Extract countries, regions and cities from a URL or text

# geograpy3

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

# What it is

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.

## Install & Setup

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


## Getting the source code

git clone https://github.com/somnathrakshit/geograpy3
cd geograpy3
scripts/install


## Basic Usage

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)


• places.countries contains a list of country names
• places.regions contains a list of region names
• places.cities contains a list of city names
• places.other lists everything that wasn't clearly a country, region or city

Note 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").

## But Wait, There's More

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 country
• places.country_cities cities broken down by country
• places.address_strings city, region, country strings useful for geocoding

## Last But Not Least

While 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)]


## If You're Really Serious

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'])



And of course all of the other information shown above (country_regions etc) is available after the corresponding set_ method is called.

## Credits

geograpy3 uses the following excellent libraries:

• NLTK for entity recognition
• newspaper for text extraction from HTML
• jellyfish for fuzzy text match
• pylodstorage for storage and retrieval of tabular data from SQL and SPARQL sources

geograpy3 uses the following data sources:

Hat tip to Chris Albon for the name.

## Project details

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