clusterPy

Analytical regionalization is a scientific way to decide how to group of a large number of geographic areas or points into a smaller number of regions based on similiarities in one or more variables (ie. income, ethnicity, or environmental condition) that the researcher believes are important for the topic at hand. Conventional conceptions of how areas should be grouped into regions may either not be relevant to the information one is trying to illustrate (i.e., using political regions to map air pollution) or may actually be designed in ways to bias aggregated results.

Working with arbitrary spatial units may lead to aggregation problems such as the modifiable areal unit problem, the small numbers problem, spurious spatial autocorrelation, aggregation bias, aggregation error (in location allocation problems). Analytical regions arise as a way to minimize this type of problems.

Developer Team

+ Juan C. Duque (Director and Co-founder)

+ Boris Dev (Co-founder)

+ Alejandro Betancourt

+ Jose L. Franco

Special Features

Customized 'Analytical' Regionalizations based on following user specifications/inputs:

Key areal attribute to regionalize on: User regionalizes (or clusters) data based on different variables she considers important for her problem at hand. (i.e., use your own 'analytical' regions versus normative or administrative regions)
Maximum or minimum number of regions.
Threshold conditions of the maximum or minimum value that all regional clusters must meet for a given variable (i.e., a minimum threshold for a social or business project might be for all regions to have at least 100,000 people, or for an ecological project regions should have an area of at least 100 square miles).
Spatial contiguity contraints (W matrix , GAL, GWT formats), or they will be created for you based the shared geographic borders of your areal units.
Time-series signature clustering: not only can areas by clustered by a cross-sectional variable, but also by the correlation of their time-series signatures of the variable.
Non-geographic clustering: In a more general sense, our algorithms can also be extended to cluster non-geographic units based given some sort of a priori spatial (or topological) constraint.

Create ESRI shapefiles:
Current algorithms:

ARISeL: Duque and Church (2004)
AZP: Openshaw and Rao (1995)
AZP-Simulated Annealing: Openshaw and Rao (1995)
AZP-Tabu: Openshaw and Rao (1995)
AZP-R-Tabu: Openshaw and Rao (1995)
Max-p-regions (Tabu): Duque, Anselin and Rey (2010)
AMOEBA: Aldstadt and Getis (2006)
SOM: Kohonen (1990)
geoSOM: Bacao (2004)
Random

Important information

Citing ClusterPy

Please cite ClusterPy when using the software in your work

Duque, J.C.; Dev, B.; Betancourt, A.; Franco, J.L. (2011). ClusterPy: Library of spatially constrained clustering algorithms, Version 0.9.9. RiSE-group (Research in Spatial Economics). EAFIT University. http://www.rise-group.org.

A BibTeX entry for LaTeX users is:

@Manual{ClusterPy,
title = {ClusterPy: {Library} of spatially constrained clustering algorithms, {Version} 0.9.9.},
author = {Juan C. Duque and Boris Dev and Alejandro Betancourt and Jose L. Franco},
organization = {RiSE-group (Research in Spatial Economics). EAFIT University.},
address = {Colombia},
year = {2011},
url = {http://www.rise-group.org}
}

License information

See the file "LICENSE.txt" for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.