Specialized agglomerations with areal data: model and detection

Christian Haedo, Michel Mouchart () and ,
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Christian Haedo: CIDETI, University of Bologna, Italy
Michel Mouchart: Université catholique de Louvain, ISBA & CORE, Belgium

No 2013060, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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 probability 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 contiguous regions is called an agglomeration. A greedy algorithm detects specialized agglomerations through a model selection criteria. A random permutation test evaluates whether the contiguity property is significant. Finally this algorithm is run on Argentinean data. Evaluating the proposed methodology concludes the paper.

Keywords: relative specialization; specialized agglomeration; areal (lattice) data; spatial clustering; spatial cluster detection; permutation bootstrap (search for similar items in EconPapers)
Date: 2013-11-25
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