Spatial cluster detection using nearest neighbor distance

Bar-Hen Avner, Emily Mathieu, Picard Nicolas. 2015. Spatial cluster detection using nearest neighbor distance. Spatial Statistics, 14 (Part C.) : 400-411.

Quartile : Q2, Sujet : STATISTICS & PROBABILITY / Quartile : Q2, Sujet : MATHEMATICS, INTERDISCIPLINARY APPLICATIONS / Quartile : Q3, Sujet : REMOTE SENSING / Quartile : Q3, Sujet : GEOSCIENCES, MULTIDISCIPLINARY

Résumé : Motivated by the analysis of the impact of ecological processes on spatial distribution of tree species, we introduce in this paper a novel approach to detect spatial cluster of points. Our procedure is based on an iterative transformation of the distance between points into a measure of closeness. Our measure has the advantage of being independent of an arbitrary cluster shape and allowing adjustment for covariates. The comparison of the observed measure of closeness to a reference point process leads to a hierarchical clustering of spatial points. The selection of the optimal number of clusters is performed using the Gap statistic. Our procedure is illustrated on a spatial distribution of the Dicorynia guianensis species in the French Guiana terra firme rainforest.

Mots-clés Agrovoc : forêt tropicale humide, dynamique des populations, distribution géographique, modèle de simulation, méthode statistique, régime sylvicole, classification, espacement, écologie forestière, peuplement forestier

Mots-clés géographiques Agrovoc : Guyane française, France

Mots-clés complémentaires : Dicorynia guianensis

Classification Agris : K10 - Production forestière
U10 - Informatique, mathématiques et statistiques
F40 - Écologie végétale

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

• Bar-Hen Avner, Université René Descartes (FRA)
• Emily Mathieu, Agrocampus Ouest (FRA)
• Picard Nicolas, CIRAD-ES-UPR BSef (CMR)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/577151/)

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