We propose a method for detecting significant interactions in very large
multivariate spatial point patterns. This methodology develops high dimensional
data understanding in the point process setting. The method is based on
modelling the patterns using a flexible Gibbs point process model to directly
characterise point-to-point interactions at different spatial scales. By using
the Gibbs framework significant interactions can also be captured at small
scales. Subsequently, the Gibbs point process is fitted using a
pseudo-likelihood approximation, and we select significant interactions
automatically using the group lasso penalty with this likelihood approximation.
Thus we estimate the multivariate interactions stably even in this setting. We
demonstrate the feasibility of the method with a simulation study and show its
power by applying it to a large and complex rainforest plant population data
set of 83 species
