如何计算形状相交的多边形数量？

Question

我有一个非常大的数据集，其中包含多边形和点，周围有缓冲区。我想在点数据中创建一个新列，其中包括点的缓冲区相交的多边形数。

这是一个简化的例子：

import pandas as pd
import geopandas as gp
from shapely.geometry import Polygon
from shapely.geometry import Point
import matplotlib.pyplot as plt

## Create polygons and points ##

df = gp.GeoDataFrame([['a',Polygon([(1, 0), (1, 1), (2,2), (1,2)])],
                     ['b',Polygon([(1, 0.25), (2,1.25), (3,0.25)])]],
                     columns = ['name','geometry'])
df = gp.GeoDataFrame(df, geometry = 'geometry')

points = gp.GeoDataFrame( [['box', Point(1.5, 1.115), 4],
                        ['triangle', Point(2.5,1.25), 8]],
                     columns=['name', 'geometry', 'value'], 
                     geometry='geometry')

##Set a buffer around the points##

buf = points.buffer(0.5)
points['buffer'] = buf
points = points.drop(['geometry'], axis = 1) 
points = points.rename(columns = {'buffer': 'geometry'})

此数据如下所示： 我想做的是在点数据框中创建另一列，其中包括点相交的多边形数量。

我尝试过使用 for 循环：

points['intersect'] = []
for geo1 in points['geometry']:
    for geo2 in df['geometry']:
        if geo1.intersects(geo2):
            points['intersect'].append('1')

然后我将求和以获得相交的总数。 但是，我收到错误消息：“值的长度与索引的长度不匹配”。我知道这是因为它试图将三行数据分配给只有两行的框架。

如何聚合计数，以便为第一个点分配值 2，为第二个点分配值 1？

Answer 1

如果您有大型数据集，我会使用 rtree 空间索引来解决问题，就像这样。

import pandas as pd
import geopandas as gp
from shapely.geometry import Polygon
from shapely.geometry import Point
import matplotlib.pyplot as plt

## Create polygons and points ##

df = gp.GeoDataFrame([['a',Polygon([(1, 0), (1, 1), (2,2), (1,2)])],
                     ['b',Polygon([(1, 0.25), (2,1.25), (3,0.25)])]],
                     columns = ['name','geometry'])
df = gp.GeoDataFrame(df, geometry = 'geometry')

points = gp.GeoDataFrame( [['box', Point(1.5, 1.115), 4],
                        ['triangle', Point(2.5,1.25), 8]],
                     columns=['name', 'geometry', 'value'], 
                     geometry='geometry')

# generate spatial index
sindex = df.sindex
# define empty list for results
results_list = []
# iterate over the points
for index, row in points.iterrows():
    buffer = row['geometry'].buffer(0.5)  # buffer
    # find approximate matches with r-tree, then precise matches from those approximate ones
    possible_matches_index = list(sindex.intersection(buffer.bounds))
    possible_matches = df.iloc[possible_matches_index]
    precise_matches = possible_matches[possible_matches.intersects(buffer)]
    results_list.append(len(precise_matches))
# add list of results as a new column
points['polygons'] = pd.Series(results_list)