Thinning spatial point processes into Poisson processes

This project describes methods for randomly thinning certain classes of spatial point processes. In the case of a Markov point process, the proposed method involves a dependent thinning of a spatial birth-and-death process, where clans of ancestors associated with the original points are identified, and where one simulates backwards and forwards in order to obtain the thinned process. In the case of a Cox process, a simple independent thinning technique is proposed. In both cases, the thinning results in a Poisson process if and only if the true Papangelou conditional intensity is used, and thus can be used as a diagnostic for assessing the goodness-of-fit of a spatial point process model. Several examples, including clustered and inhibitive point processes, are considered.