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