I am new to point pattern analysis. Trough my readings I haven't seen that any book would suggest to use All-to-All distances for point pattern analysis, but rather they talk about NND or other second order characteristics (K function or pair correlations, etc.). Therefore, I have a naive question why All-to-All is not promoted for point pattern analysis (like cluster detection)?
edit: Imagine one has 100 point patterns, that were experimentally observed (each observation has a specific window). One can compute all pairwise distances for these patterns and compute pairwise distances for uniformly distributed points in the corresponding windows. Then, one can look at the cumulative distributions of these pairwise distances (attached figure).
I am afraid that due to the variability within the observed data, it can be not a trustworthy approach.