将北美分成几部分

Question

我正在尝试创建北美地图的voronoi图，这意味着根据其首都的位置有效地将该国家划分为多个部分。为此，我正在使用Geopandas获取北美地区的地理数据，然后使用GeoVoronoi库从中创建一个Voronoi图：

import matplotlib.pyplot as plt
import geopandas as gpd
from shapely.ops import cascaded_union

from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area
from geovoronoi import voronoi_regions_from_coords, points_to_coords


logging.basicConfig(level=logging.INFO)
geovoronoi_log = logging.getLogger('geovoronoi')
geovoronoi_log.setLevel(logging.INFO)
geovoronoi_log.propagate = True

#
# load geo data
#

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

# focus on South America, convert to World Mercator (unit: meters)
north_am = world[world.continent == 'North America'].to_crs(epsg=3395)
cities = cities.to_crs(north_am.crs)   # convert city coordinates to same CRS!

# create the bounding shape as union of all South American countries' shapes
north_am_shape = cascaded_union(north_am.geometry)
north_am_cities = cities[cities.geometry.within(north_am_shape)]   # reduce to cities in South America


#
# calculate the Voronoi regions, cut them with the geographic area shape and assign the points to them
#

# convert the pandas Series of Point objects to NumPy array of coordinates
coords = points_to_coords(north_am_cities.geometry)

# calculate the regions
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, north_am_shape)


#
# Plotting
#

fig, ax = subplot_for_map()

plot_voronoi_polys_with_points_in_area(ax, north_am_shape, poly_shapes, pts)

ax.set_title('Cities data for South America from GeoPandas\nand Voronoi regions around them')

plt.tight_layout()
plt.savefig('using_geopandas.png')
plt.show()

此代码大部分直接从Geovoronoi文档中获取。但是，当我运行它时，出现以下错误：

Answer 1

您收到的错误是由于您要提取的城市信息包含很少的北美城市，或者未正确识别为北美范围内的事实。您的问题是关于基于首都创建Voronoi图，因此我加入了a link to a data set for US capitals，以便您可以在可靠的城市中测试示例：

import matplotlib.pyplot as plt
import numpy as np
import geopandas as gpd
from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area
from geovoronoi import voronoi_regions_from_coords

cities = gpd.read_file('us-state-capitals.csv')

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
usa = world[world.name == 'United States of America']
usa = usa.to_crs(epsg=3857)
usa_shape =  usa.iloc[0].geometry

coords = np.array(list(zip(cities.Shape_X,cities.Shape_Y)), dtype='float')

poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, usa_shape)

fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, usa_shape, poly_shapes, coords)
ax.set_title('Cities data for South America from GeoPandas\nand Voronoi regions around them')
plt.tight_layout()
plt.savefig('using_geopandas.png')
plt.show()

生产中：

对于北美，您可以下载a cities CSV并使用以下代码：

import matplotlib.pyplot as plt
import geopandas as gpd
from shapely.ops import cascaded_union
from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area
from geovoronoi import voronoi_regions_from_coords, points_to_coords

cities = gpd.read_file('world_populated_cities.csv')
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
na = world[world.continent == 'North America']
na = na.to_crs(epsg=3857)
cities.geometry.to_crs(epsg=3857)

na_shape = cascaded_union(na.geometry)
cities = cities.to_crs(na.crs)   # convert city coordinates to same CRS!
cities = cities[cities.geometry.within(na_shape)]

coords = points_to_coords(cities.geometry)
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, na_shape)

fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, na_shape, poly_shapes, coords)
ax.set_title('Cities data for South America from GeoPandas\nand Voronoi regions around them')
plt.tight_layout()
plt.savefig('using_geopandas.png')
plt.show()

生产中：