OSM Retail Survey: Part-9

False synonyms

True synonyms add to the confusion, provoke debate, and may discourage some data users, but in practice I suspect “false synonyms” are a bigger problem. By this, I mean tags that are used interchangeably by contributors, even when they are not true synonyms according to the guidelines. Again, we can use major chains to do some cross-checking of whether tags with similar meanings are applied consistently.

• Almost every major chain of pharmacies has a mix of outlets tagged as “shop=pharmacy” and “shop=chemist”. 
• Similarly “alcohol”, “wine”, “beverages” seem to be used interchangeably for chains of off-licences and wine merchants, with “alcohol” as the most common of these. The less common “off-licence” is not widely used on retail outlets
• For chains such as Ladbrookes and William Hill, “bookmaker'” is the most common, but “betting” and “gambling” are also quite common 
• There is a lot of overlap between outlets that are described by the relatively common “doityourself”, and the less common “hardware”, “building_supplies”, “trade”
• For “mobile_phone” the less common alternatives are “phone” and “electronics”. Tagging "electronics" could be a symptom of an evolving retail format. Phone looks like a false synonym.

The documentation in the wiki makes it reasonably clear that the above are not true synonyms, but contributors have treated them as synonyms in the sense that similar branches of the same chain use a mix of different values. As a result, data users are unable to tell where there is a true difference, and where there is imprecise tagging. In effect data users are being pushed to treat these as synonyms, even though they are documented as having different meanings.

These are examples of retail formats that contributors have difficulty with. Data users, those who maintain the documentation, and those who advocate changes to tagging need to be sensitive to where these occur. We'll look in more detail at some common examples shortly.

Multi-specialists

The above are all examples of specialist retailers. Multiple specialities are another area that give contributors a problem. Halfords is one of the most easily identified examples. How best to tag a store that offers both bicycles and car parts? The solutions that contributors have come up with include around 30 different variants:

• Choosing just one of the options: “bicycle”, “automotive”, “car_accessories”, “auto_accessories”, “car_parts” and ignoring any other area of specialisation
• Contributing a list of options separated by semi-colons: “bicycle;car_parts”, “car;bicycle” “bicycle; car_accessories”, “motor;bicycle”
• Using a more generic category: “doityourself”, “hardware”

The usual way to assign multiple values to a key is a list separated by semi-colons. In practice this is not widely used for shops (less than one in a thousand examples), but there are examples which give an idea of other multiple specialities that are giving contributors problems:

• “hairdresser;beauty”
• “kitchen;bathroom” 
• “greengrocer;florist”
• “dry_cleaning;laundry”
• “art;frame”
• “car;bicycle”
• “shoe_repair;key_cutting”
• “bicycle;car_parts”
• “tattoo;piercing”

Noticeably, these are all pairs. Happily, there don't seem to be any long lists of shop types. Contributors recognise that the intention is to record mixed types of speciality shop, not to list all the categories of good for sale.

The limited number of examples mean that these won't give data users a great problem. If they chose to ignore them they won't lose much data. If they prefer to break out the list then it won't give the much difficulty. More importantly, to my mind, contributors are sending signals here about retail formats that they find it difficult to categorise. This could be valuable information for those who maintain the documentation, and those who advocate changes to tagging.

OSM Retail Survey: Part-8

Consistency

One way to assess tagging consistency is to examine differences in tagging across similar outlets of the larger retail chains. Contributors don't always agree on how to tag similar shops, they don't always follow the guidelines, the guidelines aren't static, and they aren't always consistent.

Regardless of what the documentation might say, and the merits of any minority view, in practice data users will have to follow the consensus that has been adopted by the majority of contributors.

In principle crowd-sourcing will end up tagging most of a retail chain with the “correct” tag. By examining variations in tagging across a retail chain we can get an idea of the proportion of outlets that have been tagged according to the consensus, and how many fall outside the consensus. Data users will be able to accommodate variations, to some extent, but they won't be able to accommodate all of them.

