Voronoi regionalizer
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
import plotly.express as px
from shapely.geometry import Point
from srai.constants import WGS84_CRS
from srai.plotting.folium_wrapper import plot_regions
from srai.regionalizers import VoronoiRegionalizer, geocode_to_region_gdf
import geopandas as gpd
import numpy as np
import plotly.express as px
from shapely.geometry import Point
from srai.constants import WGS84_CRS
from srai.plotting.folium_wrapper import plot_regions
from srai.regionalizers import VoronoiRegionalizer, geocode_to_region_gdf
Regionalizer whole Earth¶
Basic usage of VoronoiRegionalizer to cover whole Earth using 6 poles.
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# 6 poles of the Earth
seeds_gdf = gpd.GeoDataFrame(
{
"geometry": [
Point(0, 0),
Point(90, 0),
Point(180, 0),
Point(-90, 0),
Point(0, 90),
Point(0, -90),
]
},
index=[1, 2, 3, 4, 5, 6],
crs=WGS84_CRS,
)
# 6 poles of the Earth
seeds_gdf = gpd.GeoDataFrame(
{
"geometry": [
Point(0, 0),
Point(90, 0),
Point(180, 0),
Point(-90, 0),
Point(0, 90),
Point(0, -90),
]
},
index=[1, 2, 3, 4, 5, 6],
crs=WGS84_CRS,
)
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seeds_gdf.plot()
seeds_gdf.plot()
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<Axes: >
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vr = VoronoiRegionalizer(seeds=seeds_gdf)
vr = VoronoiRegionalizer(seeds=seeds_gdf)
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result_gdf = vr.transform()
result_gdf = vr.transform()
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result_gdf
result_gdf
Out[6]:
| geometry | |
|---|---|
| region_id | |
| 6 | POLYGON ((45.19105 -35.3542, 45.28673 -35.3989... |
| 4 | POLYGON ((-135 -35.08447, -135 -34.99451, -135... |
| 1 | POLYGON ((45 35.08447, 45 34.99451, 45 34.9045... |
| 3 | MULTIPOLYGON (((135.19105 35.3542, 135.28673 3... |
| 2 | POLYGON ((45 0.08996, 45 0.17992, 45 0.26988, ... |
| 5 | POLYGON ((179.74556 44.99972, 179.61833 44.999... |
Globe view¶
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fig = px.choropleth(
result_gdf,
geojson=result_gdf.geometry,
locations=result_gdf.index,
color=result_gdf.index,
color_continuous_scale=px.colors.sequential.Viridis,
)
fig2 = px.scatter_geo(seeds_gdf, lat=seeds_gdf.geometry.y, lon=seeds_gdf.geometry.x)
fig.update_traces(marker={"opacity": 0.6}, selector=dict(type="choropleth"))
fig.add_trace(fig2.data[0])
fig.update_traces(marker_color="white", marker_size=10, selector=dict(type="scattergeo"))
fig.update_layout(coloraxis_showscale=False)
fig.update_geos(
projection_type="orthographic",
projection_rotation_lon=20,
projection_rotation_lat=30,
showlakes=False,
)
fig.update_layout(height=800, width=800, margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show(renderer="png") # replace with fig.show() to allow interactivity
fig = px.choropleth(
result_gdf,
geojson=result_gdf.geometry,
locations=result_gdf.index,
color=result_gdf.index,
color_continuous_scale=px.colors.sequential.Viridis,
)
fig2 = px.scatter_geo(seeds_gdf, lat=seeds_gdf.geometry.y, lon=seeds_gdf.geometry.x)
fig.update_traces(marker={"opacity": 0.6}, selector=dict(type="choropleth"))
fig.add_trace(fig2.data[0])
fig.update_traces(marker_color="white", marker_size=10, selector=dict(type="scattergeo"))
fig.update_layout(coloraxis_showscale=False)
fig.update_geos(
projection_type="orthographic",
projection_rotation_lon=20,
projection_rotation_lat=30,
showlakes=False,
)
fig.update_layout(height=800, width=800, margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show(renderer="png") # replace with fig.show() to allow interactivity
2D OSM View¶
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folium_map = plot_regions(result_gdf)
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(result_gdf)
seeds_gdf.explore(
m=folium_map,
style_kwds=dict(color="#444", opacity=1, fillColor="#f2f2f2", fillOpacity=1),
marker_kwds=dict(radius=3),
)
Out[8]:
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Regionalize single country¶
Drawing a list of arbitrary points inside of the country boundary and using them for regionalization of the same geometry.
