Hello,

I do not know what type of data you have, are you working with some GIS software? Do you have GIS data? For instance, a geometry representing your areas, and a geometry representing your sites?

Assuming that you have the above type of data, and assuming that your regions completely cover your study area (with no gap in-between), you may want to test if the distribution of points within your set of polygons (totally covering the study area) can be considered random, or if the observed points count in each polygon is larger or smaller than expected.

I have built a function in my R package '

GmAMisc'. The function is called 'pointsInPolygons()'. The calculations relative to the above scenario are based on the binomial distribution: the probability of the observed counts is

*dbinom(x, size=n.of.points, prob=p)*, where 'x' is the observed number of points within a given polygon, 'n.of.points' is the total number of points, and 'p' is equal to the size of each polygon relative to sum of the polygons' area. The probability that x or fewer points will be found within a given polygon is

*pbinom(x, size=n.of.points, prob=p)*.

If you have GIS data, you can feed them into R and use the function.

I attach an example of the output (observed vs. expected counts of points withing polygons, and p values).

View attachment 2650
View attachment 2650
Best