Shapely 几何与空间关系

在 Python 中处理地理空间数据

Dani Arribas-Bel

Geographic Data Science Lab (University of Liverpool)

标量几何值

cities = geopandas.read_file("ne_110m_populated_places.shp")
cities.head()

           name                                      geometry
0  Vatican City   POINT (12.45338654497177 41.90328217996012)
1    San Marino     POINT (12.44177015780014 43.936095834768)
2         Vaduz   POINT (9.516669472907267 47.13372377429357)
3       Lobamba  POINT (31.19999710971274 -26.46666746135247)
4    Luxembourg   POINT (6.130002806227083 49.61166037912108)

brussels = cities.loc[170, 'geometry']
print(brussels)

POINT (4.33137074969045 50.83526293533032)

标量几何值

brussels = cities.loc[170, 'geometry']
print(brussels)

POINT (4.33137074969045 50.83526293533032)

type(brussels)

shapely.geometry.point.Point

Shapely 包

type(brussels)

shapely.geometry.point.Point

Shapely

用于几何对象操作与分析的 Python 包
提供 Point、LineString、Polygon 对象
GeoSeries（GeoDataFrame 的 geometry 列）由 Shapely 对象组成

几何对象

从 GeoDataFrame 获取：

brussels = cities.loc[170, 'geometry']
paris = cities.loc[235, 'geometry']
belgium = countries.loc[countries['name'] == 'Belgium', 'geometry'].squeeze()
france = countries.loc[countries['name'] == 'France', 'geometry'].squeeze()
uk = countries.loc[countries['name'] == 'United Kingdom', 'geometry'].squeeze()

手动创建：

from shapely.geometry import Point
p = Point(1, 2)
print(p)

POINT (1 2)

空间方法

几何的面积：

belgium.area

3.8299974609075753

两几何间的距离：

brussels.distance(paris)

2.8049127723186214

还有更多！（如 centroid、simplify 等）

空间关系

geopandas.GeoSeries([belgium, france, uk, paris,  brussels, line]).plot()

空间关系

belgium.contains(brussels)

True

france.contains(brussels)

False

brussels.within(belgium)

True

belgium.touches(france)

True

line.intersects(france)

True

line.intersects(uk)

False

Vamos praticar!

在 Python 中处理地理空间数据