Specialized Agglomerations with Areal Data: Model and Detection

Abstract

This paper develops new statistical and computational methods for the automatic detection of spatial clusters displaying an over- or under- relative specialization spatial pattern. A proba- bility model provides a space partition into clusters representing homogenous portions of space as far as the probability of locating a primary unit is concerned. A cluster made of contigu- ous regions is called an agglomeration. A greedy algorithm detects specialized agglomerations through a model selection criteria. A random permutation test evaluates whether the contigu- ity property is signicant. Finally this algorithm is run on Argentinean data. Evaluating the proposed methodology concludes the paper