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GeoParse

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Website¶

Geospatial Index¶

Binder

This notebook demonstrates how to display:

  • Geohash
  • Google S2
  • Uber H3
In [1]:
import os
import sys

sys.path.append(os.path.abspath("../geoparse/"))
In [2]:
import warnings

import pandas as pd

from geoparse import SpatialIndex, plp

warnings.filterwarnings("ignore")

Read from CSV file¶

In [ ]:
In [5]:
df = pd.read_csv("https://geoparse.io/tutorials/data/fatal_crash_great_britain_2023.csv")
df.head()
Out[5]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30
1 07/01/2023 10:05 51.593701 0.022379 1 1 30
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30
4 16/01/2023 19:22 51.447944 0.117279 2 1 30
In [6]:
len(df)
Out[6]:
1522
In [7]:
plp(df)
Out[7]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [8]:
df = df.head()
df
Out[8]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30
1 07/01/2023 10:05 51.593701 0.022379 1 1 30
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30
4 16/01/2023 19:22 51.447944 0.117279 2 1 30
In [10]:
%%time
df["h3"] = SpatialIndex.ppoint_cell(df.latitude, df.longitude, cell_type="h3", res=15)
df.head()

CPU times: user 68.5 ms, sys: 136 ms, total: 204 ms
Wall time: 1.93 s
Out[10]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit h3
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30 8f194ac22370811
1 07/01/2023 10:05 51.593701 0.022379 1 1 30 8f194e68042a851
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20 8f194ad058a4c04
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30 8f195db6b2d6cca
4 16/01/2023 19:22 51.447944 0.117279 2 1 30 8f194e6deb25ac9
In [11]:
%%time
df[["lat", "lon"]] = SpatialIndex.pcell_point(df.h3, cell_type="h3")
df.head()

CPU times: user 44 ms, sys: 121 ms, total: 165 ms
Wall time: 1.93 s
Out[11]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit h3 lat lon
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30 8f194ac22370811 51.356552 -0.097765
1 07/01/2023 10:05 51.593701 0.022379 1 1 30 8f194e68042a851 51.593698 0.022381
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20 8f194ad058a4c04 51.466687 -0.011286
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30 8f195db6b2d6cca 51.671579 -0.037540
4 16/01/2023 19:22 51.447944 0.117279 2 1 30 8f194e6deb25ac9 51.447941 0.117280
In [12]:
max(abs(df.latitude - df.lat)), max(abs(df.longitude - df.lon))
Out[12]:
(3.387398464838043e-06, 6.242061589473158e-06)
In [ ]:
In [ ]:
In [13]:
%%time
df["s2"] = SpatialIndex.ppoint_cell(df.latitude, df.longitude, cell_type="s2", res=30)
df.head()

CPU times: user 49.1 ms, sys: 122 ms, total: 171 ms
Wall time: 1.88 s
Out[13]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit h3 lat lon s2
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30 8f194ac22370811 51.356552 -0.097765 487607526cb67e75
1 07/01/2023 10:05 51.593701 0.022379 1 1 30 8f194e68042a851 51.593698 0.022381 47d8a749f4303a43
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20 8f194ad058a4c04 51.466687 -0.011286 4876027b49457ecb
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30 8f195db6b2d6cca 51.671579 -0.037540 48761f9ac2a128c7
4 16/01/2023 19:22 51.447944 0.117279 2 1 30 8f194e6deb25ac9 51.447941 0.117280 47d8ae95f533fc95
In [14]:
%%time
df[["lat", "lon"]] = SpatialIndex.pcell_point(df.s2, cell_type="s2")
df.head()

CPU times: user 43.5 ms, sys: 122 ms, total: 165 ms
Wall time: 1.95 s
Out[14]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit h3 lat lon s2
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30 8f194ac22370811 51.356551 -0.097759 487607526cb67e75
1 07/01/2023 10:05 51.593701 0.022379 1 1 30 8f194e68042a851 51.593701 0.022379 47d8a749f4303a43
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20 8f194ad058a4c04 51.466689 -0.011289 4876027b49457ecb
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30 8f195db6b2d6cca 51.671577 -0.037543 48761f9ac2a128c7
4 16/01/2023 19:22 51.447944 0.117279 2 1 30 8f194e6deb25ac9 51.447944 0.117279 47d8ae95f533fc95
In [15]:
max(abs(df.latitude - df.lat)), max(abs(df.longitude - df.lon))
Out[15]:
(3.998487585477051e-08, 3.7182211115593944e-08)
In [ ]:
In [ ]:
In [23]:
%%time
df["geohash"] = SpatialIndex.ppoint_cell(df.latitude, df.longitude, cell_type="geohash", res=10)
df.head()

CPU times: user 51.5 ms, sys: 124 ms, total: 175 ms
Wall time: 2.16 s
Out[23]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit h3 lat lon s2 geohash
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30 8f194ac22370811 51.356551 -0.097759 487607526cb67e75 gcpujv0vd4
1 07/01/2023 10:05 51.593701 0.022379 1 1 30 8f194e68042a851 51.593701 0.022379 47d8a749f4303a43 u10j882716
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20 8f194ad058a4c04 51.466689 -0.011289 4876027b49457ecb gcpuz9pz0y
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30 8f195db6b2d6cca 51.671577 -0.037543 48761f9ac2a128c7 gcpvzns8v0
4 16/01/2023 19:22 51.447944 0.117279 2 1 30 8f194e6deb25ac9 51.447944 0.117279 47d8ae95f533fc95 u10hdtv3uz
In [24]:
%%time
df[["lat", "lon"]] = SpatialIndex.pcell_point(df.geohash, cell_type="geohash")
df.head()

CPU times: user 40.1 ms, sys: 116 ms, total: 156 ms
Wall time: 1.93 s
Out[24]:
date time latitude longitude number_of_vehicles number_of_casualties speed_limit h3 lat lon s2 geohash
0 03/01/2023 19:12 51.356551 -0.097759 1 1 30 8f194ac22370811 51.35655 -0.0978 487607526cb67e75 gcpujv0vd4
1 07/01/2023 10:05 51.593701 0.022379 1 1 30 8f194e68042a851 51.59370 0.0224 47d8a749f4303a43 u10j882716
2 14/01/2023 16:15 51.466689 -0.011289 1 1 20 8f194ad058a4c04 51.46669 -0.0113 4876027b49457ecb gcpuz9pz0y
3 15/01/2023 19:51 51.671577 -0.037543 1 1 30 8f195db6b2d6cca 51.67158 -0.0375 48761f9ac2a128c7 gcpvzns8v0
4 16/01/2023 19:22 51.447944 0.117279 2 1 30 8f194e6deb25ac9 51.44794 0.1173 47d8ae95f533fc95 u10hdtv3uz
In [25]:
max(abs(df.latitude - df.lat)), max(abs(df.longitude - df.lon))
Out[25]:
(3.999999997006398e-06, 4.300000000000137e-05)
In [ ]:
In [ ]: