使用空间连接计算距离 - geopandas

Question

导入请求，io，json 将 geopandas 导入为 gpd 导入 shapely.geometry 将 pandas 导入为 pd

我有两个 GeoPandas DataFrame。

gpd1 具有 POLYGON 几何 gpd2 具有 POINT 几何形状

示例数据：

res = requests.get(
    "https://opendata.arcgis.com/datasets/69dc11c7386943b4ad8893c45648b1e1_0.geojson"
)
gpd1 = gpd.GeoDataFrame.from_features(res.json()["features"], crs="CRS84").pipe(
    lambda d: d.rename(columns={c: c.lower() for c in d.columns})
).rename(columns={"lad20cd": "areaCode","lad20nm":"areaName"})


# get some public addressess - hospitals.  data that can be scattered
dfhos = pd.read_csv(io.StringIO(requests.get("http://media.nhschoices.nhs.uk/data/foi/Hospital.csv").text),
    sep="¬",engine="python",)


# create a geo dataframe of hospitals
gpd2 = gpd.GeoDataFrame(
    data=dfhos,
        geometry=dfhos.apply(lambda r: shapely.geometry.Point(r["Longitude"],r["Latitude"]), axis=1), crs="EPSG:4326"
    )

我空间加入他们：

gpd1.sjoin(gpd2, how='left')

我还想计算连接的每一行的距离。如何计算距离并将 distance 作为列包含在内？请注意，gpd2 中的每个 POINT 都在 POLYGON.

中

Answer 1

与您的问题一样，模棱两可或您的示例代码无法正常工作
有固定的示例代码来使用英国县和英国城镇/城市
sjoin()的结果是一列right_index这可以用来构建一系列的点
那么这是一个使用geopandasdistance()获取距离

import pandas as pd
import geopandas as gpd
import requests, io


# some polygons
gpd1 = gpd.read_file(
    requests.get("https://www.geoboundaries.org/api/current/gbOpen/GBR/ADM2").json()[
        "simplifiedGeometryGeoJSON"
    ]
)

# some points
df = pd.read_csv(
    io.StringIO(
        requests.get("https://simplemaps.com/static/data/country-cities/gb/gb.csv").text
    )
)


gpd2 = gpd.GeoDataFrame(
    df, geometry=gpd.points_from_xy(df["lng"], df["lat"]), crs="epsg:4326"
)


gpd_sj = gpd1.sjoin(gpd2, how="inner").reset_index()
gpd_sj["distance"] = gpd_sj.distance(gpd2.loc[gpd_sj["index_right"]])
gpd_sj.loc[:, ["index", "shapeName", "index_right", "city", "distance"]]

	index	shapeName	index_right	city	distance
0	0	Hartlepool	1606	Seaton Carew	3.29536
1	0	Hartlepool	96	Hartlepool	2.22489
2	1	Middlesbrough	2466	Stainton	1.41667
3	1	Middlesbrough	1849	Nunthorpe	0.766652
4	1	Middlesbrough	1129	Marton	0.581443
5	1	Middlesbrough	36	Middlesbrough	1.8294
6	2	Redcar and Cleveland	1628	Ormesby	3.30945
7	2	Redcar and Cleveland	635	Guisborough	2.11976
8	2	Redcar and Cleveland	1330	Loftus	3.68467
9	2	Redcar and Cleveland	1167	Skelton	3.60919

使用空间连接计算距离 - geopandas

Calculate distances with spatial join - geopandas

python

gis

geospatial

geopandas