python - 连接两个数据集，都带有点

Question

我有两个带有点的 csv 文件。一个学校数据集（lat、lon 和学校名称）和一个带有房屋坐标（lat、lon 和 houseid）的数据集。

我想列出距离学校 500 米范围内的所有房屋。

我真的不知道如何在 Python 中使用 geopandas 进行空间连接。有人可以帮帮我吗？

schools.csv
56.039484;14.164114;Parkskolan
56.029687;14.159337;Centralskolan


houses.csv
56.039240;14.165066;1
56.039008;14.166709;2
56.038608;14.169420;3

标签： pythongeopandas

Answer 1

获得解决方案的主要步骤：

1. 将 2 个数据文件读入数据帧
2. 为两个数据帧设置 CRS('epsg:4326') 并从 (lat,long) 创建点几何
3. 对于schools数据帧，将 CRS 转换为 UTMzone 33N
4. schools在数据帧上做缓冲（半径=500m）
5. 在数据帧上schools，执行并将 500m 缓冲区设置为新的geometry
6. 在公共 CRS之间houses进行适当的空间连接schools
7. houses_joined在数据框中获取结果

这是工作代码：

import pandas as pd
import geopandas as gpd
from shapely.geometry import Point, Polygon

# School data
# -----------
# read `schools.csv`, data are in (lat,long); 'epsg:4326'
#
# lat;lon;school_name
# 56.039484;14.164114;Parkskolan
# 56.029687;14.159337;Centralskolan
df_schools = pd.read_csv('schools.csv', na_values=['NaN'], sep=';')

# create Point geometry objects from (lon,lat)
sch_geom = [Point(xy) for xy in zip(df_schools.lon, df_schools.lat)]
# set initial coordinate ref system, and geometry column to the dataframe
gdf_schools = gpd.GeoDataFrame(df_schools, crs={'init': 'epsg:4326'}, geometry=sch_geom)

# convert CRS from (lat,long) to UTMzone 33N
# and get new dataframe: gdf_schools_utm33N
gdf_schools_utm33N = gdf_schools.to_crs(crs="+proj=utm +zone=33 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
# Note: crs="..." can be replaced by epsg=32633

# do buffering, radius: 500m
gdf_schools_utm33N['buffer_geometry'] = gdf_schools_utm33N.geometry.buffer(500)

# rename `geometry` -> `original_geometry`; `buffer_geometry` -> geometry
# .. and set column `geometry` as the default geometry data of the geodataframe.
gdf_schools_utm33N = gdf_schools_utm33N.rename(
    columns={'geometry':'original_geometry', 'buffer_geometry':'geometry'}).set_geometry('geometry')

# Houses data
# -----------
# read `houses.csv`, data are in (lat,long); 'epsg:4326'

# lat;lon;houseid
# 56.039240;14.165066;1
# 56.039008;14.166709;2
# 56.038608;14.169420;3
# 56.046108;14.171420;4

df_houses = pd.read_csv('houses.csv', na_values=['NaN'], sep=';')
# I add the 4th house that is too far away from all schools
# The 4th house: 56.046108  14.171420   4

# create Point geometry for the houses, and init CRS
hs_geom = [Point(xy) for xy in zip(df_houses.lon, df_houses.lat)]
gdf_houses = gpd.GeoDataFrame(df_houses, crs={'init': 'epsg:4326'}, geometry=hs_geom)

# options: plot the schools' buffers and all the houses
ax = gdf_schools_utm33N.plot(color='lightgray', edgecolor='green', alpha=0.5)
gdf_houses.to_crs(epsg=32633).plot(ax=ax, color='red')

# ******* Spatial Join *****************
# houses data frame needs CRS conversion
hss = gdf_houses.to_crs(epsg=32633)
# do spatial join of houses(points) ~ schools(circles of 500m radius)
houses_joined = gpd.sjoin(hss, gdf_schools_utm33N, op='within', how='inner')
# print out the successful joined rows (house_id, school_names)

# this prints house_id + school_name 
houses_joined[['houseid','school_name']]

# Output: house_id, school_name
# 1    Parkskolan
# 2    Parkskolan
# 3    Parkskolan

结果图：