A spatial functional count model for heterogeneity analysis in time

TL;DR

This paper introduces a novel spatial functional count model that combines wavelet and spectral analysis for heterogeneity analysis in spatiotemporal count data, demonstrated through simulations and application to respiratory mortality prediction.

Contribution

It develops a new spatial functional estimation framework integrating wavelet and spectral methods for heterogeneity analysis in spatiotemporal count data.

Findings

Effective heterogeneity analysis in time and space.

Asymptotic properties demonstrated through simulations.

Successful application to respiratory disease mortality prediction.

Abstract

A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model. Our spatial functional estimation approach handles both wavelet-based heterogeneity analysis in time, and spectral analysis in space. Specifically, model fitting is achieved by minimising the information divergence or relative entropy between the multiscale model underlying the data and the corresponding candidates in the spatial spectral domain. A simulation study is carried out within the family of log-Gaussian Spatial Autoregressive l2-valued processes (SARl2 processes) to illustrate the asymptotic properties of the proposed spatial functional estimators. We apply our modelling strategy to spatiotemporal prediction of respiratory disease mortality.