Adjacency neighbourhood
Run this notebook in Google Colab:
Remember to install the srai library before running the notebook:
%pip install srai[all]
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import geopandas as gpd
import numpy as np
from shapely.geometry import Point
from srai.constants import WGS84_CRS
from srai.neighbourhoods import AdjacencyNeighbourhood
from srai.plotting.folium_wrapper import plot_all_neighbourhood, plot_neighbours, plot_regions
from srai.regionalizers import (
AdministrativeBoundaryRegionalizer,
VoronoiRegionalizer,
geocode_to_region_gdf,
)
import geopandas as gpd
import numpy as np
from shapely.geometry import Point
from srai.constants import WGS84_CRS
from srai.neighbourhoods import AdjacencyNeighbourhood
from srai.plotting.folium_wrapper import plot_all_neighbourhood, plot_neighbours, plot_regions
from srai.regionalizers import (
AdministrativeBoundaryRegionalizer,
VoronoiRegionalizer,
geocode_to_region_gdf,
)
Adjacency Neighbourhood¶
It can generate neighbourhoods for all geodataframes with touching geometries.
Real boundaries example - Italy¶
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it_gdf = geocode_to_region_gdf(query=["R365331"], by_osmid=True)
plot_regions(it_gdf)
it_gdf = geocode_to_region_gdf(query=["R365331"], by_osmid=True)
plot_regions(it_gdf)
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regionalizer = AdministrativeBoundaryRegionalizer(admin_level=4)
it_regions_gdf = regionalizer.transform(it_gdf)
regionalizer = AdministrativeBoundaryRegionalizer(admin_level=4)
it_regions_gdf = regionalizer.transform(it_gdf)
/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/overpass/api.py:126: SyntaxWarning: "is not" with a literal. Did you mean "!="? if responseformat is not "geojson":
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plot_regions(it_regions_gdf)
plot_regions(it_regions_gdf)
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neighbourhood = AdjacencyNeighbourhood(it_regions_gdf)
neighbourhood = AdjacencyNeighbourhood(it_regions_gdf)
Nearest neighbours¶
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region_id = "Lazio"
neighbours = neighbourhood.get_neighbours(region_id)
neighbours
region_id = "Lazio"
neighbours = neighbourhood.get_neighbours(region_id)
neighbours
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{'Abruzzo', 'Campania', 'Marche', 'Molise', 'Tuscany', 'Umbria'}
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plot_neighbours(it_regions_gdf, region_id, neighbours)
plot_neighbours(it_regions_gdf, region_id, neighbours)
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Neighbours at a distance¶
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region_id = "Basilicata"
neighbours = neighbourhood.get_neighbours_at_distance(region_id, 2)
neighbours
region_id = "Basilicata"
neighbours = neighbourhood.get_neighbours_at_distance(region_id, 2)
neighbours
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{'Lazio', 'Molise'}
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plot_neighbours(it_regions_gdf, region_id, neighbours)
plot_neighbours(it_regions_gdf, region_id, neighbours)
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Regions without neighbours¶
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region_id = "Sardinia"
neighbours = neighbourhood.get_neighbours(region_id)
neighbours
region_id = "Sardinia"
neighbours = neighbourhood.get_neighbours(region_id)
neighbours
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set()
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plot_neighbours(it_regions_gdf, region_id, neighbours)
plot_neighbours(it_regions_gdf, region_id, neighbours)
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Plotting all neighbourhood¶
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region_id = "Campania"
plot_all_neighbourhood(it_regions_gdf, region_id, neighbourhood)
region_id = "Campania"
plot_all_neighbourhood(it_regions_gdf, region_id, neighbourhood)
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Voronoi example - Australia¶
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au_gdf = geocode_to_region_gdf(query=["R80500"], by_osmid=True)
plot_regions(au_gdf)
au_gdf = geocode_to_region_gdf(query=["R80500"], by_osmid=True)
plot_regions(au_gdf)
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def generate_random_points(shape, n_points=500):
"""Generates random points."""
minx, miny, maxx, maxy = shape.bounds
pts = []
rng = np.random.default_rng()
while len(pts) < 4:
randx = rng.uniform(minx, maxx, n_points)
randy = rng.uniform(miny, maxy, n_points)
coords = np.vstack((randx, randy)).T
# use only the points inside the geographic area
pts = [p for p in list(map(Point, coords)) if p.within(shape)]
del coords # not used any more
return pts
def generate_random_points(shape, n_points=500):
"""Generates random points."""
minx, miny, maxx, maxy = shape.bounds
pts = []
rng = np.random.default_rng()
while len(pts) < 4:
randx = rng.uniform(minx, maxx, n_points)
randy = rng.uniform(miny, maxy, n_points)
coords = np.vstack((randx, randy)).T
# use only the points inside the geographic area
pts = [p for p in list(map(Point, coords)) if p.within(shape)]
del coords # not used any more
return pts
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pts = generate_random_points(au_gdf.geometry[0])
au_seeds_gdf = gpd.GeoDataFrame(
{"geometry": pts},
index=list(range(len(pts))),
crs=WGS84_CRS,
)
pts = generate_random_points(au_gdf.geometry[0])
au_seeds_gdf = gpd.GeoDataFrame(
{"geometry": pts},
index=list(range(len(pts))),
crs=WGS84_CRS,
)
/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/geopandas/geoseries.py:720: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` val = getattr(super(), mtd)(*args, **kwargs)
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vr = VoronoiRegionalizer(seeds=au_seeds_gdf)
au_result_gdf = vr.transform(gdf=au_gdf)
vr = VoronoiRegionalizer(seeds=au_seeds_gdf)
au_result_gdf = vr.transform(gdf=au_gdf)
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folium_map = plot_regions(au_result_gdf, tiles_style="CartoDB positron")
au_seeds_gdf.explore(
m=folium_map,
style_kwds=dict(color="#444", opacity=1, fillColor="#f2f2f2", fillOpacity=1),
marker_kwds=dict(radius=3),
)
folium_map = plot_regions(au_result_gdf, tiles_style="CartoDB positron")
au_seeds_gdf.explore(
m=folium_map,
style_kwds=dict(color="#444", opacity=1, fillColor="#f2f2f2", fillOpacity=1),
marker_kwds=dict(radius=3),
)
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neighbourhood = AdjacencyNeighbourhood(regions_gdf=au_result_gdf)
neighbourhood = AdjacencyNeighbourhood(regions_gdf=au_result_gdf)
Nearest neighbours¶
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region_id = 0
neighbours = neighbourhood.get_neighbours(region_id)
neighbours
region_id = 0
neighbours = neighbourhood.get_neighbours(region_id)
neighbours
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{12, 67, 69, 74}
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plot_neighbours(au_result_gdf, region_id, neighbours)
plot_neighbours(au_result_gdf, region_id, neighbours)
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Neighbours at a distance¶
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region_id = 0
neighbours = neighbourhood.get_neighbours_at_distance(region_id, 3)
neighbours
region_id = 0
neighbours = neighbourhood.get_neighbours_at_distance(region_id, 3)
neighbours
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{1, 6, 7, 9, 13, 14, 25, 35, 43, 47, 54, 62, 66, 78}
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plot_neighbours(au_result_gdf, region_id, neighbours)
plot_neighbours(au_result_gdf, region_id, neighbours)
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