houtu 0.0.2

Offline fast reverse geocoding. Named after the Chinese Goddess of the Earth Houtu.

houtu

Offline fast reverse geocoding. Named after the Chinese Goddess of the Earth Houtu.

Various implementations are included.

• Naive computation of haversine distance matrix. Coordinates are not converted.
• Using ball-tree with haversine metric. Coordinates are not converted.
• Naive computation of euclidic distance matrix. Coordinates are converted from geodetic to ECEF and back.
• Using kd-tree with euclidic metric. Coordinates are converted from geodetic to ECEF and back.

Install

• requires Python 3.8+
• run pip install houtu

Use

import numpy as np
from houtu import ReverseGeocode
rg = ReverseGeocode()
arr = np.array([
    [25.04776, 121.53185],  # taipei
    [48.13743, 11.57549],  # munich
], dtype=np.float32)
coords, distances, cities = rg.query(arr, k=2, form="degrees")
print(cities)

Output

[[
  City(name='Taipei', feature_class='P', feature_code='PPLC', country_code='TW', admin1_code='04', admin2_code='TPE', admin3_code='', admin4_code='', population=7871900, elevation=None, timezone='Asia/Taipei'),
  City(name='Neihu', feature_class='P', feature_code='PPL', country_code='TW', admin1_code='04', admin2_code='TPE', admin3_code='A14', admin4_code='63000100010', population=271594, elevation=None, timezone='Asia/Taipei')
], [
  City(name='Munich', feature_class='P', feature_code='PPLA', country_code='DE', admin1_code='02', admin2_code='091', admin3_code='09162', admin4_code='09162000', population=1260391, elevation=None, timezone='Europe/Berlin'),
  City(name='Bogenhausen', feature_class='P', feature_code='PPLX', country_code='DE', admin1_code='02', admin2_code='091', admin3_code='09162', admin4_code='09162000', population=77542, elevation=None, timezone='Europe/Berlin')
]]

Benchmark

Batch of 2

class	time/s
ReverseGeocodeBruteHaversine	35.674
ReverseGeocodeBallHaversine	0.192
ReverseGeocodeBruteEuclidic	22.110
ReverseGeocodeKdLearn	0.392
ReverseGeocodeKdScipy	0.317

Batch of 100

class	time/s
ReverseGeocodeBruteHaversine	193.119
ReverseGeocodeBallHaversine	0.918
ReverseGeocodeBruteEuclidic	86.881
ReverseGeocodeKdLearn	0.122
ReverseGeocodeKdScipy	0.095

Optimize

vincenty haversine
mean_absolute_error IUGG radius of sphere of same volume (R3) 897.7743386541117
mean_relative_error Spherical Earth Approx. of Radius (RE) 0.0018409171841267883
geodesic haversine
mean_absolute_error IUGG radius of sphere of same volume (R3) 897.7743386541117
mean_relative_error Spherical Earth Approx. of Radius (RE) 0.0018409171841267883
vincenty euclidic {'mean_absolute_error': 976.6828669488417, 'mean_relative_error': 0.0006505034968118167}
geodesic euclidic {'mean_absolute_error': 976.628990753796, 'mean_relative_error': 0.0006465253527447903}

Development

• run tests python -m unittest discover -s tests

Sources

Cities database file houtu/data/cities1000.txt.xz is downloaded and recompressed from http://download.geonames.org/export/dump/cities1000.zip (Creative Commons Attribution 4.0 License).

GeoNames issues

There are about 50 pairs of cities which have the exact same coordinates, for example Fajã de Baixo and Rosto de Cão.