空间连接

在 Python 中可视化地理空间数据

Mary van Valkenburg

Data Science Program Manager, Nashville Software School

市政区与学区

35 个市政区的图

9 个学区的图

.sjoin() 的 predicate 参数

import geopandas as gpd

gpd.sjoin(blue_region_gdf, black_point_gdf, predicate = <how to build>)

predicate 可以是 intersects、contains 和 within

使用 .sjoin()

蓝色区域与黑色点的图

predicate = 'intersects'

gpd.sjoin(blue_region_gdf, black_point_gdf, predicate = 'intersects')

浅蓝色多边形与黑色点

predicate = 'contains'

gpd.sjoin(blue_region_gdf, black_point_gdf, predicate = 'contains')

浅蓝色多边形与黑色点

predicate = 'within'

gpd.sjoin(black_point_gdf, blue_region_gdf, predicate = 'within')

浅蓝色多边形与黑色点

.sjoin() 的 predicate 参数 - within

# 在学区内查找市政区

within_gdf = gpd.sjoin(council_districts, school_districts, predicate = 'within')

print('council districts within school districts: ', within_gdf.shape[0])

council districts within school districts:  11

.sjoin() 的 predicate 参数 - contains

# 查找包含市政区的学区

contains_gdf = pd.sjoin(school_districts, council_districts, predicate = 'contains')

print('school districts contain council districts: ', contains_gdf.shape[0])

school districts contain council districts:  11

.sjoin() 的 predicate 参数 - intersects

# 查找与学区相交的市政区

intersect_gdf = gpd.sjoin(council_districts, school_districts, predicate = 'intersects')

print('council districts intersect school districts: ', intersect.shape[0])

council districts intersect school districts:  100

空间连接后的 GeoDataFrame 列

within_gdf = gpd.sjoin(council_districts, school_districts, predicate = 'within')
within_gdf.head()

  first_name_left  last_name_left district_left  index_right    
0 Nick             Leonardo            1              0              
1 DeCosta          Hastings            2              0 
2 Nancy            VanReece            8              1    
3 Bill             Pridemore           9              1    
9 Doug             Pardue             10              1

# 按学区聚合市政区 - 重命名 district_left 和 district_right
within_gdf.district_left = council_district
within_gdf.district_right = school_district
within_gdf[['council_district', 'school_district']
          ].groupby('school_district'
                   ).agg('count'
                        ).sort_values('council_district', ascending = False)

                   council_district 
school_district    
       3                   3
       1                   2
       9                   2
       2                   1
       5                   1
       6                   1
       8                   1

Passons à la pratique !

在 Python 中可视化地理空间数据