如何使用 geopandas 显示特定参数的热图？

Question

在我非常简单的情况下，我想显示 points GeoJSON 文件中的点的热图，但不是地理密度（纬度，经度） .在points文件中每个点都有一个confidence属性（0到1的值），如何在这个参数上显示heatmap？ weight=points.confidence 好像不行。

例如：

#points.geojson
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": {"confidence": 0.67}, "geometry": { "type": "Point", "coordinates": [ 37.703471404215918, 26.541625492300192 ] } },
{ "type": "Feature", "properties": {"confidence": 0.76}, "geometry": { "type": "Point", "coordinates": [ 37.009744331225093, 26.710090585532761 ] } },
{ "type": "Feature", "properties": {"confidence": 0.94}, "geometry": { "type": "Point", "coordinates": [ 37.541708538306224, 26.160111944646022 ] } },
{ "type": "Feature", "properties": {"confidence": 0.52}, "geometry": { "type": "Point", "coordinates": [ 37.628566642215354, 25.917300595223857 ] } },
{ "type": "Feature", "properties": {"confidence": 0.46}, "geometry": { "type": "Point", "coordinates": [ 37.676499267124271, 26.653959791866598 ] } },
{ "type": "Feature", "properties": {"confidence": 0.55}, "geometry": { "type": "Point", "coordinates": [ 37.677033863264533, 26.654033815175087 ] } },
{ "type": "Feature", "properties": {"confidence": 0.12}, "geometry": { "type": "Point", "coordinates": [ 37.37522057234797, 26.353271000367258 ] } },
{ "type": "Feature", "properties": {"confidence": 0.62}, "geometry": { "type": "Point", "coordinates": [ 37.396556958266373, 26.459196264023291 ] } },
{ "type": "Feature", "properties": {"confidence": 0.21}, "geometry": { "type": "Point", "coordinates": [ 36.879775221618168, 26.901743663072878 ] } }
]
}

下图显示了我的结果，但它基于地理密度而不是置信度得分密度。

import geoplot as gplt
import geopandas as gpd
import geoplot.crs as gcrs
import matplotlib.pyplot as plt

points = gpd.read_file('points.geojson')
polygons = gpd.read_file('polygons.geojson')

ax = gplt.polyplot(polygons, projection=gcrs.AlbersEqualArea(), zorder=1)
gplt.kdeplot(points, cmap='Reds', shade=True, clip=polygons, ax=ax) 
#weight=points.confidence don’t work inside kdeplot()

plt.show()

Answer 1

使用您的示例数据获取积分
这些点位于沙特阿拉伯，因此假设多边形是沙特阿拉伯的区域边界。从 http://www.naturalearthdata.com/downloads/10m-cultural-vectors/
多边形数据是一个形状文件
1. 加载到 geopandas 以允许接口到 GEOJSON __geo__interface
2. 使用 pandas .loc
置信数据刚好是直线https://plotly.com/python/mapbox-density-heatmaps/
边界是https://plotly.com/python/mapbox-layers/

# fmt: off
points = {
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": {"confidence": 0.67}, "geometry": { "type": "Point", "coordinates": [ 37.703471404215918, 26.541625492300192 ] } },
{ "type": "Feature", "properties": {"confidence": 0.76}, "geometry": { "type": "Point", "coordinates": [ 37.009744331225093, 26.710090585532761 ] } },
{ "type": "Feature", "properties": {"confidence": 0.94}, "geometry": { "type": "Point", "coordinates": [ 37.541708538306224, 26.160111944646022 ] } },
{ "type": "Feature", "properties": {"confidence": 0.52}, "geometry": { "type": "Point", "coordinates": [ 37.628566642215354, 25.917300595223857 ] } },
{ "type": "Feature", "properties": {"confidence": 0.46}, "geometry": { "type": "Point", "coordinates": [ 37.676499267124271, 26.653959791866598 ] } },
{ "type": "Feature", "properties": {"confidence": 0.55}, "geometry": { "type": "Point", "coordinates": [ 37.677033863264533, 26.654033815175087 ] } },
{ "type": "Feature", "properties": {"confidence": 0.12}, "geometry": { "type": "Point", "coordinates": [ 37.37522057234797, 26.353271000367258 ] } },
{ "type": "Feature", "properties": {"confidence": 0.62}, "geometry": { "type": "Point", "coordinates": [ 37.396556958266373, 26.459196264023291 ] } },
{ "type": "Feature", "properties": {"confidence": 0.21}, "geometry": { "type": "Point", "coordinates": [ 36.879775221618168, 26.901743663072878 ] } }
]
}
# fmt: on
import geopandas as gpd
import plotly.express as px
import requests
from pathlib import Path
from zipfile import ZipFile
import urllib

# fmt: off
# download boundaries
url = "https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_1_states_provinces.zip"
f = Path.cwd().joinpath(urllib.parse.urlparse(url).path.split("/")[-1])
# fmt: on

if not f.exists():
    r = requests.get(url, stream=True, headers={"User-Agent": "XY"})
    with open(f, "wb") as fd:
        for chunk in r.iter_content(chunk_size=128):
            fd.write(chunk)
    zfile = ZipFile(f)
    zfile.extractall(f.stem)

# load downloaded boundaries
gdf2 = gpd.read_file(str(f.parent.joinpath(f.stem).joinpath(f"{f.stem}.shp")))

# confidence data
gdf = gpd.GeoDataFrame.from_features(points)

# now the simple bit, densitity plot data and Saudi Arabia regional boundaries as a layer
fig = px.density_mapbox(
    gdf, lat=gdf.geometry.y, lon=gdf.geometry.x, z="confidence"
).update_layout(
    mapbox={
        "style": "carto-positron",
        "zoom": 6,
        "layers": [
            {
                "source": gdf2.loc[gdf2["iso_a2"].eq("SA")].geometry.__geo_interface__,
                "type": "line",
            }
        ],
    },
    margin={"l":0,"r":0,"t":0,"b":0}
)

fig

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