clustering using k-means/ k-means++, for data with geolocation

Question

I need to define spatial domains over various types of data collected in my field of study. Each collection is performed at a georeferenced point.

So I need to define the spatial domains through clustering. And generate a map with the domains defined in the georeferenced points. Grateful.

Good. I have jupyter notebook, pandas, scikit-learn, openpyxl installed.Image A. georeferenced points in the study area Image B. example of map generated by GS+ on NDVI data Image C. example of the data I have in excel

Edit: I added the code, which I managed to run

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

data = pd.read_excel('areafoliartotal.xlsx')

display(data)

pca = PCA(n_components=2)
pca_data = pca.fit_transform(data)

kmeans = KMeans(n_clusters=3)
kmeans.fit(pca_data)

plt.scatter(pca_data[:, 0], pca_data[:, 1], c=kmeans.labels_)
plt.title('PCA with KMeans Clustering')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.show()

Result

start with the basics, but like all beginners it is difficult to understand everything. Discover this method of defining spatial domains through this article EVALUATION OF CLUSTERING TECHNIQUES FOR DEFINING STATIONARY DOMAINS SUPPORTED BY GEOSTATISTICS