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uk_gdf = geocode_to_region_gdf(query=["R62149"], by_osmid=True)
uk_shape = uk_gdf.iloc[0].geometry # get the Polygon
uk_gdf = geocode_to_region_gdf(query=["R62149"], by_osmid=True)
uk_shape = uk_gdf.iloc[0].geometry # get the Polygon
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uk_gdf
uk_gdf
Out[10]:
| geometry | |
|---|---|
| region_id | |
| United Kingdom | MULTIPOLYGON (((-14.01552 57.60263, -14.01459 ... |
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def generate_random_points(shape, n_points=100):
"""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=100):
"""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(uk_shape)
uk_seeds_gdf = gpd.GeoDataFrame(
{"geometry": pts},
index=list(range(len(pts))),
crs=WGS84_CRS,
)
pts = generate_random_points(uk_shape)
uk_seeds_gdf = gpd.GeoDataFrame(
{"geometry": pts},
index=list(range(len(pts))),
crs=WGS84_CRS,
)
Random points on a map¶
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folium_map = plot_regions(uk_gdf, tiles_style="CartoDB positron")
uk_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(uk_gdf, tiles_style="CartoDB positron")
uk_seeds_gdf.explore(
m=folium_map,
style_kwds=dict(color="#444", opacity=1, fillColor="#f2f2f2", fillOpacity=1),
marker_kwds=dict(radius=3),
)
Out[13]:
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vr_uk = VoronoiRegionalizer(seeds=uk_seeds_gdf)
vr_uk = VoronoiRegionalizer(seeds=uk_seeds_gdf)
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uk_result_gdf = vr_uk.transform(gdf=uk_gdf)
uk_result_gdf = vr_uk.transform(gdf=uk_gdf)
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uk_result_gdf.head()
uk_result_gdf.head()
Out[16]:
| geometry | |
|---|---|
| region_id | |
| 5 | POLYGON ((-5.30431 52.13408, -5.20062 52.06371... |
| 29 | POLYGON ((0.52179 50.67975, 0.53415 50.77038, ... |
| 31 | MULTIPOLYGON (((-7.8911 57.87541, -7.80911 57.... |
| 30 | POLYGON ((-2.07895 59.17674, -2.22727 59.22609... |
| 0 | POLYGON ((-0.79009 52.07006, -0.75568 51.98119... |
Generated regions on a map¶
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folium_map = plot_regions(uk_result_gdf, tiles_style="CartoDB positron")
uk_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(uk_result_gdf, tiles_style="CartoDB positron")
uk_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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Higher amount of points¶
Example of railway stations in Germany (5000+ seeds) with multiprocessing.
