Python Geographically Weighted Regression (GWR) with libpysal

Apologies for the confusion. It appears that the 'spreg' module in 'pysal' does not have a class named 'GWR' in recent versions.

To perform Geographically Weighted Regression (GWR) in Python, you can use the 'libpysal' library, which is a lower-level library for spatial analysis and implements GWR. Here's an updated example using 'libpysal':

First, make sure you have 'libpysal' installed. You can install it using 'pip install libpysal'.

import numpy as np
import libpysal as ps

# Prepare your data
# X: independent variables (features)
# y: dependent variable (target)
# coords: spatial coordinates (latitude and longitude)

# Create a GWR model
gwr_model = ps.model.GWR(coords, y, X)

# Fit the GWR model
gwr_results = gwr_model.fit()

# Print the summary of GWR results
print(gwr_results.summary())

In this updated example, we import 'libpysal' as 'ps'. Then, we create a GWR model using 'ps.model.GWR()', passing the 'coords', 'y', and 'X' to the function.

Next, we fit the GWR model using the 'fit()' method.

Finally, we print the summary of the GWR results using the 'summary()' method.

Please note that the 'libpysal' library provides various options for configuring GWR models, such as specifying bandwidths, kernel functions, and diagnostics. You can refer to the 'libpysal' documentation for more advanced usage and customization options.