Bayesian spatio-temporal weighted regression for integrating missing and misaligned environmental data

TL;DR

This paper introduces a Bayesian weighted regression model that effectively integrates multi-source environmental data with missing and misaligned observations, improving prediction accuracy and uncertainty quantification.

Contribution

It develops a novel spatio-temporal kernel within a Bayesian framework that handles irregular supports and missing data without separate gap-filling, enhancing environmental exposure estimation.

Findings

Simulation studies show careful parameter tuning reduces bias.

The proposed method outperforms traditional models in predictive accuracy.

Application to PM2.5 estimation demonstrates improved surface mapping and uncertainty reduction.

Abstract

Estimating environmental exposures from multi-source data is central to public health research and policy. Integrating data from satellite products and ground monitors are increasingly used to produce exposure surfaces. However, spatio-temporal misalignment often induced from missing data introduces substantial uncertainty and reduces predictive accuracy. We propose a Bayesian weighted predictor regression framework that models spatio-temporal relationships when predictors are observed on irregular supports or have substantial missing data, and are not concurrent with the outcome. The key feature of our model is a spatio-temporal kernel that aggregates the predictor over local space-time neighborhoods, built directly into the likelihood, eliminating any separate gap-filling or forced data alignment stage. We introduce a numerical approximation using a Voronoi-based spatial quadrature combined with irregular temporal increments for estimation under data missingness and misalignment. We showed that misspecification of the spatial and temporal lags induced bias in the mean and parameter estimates, indicating the need for principled parameter selection. Simulation studies confirmed these findings, where careful tuning was critical to control bias and achieve accurate prediction, while the proposed quadrature performed well under severe missingness. As an illustrative application, we estimated fine particulate matter (PM$_{2.5}$) in northern California using satellite-derived aerosol optical depth (AOD) and wildfire smoke plume indicators. Relative to a traditional collocated linear model, our approach improved out-of-sample predictive performance, reduced uncertainty, and yielded robust temporal predictions and spatial surface estimation. Our framework is extensible to additional spatio-temporally varying covariates and other kernel families.