Grouping lat-long locations geo-spatially into N clusters

cluster1.py

# Grouping lat-long locations geo-spatially into N clusters

from sklearn.cluster import KMeans
import pandas as pd

def clusterPoints(df1, N=10, cluster_column='cluster', lat_column='lat', lon_column='lon'):
    kmeans = KMeans(n_clusters=N, random_state=None, n_init='auto')
    df1[cluster_column] = kmeans.fit_predict(df1[[lat_column, lon_column]].values) + 1
    # +1 so that 0,1,2,3..N-1 becomes 1,2,3..N
    return
  

df1 = pd.read_csv('locations.csv')

# split into default 10 clusters
clusterPoints(df1)
print(df1)

# split into 4 clusters
clusterPoints(df1, N=4)
print(df1)

# specify a different cluster cloumn
clusterPoints(df1, cluster_column='group')
print(df1)

# lat-long column names are different
clusterPoints(df1, lat_column='Y', lon_column='X')
print(df1)

z_README.md

Clustering (or grouping) lat-long locations geo-spatially using K-means

Quick utility function in python to geo-spatially cluster a pandas dataframe table carrying latitude-longitude into N clusters

This will take a df and add a cluster column to it (you can change defaults by adding arguments to your function), carrying cluster number 1 to N

There are more customizations to kmeans that I haven't gone into, please explore them at https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans

required python packages installations:

pip install pandas scikit-learn