• In the case of banks, for example, there is very little variation in tagging: 100% of Barclays,  HSBC, Natwest, and Lloyds / TSB branches are tagged “amenity=bank”. 
• In other sectors, Subway doesn't fall far behind the consistency of banks at 95% tagged “amenity=fast_food”.
• At the other extreme there are more challenging examples. Wilkinson's seems to be one of the more difficult chains for contributors to classify: “department_store” is the most common choice, but only accounts for 25% of examples. “hardware”, “variety_store”, “doityourself”, “supermarket”, “general”, “convenience”, “household” and “houseware” are also popular. Robert Dyas, with a similar retail format, faces similar difficulties. 
• In the case of Halfords 42% of branches are tagged “shop=bicycle” (which doesn't really capture their business format) and the rest use a wide variety of tags. 
• Argos has 40% tagged “shop=catalogue” and the rest a variety. 

In general the most specialised chains tend to be tagged more consistently, and the most consistent tagging of all is found within chains of smaller outlets, with a well-established, widely understood,  unambiguous specialisation (“estate_agent”, “funeral_directors”, “hairdresser”, “toys”, “optician”, “laundry”, and “travel_agency”).

Less consistent tagging is found in chains where the specialisation is more ambiguous (“gift”, “catalogue”, “accessories”).

There are many shops that are not part of a chain, and we can't easily assess how consistently they are tagged. But if we assume that the pattern of tagging inconsistencies across retail chains is repeated across the whole of the retail market, then we can get some idea of how consistent tagging might be overall. In practice there tends to be more consistency across larger chains, and less across smaller chains, so results vary according to how widely we cast the net. As a broad indication we should probably anticipate that something in the region of 20% of retail outlets have been tagged with a value that differs from the one that the majority of contributors would choose (and hence the value that data users would have to expect).

Some variation is inevitable: retail business models evolve over time, and vary from place to place; different contributors place different emphasis on different characteristics; tagging guidelines change as they are refined. However, if 20% of existing data is tagged with a value different to the one that most contributors would chose, then across England there are almost 30,000 retail premises in the database that data users will find it hard to recognise, and which should perhaps be brought more into line. After the 385,000 missing retail premises, it seems to me that this must rank as the second largest data quality issue.

Synonyms

Many community discussions of tagging inconsistencies revolve around synonyms. The controversy often lies in deciding when different contributors are using different terms to describe exactly the same thing, and when they are using different terms to describe subtle differences.

Any list of synonyms invites debate, but examples that are unlikely to be controversial, and where the difference is more than a spelling mistake would probably include travel_agent / travel_agency, newspaper / newsagent, jewellery / jewelry, and deli / delicatessen. I suspect that most would also count baby / baby_goods, seafood / fish / fishmonger, bathroom_furnishing / bathroom, beauty_salon / beauty, etc.  as true synonyms.

If this is anywhere near a complete list, then true synonyms do not look like a significant problem across all retail data. Including spelling mistakes they account for fewer than 1% of all shops in the database. However, they represent a higher proportion of data within some categories of shop, and they can account for a significant proportion of the more unusual categories.

The retail categories where synonyms are likely to present the greatest problem are where they account for a significant proportion of an important category. Everyone will have different ideas of what makes a proportion significant, and a category important, so it is worth considering a couple of real examples.

I reckon there are about 2,500 delicatessens in the UK, and I can find just under 500 in the database. Of those, 456 are tagged “shop=deli”, and 39 are tagged “shop=delicatessen”. Any data user who searches for “shop=deli” will miss 39 delicatessens in the database with the “wrong” tag value, and will miss about 2,000 delicatessens that aren't in the database at all (or at least not with a recognisable tag). Of the two, the bigger problem is surely the 2,000 missing delicatessens.

On the other hand, some synonyms have a more balanced mix of values. Out of 950 independent fishmongers in the UK, 80% haven't been recorded at all.  Of the 20% of fishmongers that are in the database, 47% are tagged “shop=seafood”, 44% are tagged “shop=fishmonger”, and 9% are tagged “shop=fish”. This is more problematic, because anyone who looks for just one of the values is going to miss about half of the available data. Nevertheless, I suspect that anyone who is thinking of trawling the data for a fishmonger is still going to be scuppered by the 80% that are missing from the database altogether, not the inconvenience of testing for two or three different tag values.

