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：

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

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

例如：

Lat	Lon	Temperature	Mean Temp
51.23	4.234	23.3	Mean temperature within 100m distance
51.29	4.211	26.4	Mean temperature within 100m distance
51.25	4.238	24.3	Mean temperature within 100m distance
51.26	4.221	28.4	Mean temperature within 100m distance
51.30	4.244	19.3	Mean temperature within 100m distance
51.40	4.231	20.4	Mean temperature within 100m distance

我尝试过使用最近点：

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

但它会找到最接近的观察值及其值.. 我想找到 100m 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