Clustering latitude, longitude along with numeric and categorical data

Question

I am working on clustering the customer base of a business-to-business company. I have data on customers that consists of both numerical (e.g. # of purchases made, avg. spend per purchase) and categorical (e.g. industry code) data.

Additionally, I have latitude and longitude information for each customer, which I would like to include in the clustering. Normal categorical and numerical data can be clustered using e.g. PAM / K-Prototypes / Hierarchical Clustering (anything where a distance matrix has to be computed, since there are distance functions that can differentiate between both types).

However, I do not know how to go about including latitude and longitude values. Latitude and longitude are in decimal degrees, therefore metrics like Euclidean distance cannot be used. Some possible approaches I have considered are:

• calculating x, y, z points on a sphere from lat / lon coordinates using

  $x = \cos(lat) \times cos(lon)$

  $y = cos(lat) \times sin(lon)$

  $z = sin(lat)$

  which could then be treated as 3 numeric attributes using Euclidean distance.

• somehow implement haversine distance in the calculation of the distance matrix. So create a distance function that calculates numeric differences using Euclidean, categorical (after one-hot encoding) using e.g. Jaccard, and lat-long dissimilarity using Haversine. How could I potentially go about implementing something like this? Is it possible, or am I overlooking something?

• creating regions, such as "EMEA" (Europe, Middle East, Africa), "APAC (Asia Pacific), "NA" (North America) from the lat-lon values, thereby creating more categorical attributes.

Can someone comment on what a suitable approach might be?

Answer 1

I don't have your specific data, because you didn't share anything, but I think this generic example should suffice.

import statsmodels.api as sm
import numpy as np
import pandas as pd

df = your_df

from numpy import unique
from numpy import where
from sklearn.datasets import make_classification
from sklearn.cluster import KMeans
from matplotlib import pyplot

# define dataset
X = df[['lat','lon']]


# define the model
model = KMeans(n_clusters=8)
# fit the model
model.fit(X)

# assign a cluster to each example
yhat = model.predict(X)

X['kmeans']=yhat

pyplot.scatter(X['lat'], X['lon'], c=X['kmeans'], cmap='some_variable', s=50, alpha=0.8)

Your specific outcome will certainly be different, but you should see something essentially like this.

The key thing to focus on is this:

X = df[['lat','lon']]

Add more variables as you desire, and check the outcome each time you make a change. Ultimately, you need to figure out what makes sense, because clustering is non-supervised, so there is no R^2 metric, or accuracy metric, are any kind of supervised metric.

You can find more detail here.

https://github.com/ASH-WICUS/Notebooks/blob/master/Clustering%20-%20Historical%20Stock%20Prices.ipynb

https://github.com/ASH-WICUS/Notebooks/blob/master/Haversine%20Distance%20-%20Airport%20or%20Not.ipynb