API

geonlplify.find_sne(text)

Return list of SNE

Parameters:

text – input text

Returns:

list of SNE

geonlplify.geocode(sne_name)

Input a place name and retrieves OSM properties. Exemple for sne_name=”Montpellier”:

{‘osm_id’: 65442261, ‘osm_type’: ‘N’, ‘country’: ‘France’, ‘osm_key’: ‘place’, ‘city’: ‘Montpellier’, ‘countrycode’: ‘FR’, ‘osm_value’: ‘city’, ‘postcode’: ‘34062’, ‘name’: ‘Montpellier’, ‘county’: ‘Hérault’, ‘state’: ‘Occitanie’, ‘type’: ‘district’}

Parameters:

sne_name –

Returns:

a dictionary with OSM properties

geonlplify.geonlplify(text, method='spatial_synonym')

GeoNLPlify aims to make variations of an input sentence working on spatial information contained in words

Examples:

>> geonlplify.geonlplify(“5 cases of avian influenza found in Montpellier”) 5 cases of avian influenza found in Bangalore

Parameters:

• text – Input

• method – Between those 3 methods [generalization, specialization, spatial_synonym]

Returns:

the variation of the input text

geonlplify.importing_error_check()

Check if there is any importing issue (like missing data or spacy model) :return:

geonlplify.load_simplemaps()

Load simplemaps from https://simplemaps.com/data/world-cities CC-BY 4.0 attribution from https://simplemaps.com/data/world-cities :return:

geonlplify.replace_variants(text, list_of_variant, method, conserve_n_gram=True)

Parameters:

• text –

• list_of_variant –

  example: - error:

  [{‘name’: ‘South Sudan’, ‘label’: ‘GPE’, ‘start_char’: 56, ‘end_char’: 67, ‘generalization’: nan, ‘generalization_failed’: ‘sea’}]

  • ok:

    [{‘name’: ‘New Jersey’, ‘label’: ‘GPE’, ‘start_char’: 39, ‘end_char’: 49, ‘generalization’: ‘United States’}]

Returns: