RvGeocoder

A Python library for offline reverse geocoding. It improves on an existing library called reverse_geocoder developed by Ajay Thampi.

The package has not been updated sinse 2016 and this is an effort of keeping it a "live" project.

Main improvements on first version of this forked package:

• support init non-singleton instance of RGeocoder to support different stream simultaneously (creating RGeocoderImpl class to support that)
• added the capability change the header columns when loading custom stream and loaded different schemas
• added query_dist to support returning distance in addtion to the reverse geocoding data itself
• added do_extract to trigger extraction for any Geonames file, e.g: 1000, 15000 etc
• removed win32 and python2 support. added utility function to load data from files/buffers

• The init commit of this project is based on thampiman/reverse-geocoder Latest commit on Sep 15, 2016 a81b4095bf2cb7ef84d2187fcbc8945d5d8922d0 - and forks from there.

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 rvgeocoder

Or upgrade an existing installation using,

$ pip install --upgrade rvgeocoder

Dependencies

1. scipy
2. numpy

Release Notes

1. v1.0.1 (29-Aug-19) - First version

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 rvgeocoder as rvg

coordinates = (41.852968, -87.725730), (48.836364, 2.357422)
print(rvg.search(coordinates))

The above code will output the following:

[OrderedDict([('lat', '41.84559'),
              ('lon', '-87.75394'),
              ('name', 'Cicero'),
              ('admin1', 'Illinois'),
              ('admin2', 'Cook County'),
              ('cc', 'US')]),
 OrderedDict([('lat', '48.85341'),
              ('lon', '2.3488'),
              ('name', 'Paris'),
              ('admin1', 'Ile-de-France'),
              ('admin2', 'Paris'),
              ('cc', 'FR')])]

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 rvgeocoder as rvg

geo = rvg.RGeocoder(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.

Acknowledgements

1. Major inspiration is from Richard Penman's reverse_geocode library
2. First version based on reverse_geocoder developed by Ajay Thampi
3. Parallelised implementation of K-D Trees is extended from this article by Sturla Molden
4. Geocoded data is from GeoNames

License

Copyright (c) 2019 Sefi Itzkovich and contributors. This code is licensed under the LGPL License.