geograpy3 is a fork of geograpy2, which is itself a fork of geograpy and inherits most of it, but solves several problems (such as support for utf8, places names with multiple words, confusion over homonyms etc). Also, geograpy3 is compatible with Python 3, unlike geograpy2.
since geograpy3 0.0.2 cities,countries and regions are matched against a database derived from the corresponding wikidata entries
geograpy extracts place names from a URL or text, and adds context to those names -- for example distinguishing between a country, region or city.
The extraction is a two step process. The first process is a Natural Language Processing task which analyzes a text for potential mentions of geographic locations. In the next step the words which represent such locations are looked up using the Locator.
If you already know that your content has geographic information you might want to use the Locator interface directly.
Grab the package using pip
(this will take a few minutes)
pip install geograpy3
geograpy3 uses NLTK for entity recognition, so you'll also need to download the models we're using. Fortunately there's a command that'll take care of this for you.
geograpy-nltk
git clone https://github.com/somnathrakshit/geograpy3
cd geograpy3
scripts/install
Import the module, give some text or a URL, and presto.
import geograpy
url = 'https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay'
places = geograpy.get_geoPlace_context(url=url)
Now you have access to information about all the places mentioned in the linked article.
places.countries
contains a list of country namesplaces.regions
contains a list of region namesplaces.cities
contains a list of city namesplaces.other
lists everything that wasn't clearly a country, region or cityNote that the other
list might be useful for shorter texts, to pull out
information like street names, points of interest, etc, but at the moment is
a bit messy when scanning longer texts that contain possessive forms of proper
nouns (like "Russian" instead of "Russia").
In addition to listing the names of discovered places, you'll also get some information about the relationships between places.
places.country_regions
regions broken down by countryplaces.country_cities
cities broken down by countryplaces.address_strings
city, region, country strings useful for geocodingWhile a text might mention many places, it's probably focused on one or two, so geograpy3 also breaks down countries, regions and cities by number of mentions.
places.country_mentions
places.region_mentions
places.city_mentions
Each of these returns a list of tuples. The first item in the tuple is the place name and the second item is the number of mentions. For example:
[('Russian Federation', 14), (u'Ukraine', 11), (u'Lithuania', 1)]
You can of course use each of Geograpy's modules on their own. For example:
from geograpy import extraction
e = extraction.Extractor(url='https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay')
e.find_geoEntities()
# You can now access all of the places found by the Extractor
print(e.places)
Place context is handled in the places
module. For example:
from geograpy import places
pc = places.PlaceContext(['Cleveland', 'Ohio', 'United States'])
pc.set_countries()
print pc.countries #['United States']
pc.set_regions()
print(pc.regions #['Ohio'])
pc.set_cities()
print(pc.cities #['Cleveland'])
print(pc.address_strings #['Cleveland, Ohio, United States'])
And of course all of the other information shown above (country_regions
etc)
is available after the corresponding set_
method is called.
geograpy3 uses the following excellent libraries:
geograpy3 uses the following data sources:
Hat tip to Chris Albon for the name.