Voronoi regionalizer
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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 ((135.19105 -35.35420, 135.28673 -35.3... |
| 4 | POLYGON ((-45.00000 -0.08996, -45.00000 -0.179... |
| 1 | POLYGON ((45.00000 35.08447, 45.00000 34.99451... |
| 3 | MULTIPOLYGON (((180.00000 44.82000, 180.00000 ... |
| 2 | POLYGON ((45.00000 0.17992, 45.00000 0.26988, ... |
| 5 | POLYGON ((180.00000 89.82000, 180.00000 89.730... |
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),
)
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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 | |
| 16 | POLYGON ((-4.25677 50.15735, -4.12892 50.20430... |
| 15 | MULTIPOLYGON (((-8.59909 58.01959, -8.43344 57... |
| 13 | MULTIPOLYGON (((-6.29736 57.72209, -6.46049 57... |
| 21 | POLYGON ((-5.30498 54.90526, -5.39437 54.82989... |
| 7 | POLYGON ((-2.31268 50.84659, -2.44405 50.80166... |
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 |
| ... | ... | ... | ... |
| 74682 | 48.300290 | 4.074470 | FR |
| 74683 | 47.874740 | 7.028350 | FR |
| 74684 | 48.086210 | 7.278890 | FR |
| 74685 | 42.044830 | -6.564990 | ES |
| 74686 | 42.067773 | 13.897856 | IT |
70493 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()
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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.unary_union, envelope=pl_gdf_shape).normalize().geoms
)
region_polygons = list(
voronoi_diagram(pl_seeds_gdf.geometry.unary_union, 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 | |
|---|---|
| 38 | POLYGON ((17.60530 50.17107, 17.60560 50.17112... |
| 17 | MULTIPOLYGON (((15.74518 50.89203, 16.35362 51... |
| 4 | POLYGON ((15.19325 51.97258, 15.43387 51.98188... |
| 55 | POLYGON ((14.62933 52.55282, 15.19325 51.97258... |
| 65 | POLYGON ((16.49405 51.87148, 17.00174 51.77910... |
| ... | ... |
| 43 | POLYGON ((23.24871 53.73415, 22.64134 54.35379... |
| 22 | POLYGON ((21.33515 53.76938, 21.25720 53.84671... |
| 62 | POLYGON ((18.74340 53.96897, 18.71814 53.76253... |
| 63 | POLYGON ((19.04595 54.46026, 18.74340 53.96897... |
| 3 | POLYGON ((19.04595 54.46026, 18.42531 55.02717... |
69 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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Generating polygons: 78%|███████▊ | 54/69 [00:00<00:00, 480.68it/s]
Generating polygons: 100%|██████████| 69/69 [00:00<00:00, 440.00it/s]
In [32]:
Copied!
pl_result_gdf
pl_result_gdf
Out[32]:
| geometry | |
|---|---|
| region_id | |
| 68 | MULTIPOLYGON (((20.92719 49.31905, 21.02906 49... |
| 2 | POLYGON ((17.53045 50.31334, 17.60904 50.38804... |
| 26 | POLYGON ((16.06704 51.29343, 16.19999 51.33600... |
| 64 | POLYGON ((15.61799 51.77865, 15.64896 51.68353... |
| 56 | POLYGON ((14.59810 52.30070, 14.73236 52.24941... |
| ... | ... |
| 18 | POLYGON ((17.49096 54.91668, 17.62429 54.85982... |
| 31 | POLYGON ((18.08483 55.01241, 18.22513 54.97076... |
| 67 | POLYGON ((19.74422 53.74768, 19.73266 53.83801... |
| 23 | POLYGON ((21.18855 54.30363, 21.22175 54.21526... |
| 9 | POLYGON ((21.74205 54.30899, 21.62791 54.24153... |
69 rows × 1 columns
In [33]:
Copied!
