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Testing randomness of spatial point patterns with the Ripleystatistic

Published online by Cambridge University Press:  04 November 2013

Gabriel Lang
Affiliation:
AgroParisTech, UMR 518 Mathématique et Informatique Appliquées, 19 avenue du Maine, 75732 Paris Cedex 15, France. gabriel.lang@agroparistech.fr
Eric Marcon
Affiliation:
AgroParisTech, UMR 745 Ecologie des Forêts de Guyane, Campus agronomique BP 316, 97379 Kourou Cedex, France; eric.marcon@agroparistech.fr

Abstract

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Aggregation patterns are often visually detected in sets of location data. These clustersmay be the result of interesting dynamics or the effect of pure randomness. We build anasymptotically Gaussian test for the hypothesis of randomness corresponding to ahomogeneous Poisson point process. We first compute the exact first and second moment ofthe Ripley K-statistic under the homogeneous Poisson point process model.Then we prove the asymptotic normality of a vector of such statistics for different scalesand compute its covariance matrix. From these results, we derive a test statistic that ischi-square distributed. By a Monte-Carlo study, we check that the test is numericallytractable even for large data sets and also correct when only a hundred of points areobserved.

Information

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2013