Clustering Geo Location Coordinates (Lat, Long Pairs) Using Kmeans Algorithm with Python
Using the Following Code to Cluster Geolocation Coordinates Results in 3 Clusters: Import Numpy as Np Import Matplotlib. Pyplot as Plt from Scipy. Cluster. Vq...
Using the following code to cluster geolocation coordinates results in 3 clusters:
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.vq import kmeans2, whiten
coordinates= np.array([
[lat, long],
[lat, long],
...
[lat, long]
])
x, y = kmeans2(whiten(coordinates), 3, iter = 20)
plt.scatter(coordinates[:,0], coordinates[:,1], c=y);
plt.show()
Is it right to use Kmeans for location clustering, as it uses Euclidean distance and not Haversine formula as a distance function?
2 Answers
k-means is not a good algorithm to use for spatial clustering, for the reasons you meantioned. Instead, you could do this clustering job using scikit-learn's DBSCAN with the haversine metric and ball-tree algorithm.
This tutorial demonstrates clustering latitude-longitude spatial data with DBSCAN/haversine and avoids all those Euclidean-distance problems:
df = pd.read_csv('gps.csv')
coords = df.as_matrix(columns=['lat', 'lon'])
db = DBSCAN(eps=eps, min_samples=ms, algorithm='ball_tree', metric='haversine').fit(np.radians(coords))
Note that this specifically uses scikit-learn v0.15, as some earlier/later versions seem to require a full distance matrix to be computed. Also notice that the eps value is in radians and that .fit() takes the coordinates in radian units for the haversine metric.
It highly depends on your application:
- Around the equator the results should be fairly accurate. Close to one of the poles the results won't be useful at all.
- It might, however, work as a pre-pocessing step or for applications with low precision requirements, e.g. small, non-overlapping and very distinct clusters.
If you really need the Haversine formula, you might want to look into this discussion. As Anony-Mousse says:
Note that Haversine distance is not appropriate for k-means or average-linkage clustering, unless you find a smart way of computing the mean that minimizes variance. Do not use the arithmetic average if you have the -180/+180 wrap-around of latitude-longitude coordinates.
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