Max-p-regions model (Tabu)

The max-p-regions model, devised by Duque_Anselin_Rey2010, clusters a set of geographic areas into the maximum number of homogeneous regions such that the value of a spatially extensive regional attribute is above a predefined threshold value. In clusterPy we measure heterogeneity as the within-cluster sum of squares from each area to the attribute centroid of its cluster.

Algorithm

The max-p-regions algorithm is composed of two main blocks:

• Construction of a initial feasible solution.
• Local improvement.

Parameters

• Clustering attribute: Area attribute to cluster.
• Threshold attribute: Is the spatially extensive attribute that will beconstrained to be above the Threshold value.
• threshold value: Minimum value of the constrained variable at regional level.
• Maximum iterations: Number of times that the construction phase is repeated. The larger the value the higher the possibility of getting a largenumber of regions.
• Tabu maximum steps: Number of steps for the Tabu procedure.
• Tabu List length: Number of times a reverse move is prohibited

About Clusterpy

This algorithm is part of a set of algorithms that can be found in the Clusterpy library for Python. Website. Also on Github