I reckon that even within the more problematic categories the issues with synonyms aren't difficult to manage. Where data volumes are small, it is not difficult to fix the data. Where data volumes are large, and one value is dominant, then data users who don't look for a synonym will only lose a small proportion of the data. Where volumes are large and synonyms equally matched then keen data users will go to the trouble of testing for several different values.

Problems with spelling and synonyms are not difficult to fix, but they are relatively small in number, so not the highest priority. The bigger challenge is to achieve greater consistency in the choice of tag for similar shops. The data can provide some pointers on how to do that, but they will wait for the next post.

OSM Retail Survey: Part-7

This material seems to be generating quite a bit of interest, and I'm starting to get questions asking about what it means in practice. We'll come to that, but first I'd like to consider a different aspect of data quality. So far most of the focus has been on coverage: what proportion of retail features have been added to the database.

Coverage of one common category of shop has not been considered yet, though. In around 1% of cases the intent of the contributor was clearly to indicate that this was a shop that was not in use. These include “shop=closed”, “shop=empty”, and most commonly “shop=vacant”.

High street vacancy rates across the UK are currently averaging around 10%. Out of around 50,000 vacant shops, we have data on just over 1,000 (2%). This is one of the lowest levels of coverage that we have identified. We can probably assume that contributors are most active in the most vibrant high streets (i.e. those with fewest vacancies), but this still suggests that vacant shops are badly under-recorded in OSM. It is difficult to say whether that means the missing vacant shops are completely un-recorded, or recorded in a way that is difficult to recognise. Either way they are not readily available to data users. However, that probably doesn't matter greatly. It's difficult to imagine many users who would value an application that can find the nearest vacant shop.

But data quality is not just about completeness. We must also question whether the recorded data accurately represents what is on the ground.

In my efforts to uncover as many retail premises as possible I've identified over 2,000 different tag combinations. Around 80 of those account for more than 95% of retail premises. The most common 26 account for 85%. Among the 2,000 are around 200 minor spelling mistakes. These represent 10% of the tagging variations, but a much smaller proportion of the data.

My estimate of the number of spelling mistakes is based on calculating the Levenshtein distance between different values of the shop tag. Where there are only one or two differences in spelling between one tag and another, my initial premise is that the less common variant is a spelling mistake for the more common alternative. However, this approach also picks up some correct values of the shop tag, that have to be eliminated manually from the sample (“shop=car” and “shop=card” for example only differ in one character, but are not spelling variations of each other). The approach is bound to miss some more complex spelling mistakes, but hopefully not too many. I think it is capturing the great majority.

Variations in the use of plural and singular forms account for around 60% of these errors; differences in capitalisation for around 8%; and differences in hyphenation and underscores around 6%). The remaining 25% of near matches are more diverse. Overall this approach detected spelling mistakes in around 0.7% of shop tagging.

The "proper" values of “shop” that are most commonly misspelled are “card” (cards), “carpet” (carpets), “solicitors” (solicitor), off_licence (off_license, offlicence, off-licence).

Given the controversies over bulk editing, it may be worth noting that

• the number of retail features in the database which contain a spelling mistake in the shop key is in the order of 1,000 (compared to 385,000 missing retail premises). 
• around a third of the spelling mistakes in the shop tag are unique occurrences
• many spelling mistakes are an unusual spelling of a value which itself is comparatively rare (or a non-standard use of the “shop” tag)
• data users are probably just going to ignore these - the volumes of lost data are too small to justify a lot of effort on their part   

In other words, it looks as though very few of these cases are suitable for bulk editing: virtually all either need to be checked and fixed manually, or can be more easily fixed manually than with a bulk edit.

