Non-stationary spatial model with transfer learning in air pollution data

Oregon Convention Center 

Ambient air pollution measurements from regulatory monitoring networks are routinely used to support epidemiologic studies and environmental policy decision making. However, regulatory monitors are spatially sparse and preferentially located in areas with large populations. Numerical model output can be leveraged into the inference and prediction of air pollution data combining with measurements from monitors. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location like air pollution data. In the paper, we employ localized covariance parameters learned from the numerical output model to knit together into a global nonstationary covariance, and use this nonstationary covariance in a fully Bayesian model in which the unknown spatial process has a Gaussian process prior distribution. We model the nonstationary structure with greatly reduced number of parameters to make it computationally feasible.

spatial model

Bayesian model

non-stationary model

air pollution

environment 

Section on Statistics and the Environment