Hierarchical clustering is a valuable technique for analyzing geospatial data that includes latitude and longitude variables.
Since our data has these, I calculated the distance between them using distance metric, such as Euclidean to measure the dissimilarity between locations. Using this metric, calculated a pairwise distance matrix representing the differences between all pairs of locations. Then applied an agglomerative hierarchical clustering algorithm, coupled with a linkage method of choice to the distance matrix. The outcome was a dendrogram, visually displaying the hierarchical structure of clusters.
interpreted and analysed the results of spatial patterns within the identified clusters and investigated the geospatial implications of the clustering.