Identifying spatial clusters in Python with consideration to additional attributes

Question

I'm working with a large set comprising of spatial parcels, with each row containing geographic coordinates (UTM), parcel area & value:

[x, y, area, value]:
[272564.9434265977, 6134243.108910706, 980.63, 550.6664083293393],
[272553.9611341293, 6134209.499155387, 1026.55, 477.32696897374706],
[271292.4197118982, 6132982.047648986, 634.438, 851.1469993915875],
...

Plotting these visually identifies several distinct zones where dollar value varies based on geography (the high value strip on the left is coastal, for example):

I would like to identify clusters of value (ie the coastal strip) & have looked at several approaches;

• K-means seems the easiest clustering method to implement, but appears unsuitable due to only considering distance between points and no further attributes.

• ClusterPy looks ideal for this application but their documentation only seems to cover working with GIS files.

• DBSCAN seems more relevant but I'm not sure how I can include my additional attribute ($ value) - perhaps as a third dimension?

Can anybody suggest any other toolkits/approaches to consider?

Answer 1

Look at generalized DBSCAN (GDBSCAN), which easily allows you to require neighbor points to both

1. be within a distance threshold (as in regular DBSCAN)
2. have the price vary by at most 10% (to prevent the coastal area from merging with the regular)

Answer 2

At least in hierarchical clustering you can define connectivity constraints such that only "connected" samples can belong to same cluster. In your case x and y would be used by function sklearn.neighbors.kneighbors_graph() to create the list of neighbours, and the value variable will be used in the clustering.