of my estimated number of retail outlets
- orange contain 25-50% of my estimated number of retail outlets
- red contain less than 25% of my estimated number of retail outlets
Each area is intended to cover a manageable size: one where a few contributors should quickly be able to bring retail content up to an impressive level. Larger cities are excluded on the basis that they justify a more systematic approach. My list is bit arbitrary – it is intended to cover a mix of different towns of roughly similar size, distributed across the country. Are these really the areas where OSM retail data is likely to have most value? I doubt it, but that might be a useful discussion point in its own right. For each suggestion of a settlement that should be added, please feel free to suggest one that should be removed.
I can only assess how useful these estimates might be in areas that I know fairly well. Feedback on any unexpected results would be useful: to better understand where the technique can be improved.
Feedback to contributors
All contributors deserve to see the results of their work. But not all retail information is rendered on the standard map. And in my view it never can (and shouldn't) be. So to encourage contributors I would like to see a decent alternative to the standard map which shows more complete retail information. When I want to check specific content of the database I use either a data extract, Overpass, or the “Map Data” overlay on the standard map view. I'm happy to do this, but for many contributors (and particularly for novices) none of these techniques are particularly user-friendly. I suspect this is beyond my own technical capabilities, but there are examples (based on various data extracts) that illustrate the kind of thing that can be done.
When mapping retail areas there will normally be some shops already recorded in OSM, which need checking. Alongside other existing features such as road junctions, these also provide reference points for adding new data. When surveying retail premises, it's handy to have a crib sheet to hand, on which to collect notes of any changes, which shows the current state of the data. This needs to show every relevant feature in the database, including some which won't be rendered on the standard map.
Below is an example generated (automagically, with some rather clunky SQL) from OSM data for Winchester High Street (the pedestrian part). It starts at the western (top) end.
I've set this up to collect any shops, amenities and offices within 25 metres of the highway centre line, and display them in order. This simplistic approach only exhibits the most basic information, and includes more features than I would really want: including shops beyond each end of the central line, up side streets, and occasionally from a nearby street running parallel. But it's easy enough to cross out any unnecessary entries. To allow for some additions there's an additional spacer inserted every 20 metres (roughly twice the width of a conventional shop front). I find sheets like this speed up the data collection process and make it easier to add notes.
I hope I've made a clear case that across most of the UK adding missing retail data is a higher priority than cleaning up tagging inconsistencies. However, this isn't true everywhere, and pointers to inconsistencies could help contributors to clean local data.
Some basic consistency checks are easily carried on Overpass:
- amenity=pharmacy & dispensing=no: http://overpass-turbo.eu/s/aEc
- shop=cafe: http://overpass-turbo.eu/s/aEg
- name=Argos and shop not = catalogue: http://overpass-turbo.eu/s/aEW
- shop=fast_food: http://overpass-turbo.eu/s/aEV
- amenity=optician: http://overpass-turbo.eu/s/aEl
- shop=supermarket without building tag: http://overpass-turbo.eu/s/aEp
- shop=unknown: http://overpass-turbo.eu/s/aEr
- some basic spelling mistakes (shop tag includes upper case or space): http://overpass-turbo.eu/s/aEs
But this isn't ideal for finding all quirky data within a local area, and finding more complex inconsistencies sometimes involves extensive processing that isn't really practical interactively. Overpass isn't the ideal solution here, but it is possible to do more crunching on a data extract. Here are some examples. Unlike Overpass, anything here that is fixed won't be quickly updated in the overlay (some of these quirks are already fixed, which could get annoying). Note that, for the sake of simplicity, this overlay only contains some of the features in the UK that exhibit these quirks.