Max-P Greedy. A greedy algorithm to solve the max-p-region problem.

The so-called max-p regions model (outlined in Duque, Anselin, and Rey 2012) uses a different approach and considers the regionalization problem as an application of integer programming. In addition, the number of regions is determined endogenously.

The algorithm itself consists of a search process that starts with an initial feasible solution and iteratively improves upon it while maintaining contiguity among the elements of each cluster.

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



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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