Examples of spelling mistakes which occur more than a couple of times include:

Less common  value	Occurrences	More common equivalent	Normal occurrences
cards	60	card	77
carpets	32	carpet	494
solicitor	22	solicitors	32
crafts	16	craft	152
kitchens	15	kitchen	287
bathrooms	14	bathroom	72
antique	13	antiques	395
game	13	games	29
chandler	13	chandlery	15
bookmakers	12	bookmaker	1,542
opticians	12	optician	1,226
communications	12	communication	14
beds	11	bed	100
tile	11	tiles	67
window	11	windows	35
fireplaces	11	fireplace	26
printers	11	printer	25
furnishing	10	furnishings	23
off_license	10	off_licence	17
grocer	9	grocery	52
estate agent	8	estate_agent	1,656
accountants	8	accountant	23

OSM Retail Survey: Part-6

To assess how OSM data compares to commercial services similar searches of retail data were compared across different types of platform. I have not yet managed to do this programmatically, but a broad impression can be gained by comparing the results from a commercial search engine with the results of searching a similar area for equivalent tags on Overpass Turbo (http://overpass-turbo.eu/). The comparisons cannot be carried out precisely, so the approach relies on general impressions, and the findings are more qualitative than quantitative.  Because this approach is so subjective, it would be interesting to hear the impressions that others have of similar comparisons.

OSM was searched by specific categories of shop. The equivalent searches of commercial engines relied on using similar keywords. While these two different approaches can produce similar numbers of results, there were also differences in the specific results that were obtained.

Scope	OSM	Commercial	Notes
Supermarkets and convenience stores around Maidenhead	Around 50 examples	Around 50 examples	Similar coverage, and similar mix. Both identify many convenience stores as a supermarket
Pet shops in Truro	None	Three, plus some variants, such as pet charities	OSM retail coverage is incomplete. Commercial search is more effective
Fishmongers across Norfolk	Around 60 examples	Around 40 examples	OSM coverage better within Norwich (though some duplicates) but thin elsewhere. Commercial search produces more results in coastal towns which are less well-mapped in OSM
DIY on Tyneside	Around 80 examples	Around 40 examples	Both find branches of major chains. Commercial search picks up smaller stores by name match, including some false-positives. OSM picks up some smaller hardware shops based on DIY tagging
Cafés in Harrogate	Around 20 examples	Around 70 examples	OSM retail coverage looks incomplete. Commercial search better at finding in-store cafés and similar, but also includes many false positives (e.g. restaurants)

Inherently, the OSM search was looking for a particular “key=value” pair. I have no inside knowledge of exactly how commercial search engines do this, but it's well understood that - given a particular keyword - they use subtle algorithms to find equivalent matches within bodies of text. This includes some fuzzy searching using inflexions, synonyms and various matching algorithms to expand the scope of results beyond the specific keywords that were requested. For example, if we ask a search find “pharmcy” we are not surprised when it corrects the spelling to “pharmacy” and then retrieves “pharmacies”, pharmacist”, etc. We expect such a search to find retail pharmacies, but we are not surprised that it also retrieves university courses, job vacancies, drug manufacturers, and work by Damien Hirst as well.

I suspect that commercial search engines are also embedding some assumptions about major retail chains. So, for example, if I search for a hardware shop they seem to have some understanding that branches of B&Q and Wickes will also be of interest.

By contrast, the assumed behaviour of data retrieval in OSM is that it will be based on a search for nodes, ways and relations that satisfy a specific set of documented values within a limited subset of available keys. This implicit assumption about how data retrieval will work has an effect on the way that contributors chose how to represent data.

Certainly this model has advantages, and opens up opportunities for users of OSM data that may be difficult to achieve with services that operate on a different search techniques. For example (and for some encouragement about the quality of data that OSM is already able to deliver), try searching for a café with wheelchair access in London.

• With Overpass Turbo
• With Google Maps

It may, of course, be a reasonable assumption, that future OSM data retrieval will be heavily based on searching for combinations of specific key=value pairs, but this may also be too limiting as a way to think about how things will work. For example, an application that is asked to find a cycle shop could search both "shop=bicycle" and "name similar to Halfords". A search for a hardware shop could well re-cast this as a search for any combination of shop=hardware / doityourself / trade, or any outlet that is part of a chain that has a name like B&Q, Homebase, Wickes, Jewson, etc.

