Specialized agglomerations with Lattice data: Model and detection

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 is used to provide a basis for a space partition into clusters representing homogeneous 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.

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Document type	Article de périodique (Journal article) – Article de recherche
Access type	Accès interdit
Publication date	2015
Language	Anglais
Journal information	"Spatial Statistics" - Vol. 11, p. 113-131 (2015)
Peer reviewed	yes
Publisher	Elsevier (Amsterdam)
issn	2211-6753
Publication status	Publié
Affiliations	UCL - SSH/LIDAM/CORE - Center for operations research and econometrics UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Keywords	Relative specialization ; Specialized agglomeration ; Lattice data ; Spatial clustering ; Spatial cluster detection ; Permutation bootstrap
Links	http://hdl.handle.net/2078.1/155334[Handle] https://doi.org/10.1016/j.spasta.2014.11.003[DOI]

See also
	[boreal:133103]	Specialized Agglomerations with Areal Data: Model and Detection