In this paper we investigate a method proposed recently by K.H. Knuth to find the optimal bin size of an histogram as a tool for statistical analysis of spatial point processes. We test it through numerical simulations on various spatial processes which are of interest in ecology. We show that Knuth optimal bin size rule reducing noisy fluctuations performs better than standard kernel methods to infer the intensity of the underlying process.
Application of optimal data-based binning method to spatial analysis of ecological datasets
TOVO, ANNA;FORMENTIN, MARCO;FAVRETTI, MARCO;MARITAN, AMOS
2016
Abstract
In this paper we investigate a method proposed recently by K.H. Knuth to find the optimal bin size of an histogram as a tool for statistical analysis of spatial point processes. We test it through numerical simulations on various spatial processes which are of interest in ecology. We show that Knuth optimal bin size rule reducing noisy fluctuations performs better than standard kernel methods to infer the intensity of the underlying process.
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