analysis of spatial point patterns by kernel identification

in the analysis of spatial point patterns, complete spatial randomness (csr) hypothesis,which is a restriction of a homogenous poisson process to study region a, operates as a dividinghypothesis between “regular” and “aggregated” patterns. meanwhile, many alternatives to csr inaggregated patterns are extensions of homogenous poisson processes themselves. therefore, when thecsr hypothesis is rejected, results related to poisson processes may be used to formulate plausiblealternatives to csr. in this paper, we propose a new statistic for testing csr and then by applying it inconjunction with a notion of kernels of a point pattern, we determine the “parents” of a poisson clusterprocess when the csr hypothesis is rejected and a neyman-scott process is assumed for the pointpattern under alternative hypothesis. we have made power studies for our test statistic by simulation, andhave also surveyed the performance of our method on a certain point pattern. finally, the whole methodis carried on certain real life data.