查找一个地理数据框的特征，其中至少包含另一个地理数据框的一个特征

Question

我有两个多边形层。一个是宗地图层（名称为“parcl”），另一个是多边形图层（名称为“房屋”）。我正在尝试从房屋数据框中找到包含至少一项功能的所有地块。我试图通过如下所示的循环来做到这一点：

same = np.empty((0,parcl.shape[1]))

for i in parcl.index:
  for j in houses.index:
    a = parcl.loc[i]
    b = houses.loc[j]
    if (a.contains(b))==True:
      same.append(a)

很遗憾，我收到以下错误消息：

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-36-cedb059e5a3b> in <module>()
      5     a = parcl.loc[i]
      6     b = houses.loc[j]
----> 7     if (a.contains(b))==True:
      8       same.append(a)

/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in __getattr__(self, name)
   5139             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   5140                 return self[name]
-> 5141             return object.__getattribute__(self, name)
   5142 
   5143     def __setattr__(self, name: str, value) -> None:

AttributeError: 'Series' object has no attribute 'contains'

任何帮助将不胜感激！ :)

Answer 1

这是sjoin()的简单案例

• 模拟了一些房屋（矩形）
• 将一些英国县定义用作地块
• gdf_contains 是 sjoin() 的结果，现在是包含至少一所房子的地块
• 可视化以证明它有效（绿色包裹）

import geopandas as gpd
import shapely.geometry
import numpy as np
import requests


world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")).loc[lambda d: d["iso_a3"].eq("GBR")]
BOXES = 25
a, b, c, d = world.total_bounds

# manufactuer some houses
gdf_houses = gpd.GeoDataFrame(
    geometry=[
        shapely.geometry.box(minx, miny, maxx, maxy)
        for minx, maxx in zip(np.linspace(a, c, BOXES), np.linspace(a, c, BOXES)[1:])
        for miny, maxy in zip(np.linspace(b, d, BOXES), np.linspace(b, d, BOXES)[1:])
    ],
    crs="epsg:4326",
).sample(35)

gdf_houses.explore()
res = requests.get(
    "https://opendata.arcgis.com/datasets/37363d379f4f40fa8c3f0d28eedfdd37_0.geojson"
)
gdf_parcl = gpd.GeoDataFrame.from_features(res.json(), crs="epsg:4326")

gdf_contains = gdf_parcl.sjoin(gdf_houses)

m = gdf_parcl.explore(color="grey")
m = gdf_contains.explore(m=m, color="green")
m = gdf_houses.explore(m=m, style_kwds=dict(fillOpacity=.9))

m