Level set Cox processes

TL;DR

This paper extends the log-Gaussian Cox process model by incorporating a spatial mixture model with level set operations, enabling better modeling of heterogeneous spatial point patterns with sharp discontinuities.

Contribution

The paper introduces a novel extension of LGCP using a latent spatial mixture model with level set operations, allowing for modeling non-stationary and discontinuous spatial processes.

Findings

The new model captures heterogeneous spatial behaviors more accurately.

Bayesian inference via an efficient MCMC method is developed.

Application to rainforest data demonstrates improved fit over standard LGCP.

Abstract

The log-Gaussian Cox process (LGCP) is a popular point process for modeling non-interacting spatial point patterns. This paper extends the LGCP model to handle data exhibiting fundamentally different behaviors in different subregions of the spatial domain. The aim of the analyst might be either to identify and classify these regions, to perform kriging, or to derive some properties of the parameters driving the random field in one or several of the subregions. The extension is based on replacing the latent Gaussian random field in the LGCP by a latent spatial mixture model. The mixture model is specified using a latent, categorically valued, random field induced by level set operations on a Gaussian random field. Conditional on the classification, the intensity surface for each class is modeled by a set of independent Gaussian random fields. This allows for standard stationary covariance structures, such as the Mat\'{e}rn family, to be used to model Gaussian random fields with some degree of general smoothness but also occasional and structured sharp discontinuities. A computationally efficient MCMC method is proposed for Bayesian inference and we show consistency of finite dimensional approximations of the model. Finally, the model is fitted to point pattern data derived from a tropical rainforest on Barro Colorado island, Panama. We show that the proposed model is able to capture behavior for which inference based on the standard LGCP is biased.