Latest Contributions by JamalNUMAN

I have tried it with and without setting the “population” a s a weight, the result is as shown in the screenshots below   ... View more

Eagerly waiting to see the enhancement in  Pro 3.2! Thank you ... View more

There is no code that calculates the weights for the neighboring observations for each focal observation  ... View more

https://developers.arcgis.com/geoanalytics/tools/gwr/ https://github.com/geeksnome/machine-learning-made-easy/blob/master/locally_weighted_regression.py     ... View more

when is the Pro 3.2 expected to release? Can you provide some screenshots how will the tree look like?  ... View more

What I wanted here is to have the marked classes appeared in the legend   ... View more

I have little knowledge in coding. All what I wanted is a python code for the Geographically Weighted Regression (GWR) that can work in Jupyter or Spyder       ... View more

I used the ChatGPT in order to get the python code for the GWR as per the request below --- Write a python code to read the table saved here: "C:\D\H3.xlsx" Write a python code to predict the value of the dependent variable based on the geographically Weighted Regression (GWR) displaying how the weights of neighbouring observations are used to calculate the prediction of the focal observation The first 4 columns are the independent variables The fifth column is the dependent variable The u column represents the x-coordinate The v column represents the y coordinate Use the Gaussian as the kernel function Assume the bandwidth to be 400 meters -------   The ChatGPT provided the code below:   -------- import pandas as pd import numpy as np from spreg import GWR from spreg.utils import read_spatialdata # Load the data from the Excel file file_path = r'C:\D\H3.xlsx' data = pd.read_excel(file_path) # Define the independent variables (X1, X2, X3, X4) X = data[['X1', 'X2', 'X3', 'X4']].values # Define the dependent variable (y) y = data['y'].values # Define the coordinates (u and v) u = data['u'].values v = data['v'].values # Define the bandwidth for the Gaussian kernel bandwidth = 400  # meters # Create a spatial weights matrix based on Gaussian kernel distances = np.sqrt((u[:, np.newaxis] - u)**2 + (v[:, np.newaxis] - v)**2) kernel_weights = np.exp(-distances**2 / (2 * bandwidth**2)) # Create a GWR model model = GWR(y, X, kernel_weights) # Fit the GWR model results = model.fit() # Predict the values of the dependent variable predicted_values = results.predict(X) # You can now use predicted_values for your predictions print(predicted_values)   ------- However, running this code on the JupyterLab ends up with an error as below         ... View more

Not sure how can I can take the advantage of this python code to stick it in JupyterLab in order to apply the analysis ... View more

I thought that identifying a feature on the layer that represents the parcels should be able to display the tree of parcel lineage history. ... View more