In summary: at its best, OSM is capable of outperforming a commercial search engine in terms of both the quantity and precision of the results obtained. Generally searches based on OSM data should retrieve fewer false positives because they can draw (to a greater extent) on a degree of data structure. However, successful retrieval of data from OSM relies heavily on the volume of data recorded, of a particular type of shop, within a particular area.

OSM coverage tends to vary more from place to place. In areas where OSM coverage is around 50-60% of retail premises then my impression is that data users can expect the results of a search of OSM to match the volume of data retrieved from a commercial search engine. Commercial search engines do not find every retail outlet, so in places where OSM coverage is almost complete data users can expect better results from OSM. However, for most retail formats, across much of the UK a search for retail premises on OSM is less effective in retrieving results than a commercial search engine.

OSM Retail Survey: Part-5

Apart from estimating overall coverage, it should also be possible to provide feedback on the type of coverage within a town or similar area. In one small town that I am fairly familiar with, pubs had been thoroughly mapped, but none of the cafés or shops had been mapped. It is relatively easy to measure that kind of discrepancy in the OSM data, and contributors might find that kind of feedback useful as a pointer to areas that need more attention.

It isn't difficult to derive some broad rules of thumb about the balance between different types of retail premises that might suggest where coverage looks incomplete. Across all of the data that I have extracted, 48% of retail premises are shops, and the rest offer either refreshments or services. There are a number of places where the mix is quite different. Of course it may be that some of these towns have an extraordinarily large number of cafés and pubs. More likely that contributors haven't got round to adding many shops yet.

Similarly, there are towns where there don't seem to be as many cafés and pubs as one would normally expect. Again, this could reflect reality on the ground, but it might also point to areas that deserve some more attention.

Following the same line of thought, it ought to be possible to measure the mix on individual shopping streets. For this experiment I used the centre of Nottingham. I have no local knowledge of Nottingham, but the coverage of retail premises there is comprehensive - so the data is relatively easy to work with. Here the mix of retail premises is highlighted on any street where there is a decent sample to work with. The proportion of Shops is shown in Cyan; Refreshments (cafés, pubs, etc) in Yellow, and Services (banks, estate agents, etc) in Magenta. Green implies areas where shops and refreshments predominate. Orange implies that refreshments and services predominate (i.e. comparatively few shops). The idea was to test whether it is possible to give contributors an overall impression of the contents of the map which they can compare against local knowledge of how the town centre is organised – at a broader level than the detailed location of individual shops. It has flaws, and the data is difficult to manipulate - so I'm not convinced the approach is practical - but it might point a way towards better alternatives.

Contributors with an interest in mapping particular types of retail may be able to take advantage of the fact that similar types of retail tend to cluster together. On OSM, 85% of clothes shops have another clothes shop with 100 metres (25% have at least 10 more clothes shops within 100 metres); 70% of banks have another bank within 100 metres; 60% of estate agents and 60% of fast food outlets have an estate agent / fast food outlet within 100 metres; 40% of pubs have another pub within 100 metres. Identifying this kind of cluster might be helpful for some kinds of location search, and it may also provide useful feedback to contributors, who are able to compare the state of the map against local knowledge to identify clusters that look incomplete.

Here, for example is a map of Manchester showing clusters of clothing shops that can be identified from existing data. The analysis began with a broad definition of a clothing shop (shop=clothes, shoes, fashion, boutique, or department store) then used R clustering capabilities (the DBSCAN algorithm) on a data extract to find areas where there are more than five clothing shops within 100 metres of each other. This particular example is probably of limited use to those of us who are unfamiliar with Manchester (and also, for that matter, for those of us who are unfamiliar with shopping for clothes). But on the face of it, there must be quite a lot of missing clothes shops in Manchester, and the presence and absence of clusters in the data might point local fashion-conscious mappers to areas that deserve attention.

