如何使用geopandas找到坐标的最近值

Question

我有一个坐标列表，每个坐标都有温度。数据框如下所示：例如：

纬度	经度	温度
51.23	4.234	23.3
51.29	4.211	26.4
51.25	4.238	24.3
51.26	4.221	28.4
51.30	4.244	19.3
51.40	4.231	20.4

在 geopandas 中有没有办法直接找到每行 100 米距离内的观测值，并使用最近观测值的平均值创建一个新列

例如：

纬度	经度	温度	平均温度
51.23	4.234	23.3	100m距离内的平均温度
51.29	4.211	26.4	100m距离内的平均温度
51.25	4.238	24.3	100m距离内的平均温度
51.26	4.221	28.4	100m距离内的平均温度
51.30	4.244	19.3	100m距离内的平均温度
51.40	4.231	20.4	100m距离内的平均温度

我试过使用 nearest_point:

def get_nearest_values(row, other_gdf, point_column='geometry', 
value_column="predictions_precipitation_type"):


    # Create an union of the other GeoDataFrame's geometries:
    other_points = other_gdf["geometry"].unary_union

    # Find the nearest points
    nearest_geoms = nearest_points(row[point_column], other_points)

    # Get corresponding values from the other df
    nearest_data = other_gdf.loc[other_gdf["geometry"] == 
    nearest_geoms[1]]

    nearest_value = nearest_data[value_column].values[0]

return nearest_value

但它找到了最近的观测值及其值.. 我想找到 100 米半径内的所有观测值，然后找到平均值

Answer 1

试试这个：

import geopandas as gpd
from shapely.geometry import Point

s = """Lat  Lon Temperature
51.23   4.234   23.3
51.29   4.211   26.4
51.25   4.238   24.3
51.26   4.221   28.4
51.30   4.244   19.3
51.40   4.231   20.4"""

n = 3 # Columns
data =  [s.split()[i:i + n] for i in range(0, len(s.split()), n)]
df = gpd.pd.DataFrame(data[1:], columns=data[0])

for col in df.columns:
    df[col] = gpd.pd.to_numeric(df[col])

geometry = [Point(xy) for xy in zip(df.Lon, df.Lat)]

gdf = gpd.GeoDataFrame(df, geometry=geometry)

for index, row in gdf.iterrows():
    buffer = row.geometry.buffer(0.1)
    points_inside_buffer = gdf[gdf.geometry.within(buffer)]
    points_temperatures = points_inside_buffer['Temperature'].tolist()
    mean_temp = sum(points_temperatures)/len(points_temperatures)
    gdf.at[index, "Mean Temp within 100m"] = mean_temp

如何使用geopandas找到坐标的最近值

How to use geopandas to find the nearest value of a coordinate

python

geopandas