choropleth_1 = px.choropleth(
pl_result_gdf,
geojson=pl_result_gdf.geometry,
locations=pl_result_gdf.index,
color=pl_result_gdf.index,
color_continuous_scale=px.colors.qualitative.Plotly,
)
choropleth_2 = px.choropleth(
pl_regions_2d_gdf,
geojson=pl_regions_2d_gdf.geometry,
locations=pl_regions_2d_gdf.index,
color=pl_regions_2d_gdf.index,
color_continuous_scale=px.colors.qualitative.Plotly,
)
points_plot = px.scatter_geo(pl_seeds_gdf, lat=pl_seeds_gdf.geometry.y, lon=pl_seeds_gdf.geometry.x)
fig = make_subplots(
rows=2,
cols=2,
specs=[
[{"type": "scattergeo"}, {"type": "scattergeo"}],
[{"type": "scattergeo"}, {"type": "scattergeo"}],
],
horizontal_spacing=0.01,
vertical_spacing=0.0,
)
fig.add_trace(choropleth_1["data"][0], row=1, col=1)
fig.add_trace(choropleth_1["data"][0], row=2, col=1)
fig.add_trace(choropleth_2["data"][0], row=1, col=2)
fig.add_trace(choropleth_2["data"][0], row=2, col=2)
for r in [1, 2]:
for c in [1, 2]:
fig.add_trace(points_plot.data[0], row=r, col=c)
minx, miny, maxx, maxy = pl_gdf_shape.bounds
fig.update_traces(marker_color="black", marker_size=6, selector=dict(type="scattergeo"))
fig.update_layout(coloraxis_showscale=False)
fig.update_geos(
projection_type="natural earth",
lataxis_range=[miny - 1, maxy + 1],
lonaxis_range=[minx - 1, maxx + 1],
resolution=50,
row=1,
showlakes=False,
)
fig.update_geos(
projection_type="natural earth",
lataxis_range=[miny + 1, maxy - 1],
lonaxis_range=[minx + 2, maxx - 2],
resolution=50,
row=2,
showlakes=False,
)
fig.update_traces(marker={"opacity": 0.6}, selector=dict(type="choropleth"), row=1)
fig.update_traces(marker={"opacity": 0.25}, selector=dict(type="choropleth"), row=2)
fig.update_layout(
height=800,
width=800,
title_text="Side By Side Subplots (Left: Spherical voronoi, Right: 2D voronoi)",
margin={"r": 5, "t": 50, "l": 5, "b": 0},
)
fig.show(renderer="png") # replace with fig.show() to allow interactivity
choropleth_1 = px.choropleth(
pl_result_gdf,
geojson=pl_result_gdf.geometry,
locations=pl_result_gdf.index,
color=pl_result_gdf.index,
color_continuous_scale=px.colors.qualitative.Plotly,
)
choropleth_2 = px.choropleth(
pl_regions_2d_gdf,
geojson=pl_regions_2d_gdf.geometry,
locations=pl_regions_2d_gdf.index,
color=pl_regions_2d_gdf.index,
color_continuous_scale=px.colors.qualitative.Plotly,
)
points_plot = px.scatter_geo(pl_seeds_gdf, lat=pl_seeds_gdf.geometry.y, lon=pl_seeds_gdf.geometry.x)
fig = make_subplots(
rows=2,
cols=2,
specs=[
[{"type": "scattergeo"}, {"type": "scattergeo"}],
[{"type": "scattergeo"}, {"type": "scattergeo"}],
],
horizontal_spacing=0.01,
vertical_spacing=0.0,
)
fig.add_trace(choropleth_1["data"][0], row=1, col=1)
fig.add_trace(choropleth_1["data"][0], row=2, col=1)
fig.add_trace(choropleth_2["data"][0], row=1, col=2)
fig.add_trace(choropleth_2["data"][0], row=2, col=2)
for r in [1, 2]:
for c in [1, 2]:
fig.add_trace(points_plot.data[0], row=r, col=c)
minx, miny, maxx, maxy = pl_gdf_shape.bounds
fig.update_traces(marker_color="black", marker_size=6, selector=dict(type="scattergeo"))
fig.update_layout(coloraxis_showscale=False)
fig.update_geos(
projection_type="natural earth",
lataxis_range=[miny - 1, maxy + 1],
lonaxis_range=[minx - 1, maxx + 1],
resolution=50,
row=1,
showlakes=False,
)
fig.update_geos(
projection_type="natural earth",
lataxis_range=[miny + 1, maxy - 1],
lonaxis_range=[minx + 2, maxx - 2],
resolution=50,
row=2,
showlakes=False,
)
fig.update_traces(marker={"opacity": 0.6}, selector=dict(type="choropleth"), row=1)
fig.update_traces(marker={"opacity": 0.25}, selector=dict(type="choropleth"), row=2)
fig.update_layout(
height=800,
width=800,
title_text="Side By Side Subplots (Left: Spherical voronoi, Right: 2D voronoi)",
margin={"r": 5, "t": 50, "l": 5, "b": 0},
)
fig.show(renderer="png") # replace with fig.show() to allow interactivity