SK53 has just pointed out that it should be possible to extend this kind of approach using Food Hygiene data to identify retail areas, and compare them with OSM data. I haven't tried yet, but it sounds like a promising idea.

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Here is an additional example, picking up on the idea that Food Hygiene data might be used to identify suburban areas that need more attention. The Food Hygiene data shows location and food hygiene status for a variety of retail outlets, including pubs, supermarkets, takeaways, restaurants, cafes and some other types of retailer. Of course, the same data could also be used to identify individual outlets that are missing, but since the data only covers certain types of outlet, the aim here is more general. The idea is to identify suburban areas where there may be several missing retail outlets, including some that don't offer food. 

These are Liverpool suburbs where there is Food Hygiene data on at least five retail outlets, but none appear in OSM. Relatively few areas in the UK fit these rather crude criteria. More sophisticated approaches must be possible, but refining them will need more experimentation, and that will take longer. Meanwhile this suggests that the general approach should work in principle.

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And another example, covering Sunderland. This uses the more granular ONS Lower Layer Super Output Areas. Those rendered are where OSM contains no retail outlet, but the Food Standards Agency has at least one Food Hygiene Record (for a high-street business type). The darker the polygon, the more FSA records it contains, and hence the more retail outlets are likely to be missing from OSM. 

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And a third example, for Sheffield, showing the difference between the number of Food Hygiene Records (for high-street business types), and the number of OSM retail features that fall within each LSOA. Once again, the figures aren't directly comparable. The aim is to highlight areas where the OSM data is implausibly thin, so the figures are no more than a proxy measure of how great the shortfall is likely to be. Areas are not coloured where the volume of OSM data is equal to or larger than the FSA Food Hygiene figure - but this doesn't necessarily imply that they are complete. The real message is "if you go to the dark red areas you should find lots of unmapped shops to add".

OSM Retail Survey: Part-4

It is often useful to raise our sights from the data that has been recorded in OSM, and consider the data that hasn't been recorded. As discussed above, the overall level of retail coverage in England is 27% of retail premises. However, there are variations in the extent to which different types of shop are recorded.

There are a number of ways to assess OSM coverage of a specific sector at a national level. The approach, broadly, is to count the number of shops of a particular type that are already recorded in OSM, estimate the total number that should be there, and compare the two.

There are various sources of statistics that give basic information on numbers of different types of shop at a national level. Getting useful estimates of numbers at a more local level is a bigger challenge.

• It isn't difficult to find information on the number of branches for major retail chains - through their own publications, from business reporting, or from Wikipedia. The OSM Wiki has a detailed page on major UK Retail chains. 
• There are national statistics which can be used for some sectors and types of shop (for example, see UKBA01a Enterprise/local units by 4 Digit SIC and UK Regions; and Retail Hereditaments by Administrative Area issued by the Valuation Office Agency). 
• For independent specialists, many trade associations publish figures on the size of their sector.
• Press articles and market research companies will sometimes publish figures on the number of different types of retailer. 
• When all else fails, searching a directory  (such as Yellow Pages) can give some idea of the likely number of outlets. 
• Where retailers need to be licensed (tattoo parlours, for example), I thought it would be easy to obtain figures at a local authority level. No doubt this would be possible (through FOIA, for example), but so far I haven't found a more accessible source of licensing statistics. Local figures may be easier to find from the licensing authority, and any ideas would be welcome on where to find national figure.

For specific retail locations:

• within the OSM community, Robert Whittaker has tools relating to Post Offices on his Post Hoc pages (http://robert.mathmos.net/osm/postboxes/). 
• Beyond the community, most large retail chains publish a list of branches, and some have given permission for this information to be added to OSM. 
• Trade associations for specialist independent retailers don't normally seem to provide information on the location of individual members, but some may,
• The NHS provides lists of pharmacies and opticians.

