Cokrig-and-Regress for Spatially Misaligned Environmental Data

TL;DR

This paper introduces the Cokrig-and-Regress (CNR) method for estimating spatial regression models with multiple covariates and non-linear relationships in spatially misaligned environmental data, improving accuracy over existing methods.

Contribution

The paper proposes a novel CNR approach that handles multiple covariates and non-linear associations in spatially misaligned data using cokriging and a generalized Kronecker product covariance.

Findings

CNR outperforms existing methods like nearest-neighbor interpolation in simulations.

Application to Chinese air pollution data uncovers non-linear relationships with meteorological variables.

Bias correction and uncertainty quantification are effectively achieved through a parametric bootstrap.

Abstract

Spatially misaligned data, where the response and covariates are observed at different spatial locations, commonly arise in many environmental studies. Much of the statistical literature on handling spatially misaligned data has been devoted to the case of a single covariate and a linear relationship between the response and this covariate. Motivated by spatially misaligned data collected on air pollution and weather in China, we propose a cokrig-and-regress (CNR) method to estimate spatial regression models involving multiple covariates and potentially non-linear associations. The CNR estimator is constructed by replacing the unobserved covariates (at the response locations) by their cokriging predictor derived from the observed but misaligned covariates under a multivariate Gaussian assumption, where a generalized Kronecker product covariance is used to account for spatial correlations within and between covariates. A parametric bootstrap approach is employed to bias-correct the CNR estimates of the spatial covariance parameters and for uncertainty quantification. Simulation studies demonstrate that CNR outperforms several existing methods for handling spatially misaligned data, such as nearest-neighbor interpolation. Applying CNR to the spatially misaligned air pollution and weather data in China reveals a number of non-linear relationships between PM$_{2.5}$ concentration and several meteorological covariates.