Reverse Geocoder

A fast, offline reverse geocoder in Python

Description

A Python library for offline reverse geocoding. It improves on an existing library called reverse_geocode developed by Richard Penman. The library was built with speed in mind and it can geocode 10 million GPS coordinates in under 30 seconds on a machine with 8 cores.

Code Quality Rank: L4

Programming language: Python

License: GNU Lesser General Public License v3.0 only

Tags: Geolocation     Science And Data Analysis     Scientific     Engineering     Python    

Latest version: v1.5

README

Reverse Geocoder

A Python library for offline reverse geocoding. It improves on an existing library called reverse_geocode developed by Richard Penman.

UPDATE (15-Sep-16): v1.5.1 released! 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.

By default, the K-D tree is populated with cities that have a population > 1000. The source of the data is GeoNames. You can also load a custom data source so long as it is a comma-separated file with header (like rg_cities1000.csv), containing the following columns:

• lat: Latitude
• lon: Longitude
• name: Name of place
• admin1: Admin 1 region
• admin2: Admin 2 region
• cc: ISO 3166-1 alpha-2 country code

For usage instructions, see below.

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.

Dependencies

1. scipy
2. numpy

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.
4. v1.3 (11-Apr-15) - This release fixes issues #9, #10, #11 and #12. License has been changed from MIT to LGPL (see #12).
5. v1.4 (08-Jul-16) - Included numpy and scipy as dependencies in setup.
   
6. v1.5 (15-Sep-16) - Support for custom data source and fixes for issues #16 and #24. Hat tip to Jason and Gregoire.
7. v1.5.1 (15-Sep-16) - Fix for #26.

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': 'Bayswater', 
      'cc': 'GB', 
      'lat': '51.51116',
      'lon': '-0.18426', 
      '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)

To use a custom data source for geocoding, you can load the file in-memory and pass it to the library as follows:

import io
import reverse_geocoder as rg

geo = rg.RGeocoder(mode=2, verbose=True, stream=io.StringIO(open('custom_source.csv', encoding='utf-8').read()))
coordinates = (51.5214588,-0.1729636),(9.936033, 76.259952),(37.38605,-122.08385)
results = geo.query(coordinates)

As mentioned above, the custom data source must be comma-separated with a header as rg_cities1000.csv.

Performance

The performance of modes 1 and 2 are plotted below for various input sizes.

[Performance Comparison](performance.png)

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

Copyright (c) 2015 Ajay Thampi and contributors. This code is licensed under the LGPL License.