View of the sea crashing on the Kerala shore

Keralite Klusters

One of the problems we worked on in Kerala wasn’t a drone problem at all, but an infrastructure problem. Given a distribution of locales, how can we cluster those according to distance to help reduced duplicated infrastructure services (internet, electricity, etc.)? There are lots of ways to solve this, but we chose to do it in PostGIS because… Ok: because that’s what I always often do whether I should or not.

I worked on this with Deepthi Patric, the Geomatics expert for the group. For the record, the points below are not the points we actually analyzed, but a randomized distribution within occupied areas of Kerala.

Points across Kerala for clustering
Points across Kerala for clustering
View over Trivandrum, Kerala
View over Trivandrum, Kerala

In this case, PostGIS was a good choice of tool, at least once we started using it intelligently. At first we tried something less intelligent and more blunt wherein we buffered and created convex hulls with left joins, etc.:

This was…  well. Not the best method. So, back to the drawing board. Let’s use something a little smarter. Since PostGIS 2.2, we’ve had ST_ClusterWithin. This promisingly named function is just what we need… . It does return a geometry collection, so we need to manipulate the results a bit, but the query is pretty reasonable in the end:

Thus changing individual points:

Inset of unclustered points.
Inset of unclustered points.

to smart clusters based on proximity:

clusters_up_close_clustered
Inset of points clustered into points within 1km of each other

Not too bad, and pretty quick to run as well.

Woman pointing at man on mountain top with another man looking on

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