Abstract : Aggregation patterns are often visually detected in sets of location data. These clusters may be the result of interesting dynamics or the effect of pure randomness. We build an asymptotically Gaussian test for the hypothesis of randomness corresponding to a homogeneous Poisson point process. We first compute the exact first and second moment of the Ripley K-statistic under the homogeneous Poisson point process model. Then we prove the asymptotic normality of a vector of such statistics for different scales and compute its covariance matrix. From these results, we derive a test statistic that is chi-square distributed. By a Monte-Carlo study, we check that the test is numerically tractable even for large data sets and also correct when only a hundred of points are observed
https://hal-agroparistech.archives-ouvertes.fr/hal-01502640
Contributeur : Carole Legrand
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Soumis le : mercredi 5 avril 2017 - 20:43:03
Dernière modification le : mardi 6 février 2018 - 11:12:11
Gabriel Lang, Eric Marcon. Testing randomness of spatial point patterns with the Ripley statistic. ESAIM: Probability and Statistics, EDP Sciences, 2013, 17, pp.767 - 788. 〈10.1051/ps/2012027〉. 〈hal-01502640〉
