Multivariate Geometric Anisotropic Cox Processes

TL;DR

This paper presents a new framework for modeling multivariate anisotropic Cox processes, enabling better understanding and inference of complex spatial dependencies in ecological data.

Contribution

It introduces a novel family of multivariate anisotropic random fields and constructs corresponding point processes, with conditions for validity and a likelihood-based inference method.

Findings

Successful application to ecological data from Barro Colorado Island

Provides conditions ensuring model validity

Offers a new inference mechanism for complex spatial processes

Abstract

This paper introduces a new modelling framework for multivariate anisotropic Cox processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields and construct a family of anisotropic point processes from it. We give conditions that make the models valid, and we provide additional understanding of valid point process dependence. We also propose a likelihood-based inference mechanism for this type of process. Finally we illustrate the utility of the proposed modelling framework by analysing spatial ecological observations of plants and trees in the Barro Colorado Island study.