[1] Link to example -> https://developer.here.com/api-explorer/rest/geocoder/revers...
geocode.earth founder here, happy to answer questions.
We have great reverse geocoding with OSM and OpenAddresses data.
Feel free to shoot me an email for an invite: [email protected]
probably not worth the effort
The real catch is this, though: if you actually pulled it off, you would have just built a nearly flawless reverse geocoder, and you wouldn't need to use an external API at all. You could just look locations up in your huge property data set.
it doesn’t need to be an accurate decider, just “good enough” in a majority of cases.
The issue is the rendering is actually somewhat resource intensive and browser support is thus far incomplete. But we’re getting there!
Server side rendering can be less intensive for the client, since it just has to show an image.
Forward Geocoding (Converting the addresses to latitude/longitude): https://gist.github.com/MiniCodeMonkey/13c02d45089478182c3d1...
Reverse Geocoding (Converting the lat/lon to addresses): https://gist.github.com/MiniCodeMonkey/13c02d45089478182c3d1...
Based on the above definition, geocod.io's accuracy for reverse geocoding of this list is 95.2%
Putting this together with dbatten's original findings, the list would look like this:
Google: 97% ($4 per 1,000)
Geocod.io: 95.2% ($0.50 per 1,000)
Mapbox: 95% ($0.50 per 1,000)
TomTom: 73%
Google was 97% accurate, at $4 per 1000 requests. Mapbox was 95% accurate, at $.50 per 1000 requests. TomTom was 73% accurate, at $.42 per 1000 requests. Location IQ (one of many providers simply running the open-source Nominatim project) was 12% accurate, at $.03 per 1000 requests.
To be fair to Location IQ / Nominatim, they had the right street the vast majority of the time, but were usually wrong about house number due to interpolating between address boundaries at cross streets defined in Census Data. If you need exact addresses, Nominatim probably isn't for you, but if you need a general location, it might work fine.
Also - this was one county of test data, so take it with a grain of salt.
Nevertheless, it gave us confidence that we could move away from the Google APIs, save 90% of our costs, and still have a high level of accuracy.