Python：从Shapefile到Data Cartopy贴图的Dataframe中的多边形迭代

问题描述

我正在根据某些值在cartopy地图上为国家着色。我正在使用来自https://www.naturalearthdata.com/

的geopandas和shapefile

在遍历数据框df以获取某些国家/地区的几何图形时，我遇到了一个问题。我可以得到具有Multipolygon几何体的国家/地区的几何图形，但是我无法获得具有多边形几何体的国家/地区的几何图形，例如比利时或奥地利。

这是我的代码：

#imports
import matplotlib.pyplot as plt
import matplotlib
import cartopy
from cartopy.io import shapereader
import cartopy.crs as ccrs
import geopandas
import numpy as np

# get natural earth data (http://www.naturalearthdata.com/)
# get country borders
resolution = '10m'
category = 'cultural'
name = 'admin_0_countries'
shpfilename = shapereader.natural_earth(resolution,category,name)

# read the shapefile using geopandas
df = geopandas.read_file(shpfilename)


# Set up the canvas
fig = plt.figure(figsize=(20,20))
central_lon,central_lat = 0,45
extent = [-10,28,35,65]
ax = plt.axes(projection=cartopy.crs.Orthographic(central_lon,central_lat))
ax.set_extent(extent)
ax.gridlines()

# Add natural earth features and borders
ax.add_feature(cartopy.feature.BORDERS,linestyle='-',alpha=0.8)
ax.add_feature(cartopy.feature.OCEAN,facecolor=("lightblue"))
ax.add_feature(cartopy.feature.LAND,facecolor=("lightgreen"),alpha=0.35)
ax.coastlines(resolution='10m')

# Countries and value
countries = ['Sweden','Netherlands','Ireland','Denmark','Germany','Greece','France','Spain','Portugal','Italy','United Kingdom','Austria']
value = [47.44,32.75,27.53,23.21,20.08,18.08,17.23,13.59,12.13,5.66,22.43,7]

# normalise values
value_norm = (value-np.nanmin(value))/(np.nanmax(value) - np.nanmin(value))

# Colourmap
cmap = matplotlib.cm.get_cmap('YlOrBr')


for country,value_norm in zip(countries,value_norm):
    # read the borders of the country in this loop
    poly = df.loc[df['ADMIN'] == country]['geometry'].values[0]
    # get the color for this country
    rgba = cmap(value_norm)
    # plot the country on a map
    ax.add_geometries(poly,crs=ccrs.PlateCarree(),facecolor=rgba,edgecolor='none',zorder=1)

# Add a scatter plot of the original data so the colorbar has the correct numbers
dummy_scat = ax.scatter(value,value,c=value,cmap=cmap,zorder=0)
fig.colorbar(mappable=dummy_scat,label='Percentage of Do and Dont`s [%]',orientation='horizontal',shrink=0.8)

plt.show()
fig.savefig("Länderübersicht.jpg")

我该如何遍历这些国家或为其着色，还是必须获取另一个shapefile？ 谢谢！

解决方法

命令ax.add_geometries()要求提供几何列表，因此，单个几何将导致错误。要修复您的代码，您可以替换以下行：

ax.add_geometries(poly,crs=ccrs.PlateCarree(),facecolor=rgba,edgecolor='none',zorder=1)

使用以下代码行：

# plot the country on a map
if poly.geom_type=='MultiPolygon':
    # `poly` is a list of geometries
    ax.add_geometries(poly,zorder=1)
elif poly.geom_type=='Polygon': 
    # `poly` is a geometry
    # Austria,Belgium
    # Plot it `green` for checking purposes
    ax.add_geometries([poly],facecolor="green",zorder=1)
else:
    pass  #do not plot the geometry

请注意，如果poly.geom_type是'Polygon'，我只用[poly]代替poly。

,

从错误代码TypeError: 'Polygon' object is not iterable中汲取灵感，我从一个假设开始，即我们需要某种可迭代的对象，例如多边形列表。从this answer进行绘制，我发现函数shapely.geometry.MultiPolygon可以完成这项工作。您只需将多边形列表传递给它即可。添加一些逻辑以仅在检测到Polygon而不是MultiPolygon并且我们有以下情况时才执行此操作：

poly = df.loc[df['ADMIN'] == country]['geometry'].values[0]
if type(poly) == shapely.geometry.polygon.Polygon:
    simple_poly = df.loc[df['ADMIN'] == country]['geometry'].values[0]
    list_polys = [poly,poly]
    poly = shapely.geometry.MultiPolygon(list_polygons)

这是一个非常棘手的解决方案，它将打印两次多边形，因此请注意，如果您以后决定使其透明或类似。或者，您可以使用[poly,poly]代替[poly,some_other_poly_outside_map_area]。