Adjacency neighbourhood

In [1]:

import numpy as np
from shapely.geometry import Point
import geopandas as gpd

from srai.constants import WGS84_CRS
from srai.neighbourhoods import AdjacencyNeighbourhood
from srai.regionalizers import (
    AdministrativeBoundaryRegionalizer,
    VoronoiRegionalizer,
    geocode_to_region_gdf,
)
from srai.plotting.folium_wrapper import plot_regions, plot_neighbours, plot_all_neighbourhood

import numpy as np from shapely.geometry import Point import geopandas as gpd from srai.constants import WGS84_CRS from srai.neighbourhoods import AdjacencyNeighbourhood from srai.regionalizers import ( AdministrativeBoundaryRegionalizer, VoronoiRegionalizer, geocode_to_region_gdf, ) from srai.plotting.folium_wrapper import plot_regions, plot_neighbours, plot_all_neighbourhood

Adjacency Neighbourhood¶

It can generate neighbourhoods for all geodataframes with touching geometries.

Real boundaries example - Italy¶

In [2]:

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)

Out[2]:

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In [3]:

regionalizer = AdministrativeBoundaryRegionalizer(admin_level=4)
it_regions_gdf = regionalizer.transform(it_gdf)

regionalizer = AdministrativeBoundaryRegionalizer(admin_level=4) it_regions_gdf = regionalizer.transform(it_gdf)

Loading boundaries: 68: 100%|██████████| 6/6 [00:26<00:00,  4.50s/it]
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/srai/regionalizers/administrative_boundary_regionalizer.py:167: FutureWarning: `unary_union` returned None due to all-None GeoSeries. In future, `unary_union` will return 'GEOMETRYCOLLECTION EMPTY' instead.
  ].geometry.unary_union

In [4]:

plot_regions(it_regions_gdf)

plot_regions(it_regions_gdf)

Out[4]:

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In [5]:

neighbourhood = AdjacencyNeighbourhood(it_regions_gdf)

neighbourhood = AdjacencyNeighbourhood(it_regions_gdf)

Nearest neighbours¶

In [6]:

region_id = "Lazio"
neighbours = neighbourhood.get_neighbours(region_id)
neighbours

region_id = "Lazio" neighbours = neighbourhood.get_neighbours(region_id) neighbours

Out[6]:

{'Abruzzo', 'Campania', 'Marche', 'Molise', 'Tuscany', 'Umbria'}

In [7]:

plot_neighbours(it_regions_gdf, region_id, neighbours)

plot_neighbours(it_regions_gdf, region_id, neighbours)

Out[7]:

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Neighbours at a distance¶

In [8]:

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

Out[8]:

{'Lazio', 'Molise'}

In [9]:

plot_neighbours(it_regions_gdf, region_id, neighbours)

plot_neighbours(it_regions_gdf, region_id, neighbours)

Out[9]:

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Regions without neighbours¶

In [10]:

region_id = "Sardinia"
neighbours = neighbourhood.get_neighbours(region_id)
neighbours

region_id = "Sardinia" neighbours = neighbourhood.get_neighbours(region_id) neighbours

Out[10]:

set()

In [11]:

plot_neighbours(it_regions_gdf, region_id, neighbours)

plot_neighbours(it_regions_gdf, region_id, neighbours)

Out[11]:

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Plotting all neighbourhood¶

In [12]:

region_id = "Campania"
plot_all_neighbourhood(it_regions_gdf, region_id, neighbourhood)

region_id = "Campania" plot_all_neighbourhood(it_regions_gdf, region_id, neighbourhood)

Out[12]:

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Voronoi example - Australia¶

In [13]:

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)

Out[13]:

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In [14]:

import numpy as np
from shapely.geometry import Point
import geopandas as gpd
from srai.constants import WGS84_CRS


def generate_random_points(shape, n_points=500):
    minx, miny, maxx, maxy = shape.bounds
    pts = []

    while len(pts) < 4:
        randx = np.random.uniform(minx, maxx, n_points)
        randy = np.random.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

import numpy as np from shapely.geometry import Point import geopandas as gpd from srai.constants import WGS84_CRS def generate_random_points(shape, n_points=500): minx, miny, maxx, maxy = shape.bounds pts = [] while len(pts) < 4: randx = np.random.uniform(minx, maxx, n_points) randy = np.random.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

In [15]:

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, )

In [16]:

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)

Generating spherical polygons: 100%|██████████| 86/86 [00:01<00:00, 52.50it/s]
Interpolating edges: 100%|██████████| 329/329 [00:00<00:00, 903.54it/s] 
Generating polygons: 100%|██████████| 86/86 [00:00<00:00, 545.37it/s]

In [17]:

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), )

Out[17]:

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In [18]:

neighbourhood = AdjacencyNeighbourhood(regions_gdf=au_result_gdf)

neighbourhood = AdjacencyNeighbourhood(regions_gdf=au_result_gdf)

Nearest neighbours¶

In [19]:

region_id = 0
neighbours = neighbourhood.get_neighbours(region_id)
neighbours

region_id = 0 neighbours = neighbourhood.get_neighbours(region_id) neighbours

Out[19]:

{4, 13, 30, 57}

In [20]:

plot_neighbours(au_result_gdf, region_id, neighbours)

plot_neighbours(au_result_gdf, region_id, neighbours)

Out[20]:

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Neighbours at a distance¶

In [21]:

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

Out[21]:

{1, 5, 25, 29, 34, 36, 40, 58, 59, 60, 71, 74, 83}

In [22]:

plot_neighbours(au_result_gdf, region_id, neighbours)

plot_neighbours(au_result_gdf, region_id, neighbours)

Out[22]:

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Plotting all neighbourhood¶

In [23]:

region_id = 0
plot_all_neighbourhood(au_result_gdf, region_id, neighbourhood)

region_id = 0 plot_all_neighbourhood(au_result_gdf, region_id, neighbourhood)

Out[23]:

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