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 Euclidean distance matrix. Coordinates are converted from geodetic to ECEF and back.
- Using kd-tree with Euclidean 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.
Metadata
Release files for houtu 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| houtu-0.0.3.tar.gz | 6.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| houtu-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.4 MB
Release files / houtu-0.0.3.tar.gz
| Download URL | houtu-0.0.3.tar.gz |
|---|---|
| Size | 6.2 MB |
| Tags | Source |
|
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Release files / houtu-0.0.3-py3-none-any.whl
| Download URL | houtu-0.0.3-py3-none-any.whl |
|---|---|
| Size | 6.2 MB |
| Tags | Python 3 |
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