我终于要寻求帮助了。在尝试了所有的选择之后，我被卡住了。
现在我有数据是从一个至少有105个村庄的地区的10个村庄抽样的。有了这10个村庄的样本数据，我做了预测，效果也很好，我的最终预测值表看起来像这样（不幸的是，我无法将此表转换为可以在这里分享的东西）：

现在我的问题是插值。我想插入这些数据，以覆盖其他未采样的村庄，这是我如何做到的：

from scipy.interpolate import griddata

# Extract the longitude, latitude, and prediction columns from the decoded dataframe
interpolation_data = decoded_df[['longitude', 'latitude', 'prediction']]

# Remove any rows with missing values
interpolation_data = interpolation_data.dropna()

# Convert the data to numpy arrays
points = interpolation_data[['longitude', 'latitude']].values
values = interpolation_data['prediction'].values

# Define the grid points for interpolation
grid_points = np.vstack((grid_lon.flatten(), grid_lat.flatten())).T

# Perform IDW interpolation
interpolated_values = griddata(points, values, grid_points, method='linear')

interpolated_values = interpolated_values.reshape(grid_lon.shape)

# Create a contour plot of the interpolated predictions
plt.contourf(grid_lon, grid_lat, interpolated_values)
plt.colorbar()
plt.scatter(decoded_df['longitude'], decoded_df['latitude'], c=decoded_df['prediction'], cmap='viridis', edgecolors='black')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Interpolated Predictions')
plt.show()

这给了我这个

现在，下一步是将插值结果叠加到该区域的Map上。我是这样做的：

import geopandas as gpd
from mpl_toolkits.axes_grid1 import make_axes_locatable

import geopandas as gpd
import matplotlib.pyplot as plt

# Read shapefile data of Babati
shapefile_path = "Babati Villages/Babati_villages.shp"  # Replace with the actual path to the shapefile
gdf_babati = gpd.read_file(shapefile_path)

gdf_bti= gdf_babati[gdf_babati["District_N"] == "Babati"]
gdf_bti.head()

# Define the grid points for interpolation
grid_points = np.vstack((grid_lon.flatten(), grid_lat.flatten())).T

# Perform IDW interpolation
interpolated_values = griddata(points, values, grid_points, method='linear')

# Reshape the interpolated values to match the grid shape
interpolated_values = interpolated_values.reshape(grid_lon.shape)

from shapely.geometry import box
# Create a bounding box geometry of the Babati region
bbox = box(gdf_bti.total_bounds[0], gdf_bti.total_bounds[1],
           gdf_bti.total_bounds[2], gdf_bti.total_bounds[3])

# Clip the interpolated predictions to the extent of the Babati region
interpolated_predictions = gpd.clip(interpolated_predictions, bbox)

# Create subplots
fig, ax = plt.subplots(figsize=(10, 10))

# Plot the shapefile of the Babati region
gdf_bti.plot(ax=ax, facecolor='none', edgecolor='black')

# Plot the interpolated predictions
interpolated_predictions.plot(ax=ax, column='prediction', cmap='viridis', markersize=30, legend=True)

# Add colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
interpolated_predictions.plot(ax=cax, column='prediction', cmap='viridis', legend=True, cax=cax)

# Set plot title and labels
ax.set_title('Interpolated Predictions in Babati Region')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')

# Show the plot
plt.show()

这里是现在的问题是，因为插值的覆盖是完全关闭的。我希望它能覆盖所有插入的村庄，但它没有。这就是我得到的

我做错了什么，有什么办法解决这个问题吗？