Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport

TL;DR

This paper introduces a novel method combining normalizing flows and spherical point processes to model and estimate non-homogeneous Poisson process intensities on the sphere, demonstrated through simulations and cyclone data analysis.

Contribution

It presents a new approach for modeling spherical Poisson intensities using measure transport and normalizing flows, enabling flexible and efficient estimation.

Findings

Normalizing flows provide a flexible way to model spherical intensities.

Map architecture significantly affects fit quality.

Method successfully applied to cyclone event data.

Abstract

Recent years have seen an increased interest in the application of methods and techniques commonly associated with machine learning and artificial intelligence to spatial statistics. Here, in a celebration of the ten-year anniversary of the journal Spatial Statistics, we bring together normalizing flows, commonly used for density function estimation in machine learning, and spherical point processes, a topic of particular interest to the journal's readership, to present a new approach for modeling non-homogeneous Poisson process intensity functions on the sphere. The central idea of this framework is to build, and estimate, a flexible bijective map that transforms the underlying intensity function of interest on the sphere into a simpler, reference, intensity function, also on the sphere. Map estimation can be done efficiently using automatic differentiation and stochastic gradient descent, and uncertainty quantification can be done straightforwardly via nonparametric bootstrap. We investigate the viability of the proposed method in a simulation study, and illustrate its use in a proof-of-concept study where we model the intensity of cyclone events in the North Pacific Ocean. Our experiments reveal that normalizing flows present a flexible and straightforward way to model intensity functions on spheres, but that their potential to yield a good fit depends on the architecture of the bijective map, which can be difficult to establish in practice.