Using this approach, and taking three examples where we might expect levels of recording to be relatively high:

• There are 11,696 post offices in the UK, and I have found 7,622 (65%) of them in OSM. Around 90% of built-up areas with a population of more than 5,000 have a post office in OSM. The 72 that don't might be good places to find missing post offices. More generally, post offices can be used as an indicator of a wider gap in coverage. They are one of the types of retail outlet that are likely to be added before other retail properties. So a larger settlement with a missing post office is likely to contain other retail properties that need to be added. 
• There are 11,647 community pharmacies in England. I found 4,225 tagged as a pharmacy (36% of the total), and another 483 tagged as “chemist”. Strictly speaking “chemist” is for shops that don't supply prescriptions, but has been quite widely used as a synonym for “pharmacy”. Taken together we locate about 40% of community pharmacies. More than half are missing. I imagine that towns the size of Braintree, Grantham, Peterlee, Melton Mowbray, Haverhill, Maghull, and Congleton have a community pharmacy – but none seems to be recorded in OSM (they are displayed in Google location searches). Only around half of the pharmacies in England are specialised shops – the rest are an operation embedded within another store. Community pharmacies that operate from within a large supermarket seem to be under-recorded in OSM. 
• There are about 2,500 specialist bicycle shops in the UK, and I have found 1,631 (65%) in the OSM database. The largest bicycle retailer, Halfords, has 465 branches across the UK, of which I found 364 (78%). I'm not sure how many of those offer bicycles, but OSM says that 151 of them do (41%). That must surely under-state the true figure.

And some examples where I expected coverage to be relatively low:

• Figures suggest that there are about 1,400 pound stores in the UK. I've found 586 tagged with “variety_store”, and another 170 or so with alternative tagging. Which means that tagging is inconsistent, but suggests that coverage is over 50% - i.e. more than I expected. Perhaps my estimate of the total is too low
• There are 8,500 Charity Shops in England, 900 in Scotland and 500 in Wales. I should find 9,900 in my data extract. Depending on how carefully I interpret the data, I can find between 1,751 and 1,994 (18-20%). Around 90% are tagged as “shop=charity” but there is a smattering of others tagged according to their specialisation: “shop=clothes”, “shop=secondhand” or “shop=books”

Primary tag value	OSM count (UK)	OSM count (England)	Estimated   actual (UK)	Estimated   actual (England)	Approx. coverage
pub	34,937	31,180	48,000		73%
restaurant	16,062	13,855	60,000		27%
fast_food	15,762	13,794		41,295	33%
cafe	13,137	11,280	16,501		80%
convenience	13,108	11,212	48,303		27%
supermarket	8,720	7,352	6,410		119%
post_office	7,622	6,199	11,696		65%
hairdresser	7,187	6,366	38,300		19%
fuel	6,207	5,190	8,588		72%
bank	5,946	5,089	8,961		66%
pharmacy	4,871	4,225		11,647	36%
charity	1,682	1,476	9,900	8,500	17%
bicycle	1,631	1,406	2,500		65%
beauty	1,543	1,361	13,000		12%
bookmaker	1,386	1,242	9,128		15%
optician	1,161	1,041	7,250		16%
florist	983	885	8,000		12%
alcohol	784	665	5,575	4,195	14%
variety_store	586	528	1,400		42%
deli	456	391	2,500		18%
seafood	87	70	950		9%

It is interesting to consider in more detail at how data users might interpret some specific examples.

Finding a pharmacist (i.e. someone who can dispense prescriptions) could be the basis of a useful application, and there have been various attempts to develop appropriate tagging, but the actual data is quite complex for data users to interpret.

Values of “pharmacy” and “chemist” can appear for “amenity” and “shop”; “dispensing” can be set to “yes”, “no” or sometimes the name of the outlet. And all of these can be combined in different ways, alongside other values of “amenity” and “shop”.

