Bayesian Hierarchical Models for the Combination of Spatially Misaligned Data: A Comparison of Melding and Downscaler Approaches Using INLA and SPDE

The spatially misaligned data problem occurs when data at different spatial scales need to be combined. Bayesian hierarchical models such as melding and downscaler approaches are suitable to solve this problem but their application is limited when MCMC is used for inference due to the high computati...

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Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 2024-03, Vol.29 (1), p.110-129
Main Authors: Zhong, Ruiman, Moraga, Paula
Format: Article
Language:English
Subjects:
Agriculture
Bayesian analysis
Bayesian theory
Biostatistics
Environmental factors
Health Sciences
Mathematical models
Mathematics and Statistics
Medicine
model validation
monitoring
Monitoring/Environmental Analysis
Particulate emissions
Particulate matter
particulates
Performance assessment
prediction
problem solving
roads
simulation models
Spatial data
Statistical inference
Statistics
Statistics for Life Sciences
surfaces
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Summary:The spatially misaligned data problem occurs when data at different spatial scales need to be combined. Bayesian hierarchical models such as melding and downscaler approaches are suitable to solve this problem but their application is limited when MCMC is used for inference due to the high computational cost. The use of INLA and SPDE represents an alternative to MCMC for Bayesian inference that enables faster inference for latent Gaussian models. In this paper, we describe how INLA and SPDE can be adapted to fit Bayesian melding and downscaler models to combine spatially misaligned data. We assess the performance of the models using simulated and real data in a range of scenarios and sampling strategies and compare the suitability of the models in each situation. We also show how to obtain fine particulate matter (PM 2.5 ) predictions in the UK at a continuous surface and at policy-relevant spatial scales by combining spatially misaligned monitoring station data, satellite-derived indicators, road information, and environmental factors.
ISSN:1085-7117
1537-2693
DOI:10.1007/s13253-023-00559-w