Level set Cox processes
Journal article, 2018

An extension of the popular log-Gaussian Cox process (LGCP) model for spatial point patterns is proposed for 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 specified using a categorically valued random field. This classification is defined through level set operations on a Gaussian random field and allows for standard stationary covariance structures, such as the Matérn family, to be used to model 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.

Cox process

Level set inversion

Classification

Gaussian fields

Point process

Author

Anders Hildeman

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

David Bolin

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Jonas Wallin

Lund University

Janine B. Illian

University of St Andrews

Spatial Statistics

2211-6753 (ISSN)

Vol. 28 169-193

Latent jump fields for spatial statistics

Swedish Research Council (VR), 2017-01-01 -- 2020-12-31.

Subject Categories

Other Computer and Information Science

Applied Mechanics

Probability Theory and Statistics

DOI

10.1016/j.spasta.2018.03.004

More information

Latest update

12/10/2018