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stations_csv = gpd.pd.read_csv(
"https://raw.githubusercontent.com/trainline-eu/stations/master/stations.csv",
sep=";",
index_col="id",
usecols=["id", "latitude", "longitude", "country"],
)
stations_csv
stations_csv = gpd.pd.read_csv(
"https://raw.githubusercontent.com/trainline-eu/stations/master/stations.csv",
sep=";",
index_col="id",
usecols=["id", "latitude", "longitude", "country"],
)
stations_csv
Out[18]:
| latitude | longitude | country | |
|---|---|---|---|
| id | |||
| 1 | 44.081790 | 6.001625 | FR |
| 2 | 44.061565 | 5.997373 | FR |
| 3 | 44.063863 | 6.011248 | FR |
| 4 | 44.350000 | 6.350000 | FR |
| 6 | 44.088710 | 6.222982 | FR |
| ... | ... | ... | ... |
| 74703 | 48.815550 | 7.432740 | FR |
| 74704 | 48.265370 | 7.235370 | FR |
| 74705 | 48.262740 | 7.225320 | FR |
| 74706 | 48.794560 | 7.589270 | FR |
| 74707 | 44.360410 | 5.138650 | FR |
70497 rows × 3 columns
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stations = []
positions = set()
for idx, r in stations_csv.iterrows():
if r.country != "DE" or gpd.pd.isna(r.latitude) or gpd.pd.isna(r.longitude):
continue
pos = round(r.longitude, 5), round(r.latitude, 5)
if pos not in positions:
stations.append({"id": idx, "geometry": Point(*pos)})
positions.add(pos)
stations_gdf = gpd.GeoDataFrame(data=stations, crs=WGS84_CRS).set_index("id")
del stations_csv
del stations
del positions
stations_gdf.head()
stations = []
positions = set()
for idx, r in stations_csv.iterrows():
if r.country != "DE" or gpd.pd.isna(r.latitude) or gpd.pd.isna(r.longitude):
continue
pos = round(r.longitude, 5), round(r.latitude, 5)
if pos not in positions:
stations.append({"id": idx, "geometry": Point(*pos)})
positions.add(pos)
stations_gdf = gpd.GeoDataFrame(data=stations, crs=WGS84_CRS).set_index("id")
del stations_csv
del stations
del positions
stations_gdf.head()
Out[19]:
| geometry | |
|---|---|
| id | |
| 6691 | POINT (9.03081 51.73032) |
| 6692 | POINT (11.80785 49.09284) |
| 6693 | POINT (10.48471 53.14212) |
| 6695 | POINT (6.59351 50.44187) |
| 6696 | POINT (8.31165 49.93009) |
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vr_rail = VoronoiRegionalizer(seeds=stations_gdf)
vr_rail = VoronoiRegionalizer(seeds=stations_gdf)
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rail_result_gdf = vr_rail.transform()
rail_result_gdf = vr_rail.transform()
Germany view¶
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folium_map = plot_regions(rail_result_gdf, tiles_style="CartoDB positron")
stations_gdf.explore(
m=folium_map,
style_kwds=dict(color="#444", opacity=1, fillColor="#f2f2f2", fillOpacity=1),
marker_kwds=dict(radius=1),
)
folium_map.fit_bounds([(54.98310, 5.98865), (47.30248, 15.01699)])
folium_map
folium_map = plot_regions(rail_result_gdf, tiles_style="CartoDB positron")
stations_gdf.explore(
m=folium_map,
style_kwds=dict(color="#444", opacity=1, fillColor="#f2f2f2", fillOpacity=1),
marker_kwds=dict(radius=1),
)
folium_map.fit_bounds([(54.98310, 5.98865), (47.30248, 15.01699)])
folium_map
Out[22]:
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Berlin view¶
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# Berlin
folium_map.fit_bounds([(52.51637 + 0.1, 13.40665 + 0.1), (52.51637 - 0.1, 13.40665 - 0.1)])
folium_map
# Berlin
folium_map.fit_bounds([(52.51637 + 0.1, 13.40665 + 0.1), (52.51637 - 0.1, 13.40665 - 0.1)])
folium_map
Out[23]:
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Difference between spherical voronoi and 2d voronoi¶
Showing the difference between working on the sphere and projected 2D plane.
Uses shapely.voronoi_polygons function as an example.
shapely.voronoi_diagram function allows for a quick division of the Earth using list of seeds on a projected 2d plane.
This results in straight lines with angles distorted and polygons differences
might be substantial during comparisons or any area calculations.
