GeoParse
It's all about points 
lines 
and polygons 
Website¶
Visualization¶
This notebook demonstrates how to display:
- Points, lines, and polygons.
- Their heatmaps and clusters.
- Their coverage areas using geospatial cells such as H3, S2, and Geohash.
- Geospatial cells on a map based on their indexes.
- OSM roads and buildings using their IDs.
In [1]:
import sys
import os
sys.path.append(os.path.abspath("../geoparse/"))
In [2]:
import warnings
import geopandas as gpd
import pandas as pd
from geoparse import plp
warnings.filterwarnings("ignore")
Read from CSV file¶
In [3]:
df = pd.read_csv("data/great_britain_road_casualties-2023.csv")
df.head()
Out[3]:
| date | time | lat | lon | 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 [4]:
len(df)
Out[4]:
1522
In [5]:
plp(df)
Out[5]:
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In [6]:
plp(df, heatmap=True, cluster=True)
Out[6]:
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In [7]:
plp(df, point_color="speed_limit", point_popup={"Speed limit": "speed_limit"})
Out[7]:
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Read a Polygon from Geo File¶
In [8]:
gdf = gpd.read_file("data/london.geojson")
gdf
Out[8]:
| name | geometry | |
|---|---|---|
| 0 | London | POLYGON ((-0.51035 51.46822, -0.51028 51.46837... |
In [9]:
plp(gdf)
Out[9]:
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In [10]:
plp(gdf, centroid=True)
Out[10]:
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In [11]:
plp(gdf, h3_res=6)
Out[11]:
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In [12]:
plp(gdf, h3_res=10, compact=True)
Out[12]:
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In [13]:
plp(gdf, s2_res=12)
Out[13]:
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In [14]:
plp(gdf, s2_res=14, compact=True)
Out[14]:
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In [15]:
plp(gdf, geohash_res=5)
Out[15]:
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In [16]:
plp(gdf, geohash_res=7, compact=True)
Out[16]:
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In [ ]:
Mutiple data frames¶
In [17]:
plp([df, gdf], h3_res=6)
Out[17]:
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Geospatial Cells¶
In [18]:
import h3
lat, lon = 41.87, -87.78
# Get the H3 index at resolution 6 for the central point
h3_index = h3.latlng_to_cell(lat, lon, 6)
# Get adjacent H3 cells including the central cell itself
h3_cells = h3.grid_disk(h3_index, 1) # k_ring with radius 1 returns the central cell + neighbors
h3_cells
Out[18]:
['862664c87ffffff', '862664cb7ffffff', '862664c97ffffff', '862664c9fffffff', '862664c8fffffff', '862664cafffffff', '862664ca7ffffff']
In [19]:
plp(cells=list(h3_cells), cell_type="h3")
Out[19]:
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OSM Ways¶
In [24]:
plp(osm_ways=[335265936, 53820456, 1117218957], s2_res=20, compact=True)
Out[24]:
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In [25]:
plp(osm_ways=[260909736, 54454413])
Out[25]:
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In [ ]: