yat-geo-db 1.1.1

A simple and effective Python wrapper around the Open Geo DB managed by YAT

YAT Geo DB

An elegantly simple Geo Reference manager with managed database of US, Canada and Mexico. Quickly perform auto-complete style fuzzy search, fetch record details and search a mile radius around a specific place.

Geo DB file lists available at https://yat-geo-db.sfo3.digitaloceanspaces.com/

Install

With Python 3.7 or greater install the package with simple pip command.

pip install yat-geo-db

Usage

The manager utilizes two flat files maintained by YAT available at <location>. On initial load those files will be downloaded to local machined and stored on machine as a form of cache. You can specify a specific version denoted by date or utilize the current version.

Import and load data

from yat_geo_db import GeoManager as GeoManagerImport

GeoManager = GeoManagerImport()

GeoManager.load_data(
    force_db_fetch= False, cache_local=True, compressed=True
)

Refresh local data (current version)

from yat_geo_db import GeoManager as GeoManagerImport

GeoManager = GeoManagerImport()

GeoManager.load_data(force_db_fetch=True)

Perform Auto-complete style search

search_param = "Nashvil"
filters = {"ref_data.state_prov": "TN", "ref_data.country": "US"}
fuzzy_res = GeoManager.fuzzy_search(
    search_param, num_results=2, filters=filters
)
print([(value["value"], value["id"]) for value in fuzzy_res])
>>> [('Nashville, TN', 'us__tn__nashville'), ('Nashville Metro Area, TN', 'nashville_tn_us_metro')]

Apply filters for any element in shape object including geo_type and ref_data, latter allowing to refine search to specific country or state.

search_param = "Nashvil"
filters = {"geo_type": "ZipCode", "ref_data.country": "US"}
fuzzy_res = GeoManager.fuzzy_search(
    search_param, num_results=2, filters=filters
)
print([(value["value"], value["id"]) for value in fuzzy_res])
>>> []

Fetch a shape object by the reference code. All reference codes follow a hierarchical structure, for below example <country>__<state>__<name with _ seperator>.

reference_code = 'us__tn__nashville'

res = GeoManager.get_shape_by_ref_code(reference_code=reference_code)

print(res)

>>> {'value': 'Nashville, TN',
>>>  'clean_value': 'nashville tn',
>>>  'id': 6818,
>>>  'area': 0.0,
>>>  'bbox': {'ll_latitude': '36.165890',
>>>   'ur_latitude': '36.165890',
>>>   'll_longitude': '-86.784440',
>>>   'ur_longitude': '-86.784440'},
>>>  'geo_type': 'City',
>>>  'latitude': 36.16589,
>>>  'ref_data': {'city': 'Nashville',
>>>   'country': 'US',
>>>   'zip_code': '37222',
>>>   'state_prov': 'TN'},
>>>  'longitude': -86.78444,
>>>  'population': 530852,
>>>  'is_zip_code': False,
>>>  'is_aggregate': False,
>>>  'long_display': 'Nashville, TN 37222',
>>>  'short_display': 'Nashville, TN',
>>>  'primary_source': None,
>>>  'reference_code': 'us__tn__nashville',
>>>  'primary_timezone': 'America/Chicago',
>>>  'related_shape_id': 6718,
>>>  'primary_source_id': None,
>>>  'is_three_digit_zip_code': False}

Perform radius search around a Geo Object, utilizing a reference code, radius in miles and indicator to return results within the same country. Results returned are a list of Geo Shape IDs or with full_results=True a full list of Geo Objects returned.

reference_code = 'us__tn__nashville'

res = GeoManager.radius_search(
    reference_code=reference_code, radius=10, country_exact=True
)

print(res)
>>> [6702, 6831, 142898, 142897, 142895, 142901, 142903, 142904, 142905, 142910, 142893, 119979, 104924, 258833, 259091, 118948, 
>>> 118950, 119199, 119208, 119565, 119569, 119570, 119978, 259331, 239701, 242429, 6764, 98, 6609, 6818, 242049, 6612, 6621, 
>>> 142899, 142900, 241027, 119206, 119572, 133787, 119196, 119567, 119977, 6622, 134214, 134217, 143024, 179468, 133797, 133808, 
>>> 119980, 119201, 119204, 119205, 119210, 142902, 142906, 142907, 142908, 142909]

Example

For an example microservice implementation with Flask check out this repository.

Of try out the free API with documentation here YAT Demo.