sarutobi/reverse-geocoder

A fast, offline reverse geocoder in Python

Reverse Geocoder

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.

About

Ajay Thampi | @thampiman | opensignal.com | ajaythampi.com

Features

1. Besides city/town and country code, this library also returns the nearest latitude and longitude and also administrative regions 1 and 2.
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.

Installation

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.

Release Notes

1. v1.0 (27-Mar-15) - First version with support for only Python2
2. v1.1 (28-Mar-15) - Fix for issue #1 by Brandon
3. 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.

Usage

The library supports two modes:

1. Mode 1: Single-threaded K-D Tree (similar to reverse_geocode)
2. 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)

Performance

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).

Acknowledgements

1. Major inspiration is from Richard Penman's reverse_geocode library
2. Parallelised implementation of K-D Trees is extended from this article by Sturla Molden
3. Geocoded data is from GeoNames

License

The MIT License (MIT)