  Investigation of highly structured data sets to unveil statistical
regularities is of major importance in complex system research. The first step
is to choose the scale at which to observe the process, the most informative
scale being the one that includes the important features while disregarding
noisy details in the data. In the investigation of spatial patterns, the
optimal scale defines the optimal bin size of the histogram in which to
visualize the empirical density of the pattern. 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. Moreover it can be used to highlight
relevant spatial characteristics of the underlying distribution such as space
anisotropy and clusterization. We apply these findings to analyse cluster-like
structures in plants' arrangement of Barro Colorado Island rainforest.
