A Python library for offline reverse geocoding. It improves on an existing library called reverse_geocode developed by Richard Penman.
UPDATE (30-Mar-15): v1.2 released with Python3 support and more accurate geocoding! See release notes below.
Ajay Thampi | @thampiman | opensignal.com | ajaythampi.com
- Besides city/town and country code, this library also returns the nearest latitude and longitude and also administrative regions 1 and 2.
- This library also uses a parallelised implementation of K-D trees which promises an improved performance especially for large inputs.
The K-D tree is populated with cities that have a population > 1000. The source of the data is GeoNames.
For first time installation,
$ pip install reverse_geocoder
Or upgrade an existing installation using,
$ pip install --upgrade reverse_geocoder
Package can be found on PyPI.
- v1.0 (27-Mar-15) - First version with support for only Python2
- v1.1 (28-Mar-15) - Fix for issue #1 by Brandon
- v1.2 (30-Mar-15) - Support for Python 3, conversion of Geodetic coordinates to ECEF for use in K-D trees to find nearest neighbour using the Euclidean distance function. This release fixes issues #2 and #8. Special thanks to David for his help in partly fixing #2.
The library supports two modes:
- Mode 1: Single-threaded K-D Tree (similar to reverse_geocode)
- Mode 2: Multi-threaded K-D Tree (default)
import reverse_geocoder as rg
coordinates = (51.5214588,-0.1729636),(9.936033, 76.259952),(37.38605,-122.08385)
results = rg.search(coordinates) # default mode = 2
print results
The above code will output the following:
[{'name': 'Barbican',
'cc': 'GB',
'lat': '51.51988',
'lon': '-0.09446',
'admin1': 'England',
'admin2': 'Greater London'},
{'name': 'Cochin',
'cc': 'IN',
'lat': '9.93988',
'lon': '76.26022',
'admin1': 'Kerala',
'admin2': 'Ernakulam'},
{'name': 'Mountain View',
'cc': 'US',
'lat': '37.38605',
'lon': '-122.08385',
'admin1': 'California',
'admin2': 'Santa Clara County'}]
If you'd like to use the single-threaded K-D tree, set mode = 1 as follows:
results = rg.search(coordinates,mode=1)
The performance of modes 1 and 2 are plotted below for various input sizes.
Mode 2 runs ~2x faster for very large inputs (10M coordinates).
- Major inspiration is from Richard Penman's reverse_geocode library
- Parallelised implementation of K-D Trees is extended from this article by Sturla Molden
- Geocoded data is from GeoNames
The MIT License (MIT)