Abstract
Spatial point pattern is a collection of points observed in a bounded region of d-dimensional Euclidean space, d >= 2. Usually, d = 2 or d = 3.
Individual points then represent e.g. observed locations of cell nuclei in human or animal tissue, nests of a specic bird species etc. Popular tools for point pattern analysis are functional summary characteristics describing dierent features of the complex point pattern structure via functions of one or more arguments.
Thus, we can benefit from the link between a point pattern and the corresponding empirical value of a chosen functional characteristic. This paper presents a brief overview of classical techniques from functional data analysis that can be effortlessly adapted to the context of point pattern data.