Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources

TL;DR

This paper develops a Bayesian hierarchical model with linearized nonlinear components and sparse covariance structures to improve spatio-temporal prediction of environmental exposure levels from multiple data sources, especially when data are aggregated at different temporal scales.

Contribution

It introduces an efficient MCMC approach for nonlinear latent process models with multiscale data, enhancing prediction accuracy and computational feasibility in environmental epidemiology.

Findings

Effective pooling of multiple data sources reduces prediction error.

Linearization and sparsity techniques enable scalable inference.

Model successfully addresses temporal aggregation challenges.

Abstract

Spatio-temporal prediction of levels of an environmental exposure is an important problem in environmental epidemiology. Our work is motivated by multiple studies on the spatio-temporal distribution of mobile source, or traffic related, particles in the greater Boston area. When multiple sources of exposure information are available, a joint model that pools information across sources maximizes data coverage over both space and time, thereby reducing the prediction error. We consider a Bayesian hierarchical framework in which a joint model consists of a set of submodels, one for each data source, and a model for the latent process that serves to relate the submodels to one another. If a submodel depends on the latent process nonlinearly, inference using standard MCMC techniques can be computationally prohibitive. The implications are particularly severe when the data for each submodel are aggregated at different temporal scales. To make such problems tractable, we linearize the nonlinear components with respect to the latent process and induce sparsity in the covariance matrix of the latent process using compactly supported covariance functions. We propose an efficient MCMC scheme that takes advantage of these approximations. We use our model to address a temporal change of support problem whereby interest focuses on pooling daily and multiday black carbon readings in order to maximize the spatial coverage of the study region.