A statistical test for ripley's K function rejection of poisson null hypothesis

Abstract : Ripley’s K function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution. We introduce a statistical test against complete spatial randomness (CSR). The test returns the p-value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided.
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Eric Marcon, Stephane Traissac, Gabriel Lang. A statistical test for ripley's K function rejection of poisson null hypothesis. International Scholarly Research Network, ISRN Ecology, 2013, 2013 (Article ID753475), 9 p. ⟨10.1155/2013/753475⟩. ⟨hal-01502637⟩

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