将北美划分为碎片

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国家切成碎片。为此，我使用Geopandas获取北美的地理数据，然后使用GeoVoronoi库创建一个Voronoi图：import matplotlib.pyplot as pltimport geopandas as gpdfrom shapely.ops import cascaded_unionfrom geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_areafrom geovoronoi import voronoi_regions_from_coords, points_to_coordslogging.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' shapesnorth_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 coordinatescoords = points_to_coords(north_am_cities.geometry)# calculate the regionspoly_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()这些代码大部分直接取自 Geovoronoi 文档。然而，当我运行它时，我收到以下错误：

Answer 1

您收到的错误源于以下事实：您正在提取的城市信息包含北美城市很少，或者它们未被正确识别为北美境内。您的问题是关于基于首都创建Voronoi图，因此我包含了一个指向美国首都数据集的链接，以便您可以使用可靠数量的城市测试示例：

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：

对于北美，您可以下载城市 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：