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I have data about houses for sale, that I present over a map.

Each house has coordinates ([lat,lng]) and other features. The data is only for one country, so no need to address the 180deg world wrap.

I want to cluster neighbourhoods, out of those houses, and base the the neighbourhood borders on prices (sqm price).

It's really easy to see those clusters when I paint the houses with gradient color - from cheap to expensive, but I can't find a trivial way to do it mathematically.

Basically, what I want is price clustering, but with a limitation on the clustering algorithm so there won't be any geo-location overlap.

Another way to look at it - I want to blur the data points and create low-resolution areas.

Not sure where to go with this, any help will be appreciated.

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There are many such approaches, for example spatial autocorrelation, Lisa, etc.

In the clustering domain, GDBSCAN is a generalization of DBSCAN where you could easily define neighbors as points being within a certain distance and having a similar price.

Nevertheless it is probably a good idea to look into actual geostatistics and beyond the limitations of sklearn.

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  • $\begingroup$ Thanks. I will try it out. can GDBSCAN guarantee that there won't be a geo overlap between clusters or just limit the geo distance? I'm not bound at all to sklearn, can you recommend any geostatistics tools? $\endgroup$ – yonatanmn Jul 23 '19 at 7:38
  • $\begingroup$ Depending how you set things up with your definitions, they may overlap. $\endgroup$ – Has QUIT--Anony-Mousse Jul 23 '19 at 20:18

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