We describe the Poisson distribution and how it is used to detect randomness in the spatial (or temporal) pattern of objects or events. We discuss several options for testing and quantifying the nature of spatial patterns, ranging from aggregated, through randomly arranged, up to regularly positioned objects. We demonstrate an approach based on comparing the variance and mean of count data, before moving to alternative methods that use the K-function alongside other similar functions, describing the change in spatial relationships across a range of spatial scales. Finally, we introduce the binomial distribution and how to estimate its p parameter (proportion of outcomes of a particular type within a set of observed objects) together with its confidence interval. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use, including the spatstat and fitdistrplus packages.
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