Spatial clustering analysis using copulas and point patterns

Rat sightings can be described by spatial coordinates in a particular region of interest defining a spatial point pattern. In this paper we investigate the spatial structure of rat sightings and its relation to a number of distance-based covariates that relate to the proliferation of rats. We use copula functions to build a particular spatial multivariate distribution using univariate margins coming from the covariate information. We use maximum likelihood together with the Bee algorithm to estimate the corresponding parameters, and perform prediction of rat sightings according to the predefined six focuses in Latina (Madrid).