Reverse Geocoding Without Web Access
Question:
I am working on an application where one of the requirements is that I be able to perform realtime reverse geocoding operations based on GPS data. In particular, I must be able to determine the state/province to which a latitude, longitude pair maps and detect when we have moved from one state/province to another.
I have a couple ideas so far but wondered if anyone had any ideas on either of the following:
- What is the best approach for tackling this problem in an efficient manner?
- Where is a good place to find and what is the appropriate format for North American state/province boundaries
As a starter, here are the two main ideas I have:
- Break North America into a grid with each rectangle in the grid mapping to a particular state province. Do a lookup on this table (which grows quickly the more precise you would like to be) based on the latitude and then the longitude (or vice versa).
- Define polygons for each of the states and do some sort of calculation to determine in which polygon a lat/lon pair lies. I am not sure exactly how to go about this. HTML image maps come to mind as one way of defining the bounds for a state/province.
I am working in python for the interested or those that might have a nice library they would like to suggest.
To be clear… I do not have web access available to me, so using an existing reverse geocoding service is not an option at runtime
Answers:
I have a database with all of this data and some access tools. I made mine from the census tiger data. I imagine it’d basically be an export of my database to sqlite and a bit of code translation.
I would stay away from implementing your own solution from scratch. This is a pretty big undertaking and there are already tools out there to do this. If you’re looking for an open source approach (read: free), take a look at this blog post: Using PostGIS to Reverse Geocode.
I suggest using a variant of your first idea: Use a spatial index. A spatial index is a data structure built from rectangles, mapping lat/long to the payload. In this case you will probably map rectangles to state-province pairs. An R-tree may be a good option. Here’s an R-tree python package. You could detect roaming by comparing the results of consecutive searches.
If you can get hold of state boundaries as polygons (for example, via OpenStreetMap), determining the current state is just a point-in-polygon test.
If you need address data, an offline solution would be to use Microsoft Mappoint.
You can get data for the entire united states from open street map You could then extract the data you need such as city or state locations into what ever format works best for your application. Note although data quality is good it isn’t guaranteed to be completely accurate so if you need complete accuracy you may have to look somewhere else.
I created an offline reverse geocoding module for countries: https://github.com/richardpenman/reverse_geocode
>>> import reverse_geocode
>>> coordinates = (-37.81, 144.96), (31.76, 35.21)
>>> reverse_geocode.search(coordinates)
[{'city': 'Melbourne', 'code': 'AU', 'country': 'Australia'},
{'city': 'Jerusalem', 'code': 'IL', 'country': 'Israel'}]
I will see if I can add data for states.
The free reverse geocoding service I developed (www.feroeg.com) is based on spatialite, a sqlite library implementing SQL spatial capabilities (r-tree).
The data are imported from OpenStreetMap (nation, cities, streets,street number) and OpenAddresses (street numbers) using proprietary tools.
The entire world consumes about 250GB.
There is a paper describing the architecture of the service:
https://feroeg.com/Feroeg_files/Feroeg Presentation.pdf
At the moment the project (importer and server) is closed source.
Reverse Geocoding Library (C++) and converting tools are availabile on request.
I am working on an application where one of the requirements is that I be able to perform realtime reverse geocoding operations based on GPS data. In particular, I must be able to determine the state/province to which a latitude, longitude pair maps and detect when we have moved from one state/province to another.
I have a couple ideas so far but wondered if anyone had any ideas on either of the following:
- What is the best approach for tackling this problem in an efficient manner?
- Where is a good place to find and what is the appropriate format for North American state/province boundaries
As a starter, here are the two main ideas I have:
- Break North America into a grid with each rectangle in the grid mapping to a particular state province. Do a lookup on this table (which grows quickly the more precise you would like to be) based on the latitude and then the longitude (or vice versa).
- Define polygons for each of the states and do some sort of calculation to determine in which polygon a lat/lon pair lies. I am not sure exactly how to go about this. HTML image maps come to mind as one way of defining the bounds for a state/province.
I am working in python for the interested or those that might have a nice library they would like to suggest.
To be clear… I do not have web access available to me, so using an existing reverse geocoding service is not an option at runtime
I have a database with all of this data and some access tools. I made mine from the census tiger data. I imagine it’d basically be an export of my database to sqlite and a bit of code translation.
I would stay away from implementing your own solution from scratch. This is a pretty big undertaking and there are already tools out there to do this. If you’re looking for an open source approach (read: free), take a look at this blog post: Using PostGIS to Reverse Geocode.
I suggest using a variant of your first idea: Use a spatial index. A spatial index is a data structure built from rectangles, mapping lat/long to the payload. In this case you will probably map rectangles to state-province pairs. An R-tree may be a good option. Here’s an R-tree python package. You could detect roaming by comparing the results of consecutive searches.
If you can get hold of state boundaries as polygons (for example, via OpenStreetMap), determining the current state is just a point-in-polygon test.
If you need address data, an offline solution would be to use Microsoft Mappoint.
You can get data for the entire united states from open street map You could then extract the data you need such as city or state locations into what ever format works best for your application. Note although data quality is good it isn’t guaranteed to be completely accurate so if you need complete accuracy you may have to look somewhere else.
I created an offline reverse geocoding module for countries: https://github.com/richardpenman/reverse_geocode
>>> import reverse_geocode
>>> coordinates = (-37.81, 144.96), (31.76, 35.21)
>>> reverse_geocode.search(coordinates)
[{'city': 'Melbourne', 'code': 'AU', 'country': 'Australia'},
{'city': 'Jerusalem', 'code': 'IL', 'country': 'Israel'}]
I will see if I can add data for states.
The free reverse geocoding service I developed (www.feroeg.com) is based on spatialite, a sqlite library implementing SQL spatial capabilities (r-tree).
The data are imported from OpenStreetMap (nation, cities, streets,street number) and OpenAddresses (street numbers) using proprietary tools.
The entire world consumes about 250GB.
There is a paper describing the architecture of the service:
https://feroeg.com/Feroeg_files/Feroeg Presentation.pdf
At the moment the project (importer and server) is closed source.
Reverse Geocoding Library (C++) and converting tools are availabile on request.