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from plotly.subplots import make_subplots
from shapely.ops import voronoi_diagram
from plotly.subplots import make_subplots
from shapely.ops import voronoi_diagram
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pl_gdf = geocode_to_region_gdf(query=["R49715"], by_osmid=True)
pl_gdf_shape = pl_gdf.iloc[0].geometry # get the Polygon
pl_gdf = geocode_to_region_gdf(query=["R49715"], by_osmid=True)
pl_gdf_shape = pl_gdf.iloc[0].geometry # get the Polygon
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pts = generate_random_points(pl_gdf_shape)
pl_seeds_gdf = gpd.GeoDataFrame(
{"geometry": pts},
index=list(range(len(pts))),
crs=WGS84_CRS,
)
pts = generate_random_points(pl_gdf_shape)
pl_seeds_gdf = gpd.GeoDataFrame(
{"geometry": pts},
index=list(range(len(pts))),
crs=WGS84_CRS,
)
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region_polygons = list(
voronoi_diagram(pl_seeds_gdf.geometry.union_all(), envelope=pl_gdf_shape).normalize().geoms
)
region_polygons = list(
voronoi_diagram(pl_seeds_gdf.geometry.union_all(), envelope=pl_gdf_shape).normalize().geoms
)
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pl_regions_2d_gdf = gpd.GeoDataFrame(
{"geometry": [polygon for polygon in region_polygons]},
index=list(range(len(region_polygons))),
crs=WGS84_CRS,
).clip(pl_gdf_shape)
pl_regions_2d_gdf = gpd.GeoDataFrame(
{"geometry": [polygon for polygon in region_polygons]},
index=list(range(len(region_polygons))),
crs=WGS84_CRS,
).clip(pl_gdf_shape)
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pl_regions_2d_gdf
pl_regions_2d_gdf
Out[29]:
| geometry | |
|---|---|
| 15 | MULTIPOLYGON (((21.05031 49.41294, 21.05024 49... |
| 35 | POLYGON ((20.09722 49.74104, 20.93692 49.84709... |
| 46 | POLYGON ((19.13612 50.85364, 19.69096 50.82116... |
| 42 | POLYGON ((20.19607 50.11409, 20.83036 50.54587... |
| 27 | POLYGON ((19.98984 51.03263, 20.27456 51.09956... |
| ... | ... |
| 18 | POLYGON ((23.04528 54.34954, 23.0477 54.34919,... |
| 3 | POLYGON ((22.72553 53.70373, 21.97487 53.88886... |
| 28 | POLYGON ((19.62876 54.46467, 19.63765 54.45915... |
| 47 | POLYGON ((18.83069 54.88522, 18.83433 54.884, ... |
| 4 | POLYGON ((18.60203 54.5009, 17.83428 54.13932,... |
68 rows × 1 columns
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vr_pl = VoronoiRegionalizer(seeds=pl_seeds_gdf)
vr_pl = VoronoiRegionalizer(seeds=pl_seeds_gdf)
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pl_result_gdf = vr_pl.transform(gdf=pl_gdf)
pl_result_gdf = vr_pl.transform(gdf=pl_gdf)
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pl_result_gdf
pl_result_gdf
Out[32]:
| geometry | |
|---|---|
| region_id | |
| 52 | POLYGON ((20.79225 49.38365, 20.92237 49.4317,... |
| 65 | POLYGON ((21.77687 49.41187, 21.76971 49.50182... |
| 21 | POLYGON ((14.73474 51.7833, 14.88359 51.7669, ... |
| 26 | POLYGON ((22.96462 51.33929, 23.10685 51.35927... |
| 63 | POLYGON ((23.36493 52.54332, 23.22156 52.59597... |
| ... | ... |
| 35 | POLYGON ((20.4941 53.75723, 20.58841 53.83679,... |
| 14 | POLYGON ((21.03209 54.32271, 21.06542 54.23343... |
| 36 | POLYGON ((18.51288 53.52042, 18.32731 53.54533... |
| 55 | POLYGON ((17.67888 54.59099, 17.75716 54.50411... |
| 67 | POLYGON ((19.88952 54.39633, 19.82156 54.31356... |
68 rows × 1 columns