The Spatial C(K)luster Analysis by Tree Edge Removal (SKATER). The Spatial C(K)luster Analysis by Tree Edge Removal (SKATER) algorithm introduced by Assuncao et al. (2006) is based on the optimal pruning of a minimum spanning tree that reflects the contiguity structure among the observations. It provides an optimized algorithm to prune to tree into several clusters that their values of selected variables are as similar as possible.

The node is based on the package pygeoda and here are related tools and references:


Number of clusters

The number of user-defined clusters.


The seed for the random number generator.

Cluster settings

Geometry column

Select the geometry column to implement spatial clustering.

Bound column for minibound

Select the bound column for clusters with minibound.

Attribute columns for clustering

Select columns for calculating attribute distance.

Minimum total value for the bounding variable in each output cluster

The sum of the bounding variable in each cluster must be greater than this minimum value.

Spatial weight model

Input spatial weight mode.

Available options:

  • Queen: Queen contiguity weights.
  • Rook: Rook contiguity weights.

Input Ports


Geodata for spatial clustering implementation.

Output Ports


Output table with cluster tag.

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