Google Local Search better than Google Geocoding?

Google offers two (apparently) unrelated solutions for doing geocoding (converting a text string into a latitude-longitude and a structured address): the Google Geocoding API (which is part of the Google Maps API) and the Google Local Search API (which has JavaScript and non-JavaScript variants). This post discusses our experiences with both and the results of recent testing we have been doing, which has led us to a decision to switch from our current hybrid approach to using only local search.

Initially we were attracted to using Google Local Search for whereyougonnabe, as it lets you search for places of interest like "Wynkoop Brewing Company" or "Intergraph Huntsville", rather than having to know an address in all cases - whereas the geocoding API only works with addresses.

However, in our initial testing with local search we found a number of cases where it returned a location and address string successfully, but did not properly parse the address into its constituents correctly (for example, returning a blank city). For our application it was important to be able to separate out the city from the rest of the address. In our initial testing, the geocoding API seemed to do a better job of correctly parsing out the address. In addition, it returned a number indicating how accurate the geocoding was (for example street level or city level). So we ended up implementing a rather ugly hybrid solution, where we first used local search to allow us to search for places of interest in addition to addresses, and then passed the address string which was returned to the geocoding API to try to structure it more consistently. In most cases this was transparent to the user, but in a number of cases we hit problems where the second call would not be able to find the address returned by the first call, or where the address would get mysteriously "moved". With so much to do and so little time :) we elected to go with this rather unsatisfactory solution to start with, but I have recently been revisiting this area, and doing some more detailed testing of the two options.

Before I get into more details of the testing, I should just comment on a couple of other things. One is that a key reason we are revisiting our geocoding in general is that we recently introduced the ability to import activities from external calendar systems, which means we need to call geocoding functionality from our server from a periodic background process, whereas before that we just called it from the browser on each user's client machine. This is sigifnicant because the Google geocoding API restricts you to 15,000 calls per IP address per day, which is not an issue at all if you do all your geocoding on the client but quickly becomes an issue if you need to do server based geocoding. Interestingly, Google Local Search has a different approach, with no specified transaction limits (which is not to say that they could not introduce some of course, but it's a much better starting point than the hard limit on the geocoding API).

Secondly, a natural question to ask is whether we have looked at other solutions beyond these two, and the answer is yes, though not in huge detail. Most of the other solutions out there do not have the ability to handle points of interest in addition to addresses, which is a big issue for us. Microsoft Virtual Earth looks like the strongest competitor in this regard, but it seems that we would need to pay in order to use that, and we need to talk to Microsoft to figure out how much, which we haven't done yet - and obviously if we can get a free solution which works well we would prefer that. Several solutions suffer from lack of global coverage and/or even lower transaction limits than Google. We are using the open source geonames database for an increasing number of things, which I'll talk about more in a future post, but that won't do address level geocoding and points of interest are more limited than in Google (currently at least).

Anyway, on to the main point of the post :) ! I tested quite a variety of addresses and points of interest on both services. Some of these were fairly random, a number were addresses which specific users had reported to us as causing problems in their use of whereyougonnabe. In almost all the specific problem cases, we found that the issue was with the geocoding API rather than local search. The following output shows a sample of our test cases (mainly those where one or other had some sort of problem):

I=Input, GC=geocoding API result, LS=local search API result, C=comments

I: 1792 Wynkoop St, Denver
GC: 1792 Wynkoop St, Denver, CO 80202, USA
LS: 1792 Wynkoop St, Denver, CO 80202
C: LS did not include country in the address string returned, but it was included separately in the country field. The postcode field was not set in LS, even though it appeared in the address string. Other fields including the city and state (called "region") were broken out correctly. This was typical with other US addresses - LS did not set the zip code / postal code, but otherwise generally broke out the address components correctly.

I: 42 Latimer Road, Cropston
GC: 42 Latimer Rd, Cropston, Leicestershire, LE7 7, UK
LS: null
C: The main case where GC fared better was with relatively incomplete addresses, like this one with the country and nearest large town omitted (Cropston is a very small village)

I: 72 Latimer Road, Cropston, England
GC: 72 Latimer Rd, Cropston, Leicestershire, LE7 7, UK
LS: 72 Latimer Rd, Cropston, Leicester, UK
C: Both worked in this case, with slight variations. GC included the postal code (a low accuracy version) and LS did not. LS included the local larger town Leicester which is technically part of the mailing address.

I: 8225 Sage Hill Rd, St Francisville, LA 70775
GC: null
LS: 8225 Sage Hill Rd, St Francisville, LA 70775
C: One of several examples from our users which didn't work in GC but did in LS.

I: Wynkoop Brewing Company Denver
GC: null
LS: 1634 18th St, Denver, CO
C: As expected, points of interest like this do not get found by GC

I: Kismet, NY
GC: Kismet Ct, Ridge, NY 11961, USA
LS: Kismet, Islip, NY
C: Another real example from a user, where GC returned an incorrect location about 45 miles away from the correct location, which was found by LS.

I: 1111 West Georgia Street, Vancouver, BC, Canada
GC: 1111 E Georgia St, Vancouver, BC, Canada
LS: 1111 W Georgia St, Vancouver, BC, Canada
C: Another real example from a user which GC gets wrong - strangely it switches the street from W Georgia St to E Georgia St, which moves the location about 2 miles from where it should be.

I: London E18
GC: London, Alser Strafle 63a, 1080 Josefstadt, Wien, Austria
LS: London E18, UK
C: Another real user example. London E18 is a common way of denoting an area of London (the E18 is the high level portion of the postcode). GC gets it completely wrong, relocating the user to Austria, but LS gets it right.

So in summary, in our test cases we found a lot more addresses that could not be found or were incorrectly located by the Google Geocoding API, but were correctly located by Google Local Search, than vice versa. It is hard to draw firm conclusions without doing much larger scale tests, and it is possible that there is a bias in our problem cases as our existing application may have tended to make more problems visible from the Geocoding API than from Local Search (it is hard to tell whether this is the case or not). But nevertheless, based on these tests we feel much more comfortable going with Local Search rather than the Geocoding API, especially given its compelling benefits in locating points of interest by name rather than address. These point of interest searches can also take a search point, which is also a very useful feature for us, which I will save discussion of for a future post. Advantages of the geocoding API are that it returns an accuracy indicator and generally returns the zip/postal code also, neither of which are true for Local Search. Neither of these were critical issues for us, though we would really like to have the accuracy indicator in local search also. For our application we are not too concerned about precisely how the addresses align with the base maps - one reason I have heard given in passing for Google having these two separate datasets is that they want a geocoding dataset which uses the same base data as Google Maps uses, to try to ensure that locations returned by geocoding align as well as possible with the maps. If this is a high priority for your application, you might want to test this aspect in more detail. It also appears that the transaction limits are more flexible for server based geocoding with Local Search.

So we'll be switching to an approach which uses only Google Local Search in an upcoming release of whereyougonnabe. I'll report back if anything happens to change our approach, and I'll also talk more about what we're doing with geonames relating to geocoding in a future post.