A general theory for preferential sampling in environmental networks
Watson, J; Zidek, JV; Shaddick, G
Date: 28 November 2019
Journal
Annals of Applied Statistics
Publisher
Institute of Mathematical Statistics
Publisher DOI
Abstract
This paper presents a general model framework for detecting the
preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a
site–selection process that considers where and when sites are placed
to ...
This paper presents a general model framework for detecting the
preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a
site–selection process that considers where and when sites are placed
to measure the process. The environmental process may be spatial,
temporal or spatio–temporal in nature. By sharing random effects between the two processes, the joint model is able to establish whether
site placement was stochastically dependent of the environmental process under study. Furthermore, if stochastic dependence is identified
between the two processes, then inferences about the probability distribution of the spatio–temporal process will change, as will predictions made of the process across space and time. The embedding
into a spatio–temporal framework also allows for the modelling of
the dynamic site—selection process itself. Real–world factors affecting both the size and location of the network can be easily modelled
and quantified. Depending upon the choice of population of locations to consider for selection across space and time under the site–
selection process, different insights about the precise nature of preferential sampling can be obtained. The general framework developed
in the paper is designed to be easily and quickly fit using the R-INLA
package. We apply this framework to a case study involving particulate air pollution over the UK where a major reduction in the size
of a monitoring network through time occurred. It is demonstrated
that a significant response–biased reduction in the air quality monitoring network occurred, namely the relocation of monitoring sites to
locations with the highest pollution levels, and the routine removal
of sites at locations with the lowest. We also show that the network
was consistently unrepresentative of the levels of particulate matter
seen across much of GB throughout the operating life of the network.
Finally we show that this may have led to a severe over-reporting of
the population–average exposure levels experienced across GB. This
could have great impacts on estimates of the health effects of black
smoke levels.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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