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pointsGeoJSON 文件中点的热图，但不显示地理密度（纬度、经度）。文件中points每个点都有一个confidence属性（0到1之间的值），如何根据该参数显示热图？weight=points.confidence似乎不起作用。

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举个例子：

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

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下图显示了我的结果，但它是基于地理密度而不是置信度得分密度。

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import geoplot as gplt\nimport geopandas as gpd\nimport geoplot.crs as gcrs\nimport matplotlib.pyplot as plt\n\npoints = gpd.read_file(\'points.geojson\')\npolygons = gpd.read_file(\'polygons.geojson\')\n\nax = gplt.polyplot(polygons, projection=gcrs.AlbersEqualArea(), zorder=1)\ngplt.kdeplot(points, cmap=\'Reds\', shade=True, clip=polygons, ax=ax) \n#weight=points.confidence don\xe2\x80\x99t work inside kdeplot()\n\nplt.show()\n

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Answer 1

• 使用您的样本数据作为点
• 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-culture-vectors/下载此内容
• 多边形数据是一个形状文件
  1. 加载到geopandas中以允许与GEOJSON接口 __geo__interface
  2. 使用pandas动态过滤到沙特 .loc
• 置信度数据只是一个直接的https://plotly.com/python/mapbox-densis-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