• “amenity=pharmacy” alongside any value of “shop=*” and either “dispensing=yes” or no value for “dispensing”: this is in line with the various guidelines, and unambiguously indicates that prescriptions will be dispensed. This accounts for almost 90% of cases in the data
• “shop=chemist” without any indication of “dispensing”: is correct tagging for a place where prescriptions will NOT be dispensed, but examining actual examples suggests that it is widely mis-used for pharmacies. So in practice it has to be regarded as ambiguous. It represents almost 10% of cases.
• “amenity=pharmacy” with “dispensing=no”: is inconsistent tagging, and not in line with the guidelines, but can still be interpreted fairly confidently as a place where prescriptions will NOT be dispensed. It accounts for around 1% of cases
• “shop=chemist” without “amenity=pharmacy”, and with “dispensing=no” is correct tagging, and unambiguously a place where prescriptions will NOT be dispensed. It only accounts for 0.1% of cases.
• “shop=pharmacy”, “amenity=chemist”, with or without other values are examples of incorrect use of the tags, but small in volume (less than 0.5%), and often appear alongside a correct tag (e.g. “shop=pharmacy”+“amenity=pharmacy”): the incorrect tag values can safely be ignored by data users without sacrificing significant amounts of relevant data

The above figures are calculated from pharmacies and chemists recorded in the database. So it is worth recalling that this only accounts for 40% of actual pharmacies, and around 60% of these outlets do not appear in the database at all.

In practice data users are going to be reasonably confident that they have found a dispensing pharmacist where “amenity=pharmacy” is present, and “dispensing” is either absent, or set to anything other than “no”. They will have to treat “shop=chemist” as ambiguous in this context. In practice they will probably ignore everything else because the complexity of the logic increases out of all proportion to the quantity of reliable data that it can uncover. In summary they will confidently interpret 90% of the data in the database, and find just over one in three pharmacies. If they interpret the data more loosely they will be able to point their users to about 40% of real pharmacies. If they want to find more, then at present they will have to look elsewhere for their data.

Next we will look at how coverage by type of retail outlet might be used to provide useful feedback to contributors and data users.....

OSM Retail Survey: Part-3

It is quite difficult to identify areas of incomplete retail mapping within a large conurbation.

Initially I thought investigation of areas marked as “landuse=retail” showed promise, but they proved disappointing in practice. The meaning of this tag has been interpreted in different ways, and used inconsistently. Almost half of retail areas in OSM contain no retail properties, so they can provide some indication of gaps. However, some of them are very small, and two-thirds of retail properties already in OSM lie outside a marked retail area, so in general checking for data on shops within retail areas is unlikely to be efficient.

• A number of fairly large settlements don't have any retail area described in OSM (including Weymouth, Wellingborough, Grantham, and Newark-on-Trent). 

The reasons vary.

• In most of these the main retail area hasn't been marked as such, even though other types of urban landuse have been applied in other parts of the town. 
• In some cases the whole town is marked as “landuse=residential”, 
• In others an area that seems to be predominantly retail space has been described as “landuse=commercial”. 

Overall, there are too many exception cases to make productive use of retail landuse data.

On a very traditional high street, individual shop frontages tend to be relatively narrow, and relatively consistent in length. In theory, if we could compare the density of shops in OSM with what we expect, then we should be able to get a sense of where the database looks thin. However, this can only give a broad indication.

• A well-documented street with a few large shops will still look empty in comparison to a street where the data only includes a small proportion of numerous small shops. 
• There are practical difficulties in calculating density consistently when nearby shops may lie on opposite sides of a street, or in adjoining, and neighbouring streets. 

These challenges can be overcome to a degree, but my attempts have involved some intensive computation. In practice a simple heat map seems to be equally useful for flagging up suburban areas that already have fairly high levels of retail content. In conjunction with local knowledge of where retail outlets are clustered this could be sufficient to identify some larger suburban shopping areas that need attention.

This is an example from Newcastle. Data on retail is dense in the centre of the city, the quayside, and along Gosforth High Street. Coverage of retail barely shows up in areas such as Jesmond, and along Westgate Road. Those with local knowledge might be able to use this kind of feedback to identify areas that are worth further investigation.

Finally, while the overall level of retail coverage in England is 27% of retail premises, there are variations in the extent to which different types of shop are recorded. Analysis of the mix can be useful at a national level, but it can also be informative at a local level.

First, though, we will look at the mix at a national level.