32 research outputs found
An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland
The long-term health effects of air pollution can be estimated using a spatio-temporal ecological study, where the disease data are counts of hospital admissions from populations in small areal units at yearly intervals. Spatially representative pollution concentrations for each areal unit are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over grid level concentrations from an atmospheric dispersion model. We propose a novel fusion model for estimating spatially aggregated pollution concentrations using both the modelled and monitored data, and relate these concentrations to respiratory disease in a new study in Scotland between 2007 and 2011
Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models
We introduce a general hierarchical Bayesian framework that incorporates a
flexible nonparametric data model specification through the use of empirical
likelihood methodology, which we term semiparametric hierarchical empirical
likelihood (SHEL) models. Although general dependence structures can be readily
accommodated, we focus on spatial modeling, a relatively underdeveloped area in
the empirical likelihood literature. Importantly, the models we develop
naturally accommodate spatial association on irregular lattices and irregularly
spaced point-referenced data. We illustrate our proposed framework by means of
a simulation study and through three real data examples. First, we develop a
spatial Fay-Herriot model in the SHEL framework and apply it to the problem of
small area estimation in the American Community Survey. Next, we illustrate the
SHEL model in the context of areal data (on an irregular lattice) through the
North Carolina sudden infant death syndrome (SIDS) dataset. Finally, we analyze
a point-referenced dataset from the North American Breeding Bird survey that
considers dove counts for the state of Missouri. In all cases, we demonstrate
superior performance of our model, in terms of mean squared prediction error,
over standard parametric analyses.Comment: 29 pages, 3 figue
Estimating Abundance from Counts in Large Data Sets of Irregularly-Spaced Plots using Spatial Basis Functions
Monitoring plant and animal populations is an important goal for both
academic research and management of natural resources. Successful management of
populations often depends on obtaining estimates of their mean or total over a
region. The basic problem considered in this paper is the estimation of a total
from a sample of plots containing count data, but the plot placements are
spatially irregular and non randomized. Our application had counts from
thousands of irregularly-spaced aerial photo images. We used change-of-support
methods to model counts in images as a realization of an inhomogeneous Poisson
process that used spatial basis functions to model the spatial intensity
surface. The method was very fast and took only a few seconds for thousands of
images. The fitted intensity surface was integrated to provide an estimate from
all unsampled areas, which is added to the observed counts. The proposed method
also provides a finite area correction factor to variance estimation. The
intensity surface from an inhomogeneous Poisson process tends to be too smooth
for locally clustered points, typical of animal distributions, so we introduce
several new overdispersion estimators due to poor performance of the classic
one. We used simulated data to examine estimation bias and to investigate
several variance estimators with overdispersion. A real example is given of
harbor seal counts from aerial surveys in an Alaskan glacial fjord.Comment: 37 pages, 7 figures, 4 tables, keywords: sampling, change-of-support,
spatial point processes, intensity function, random effects, Poisson process,
overdispersio
Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment
Incompatible spatial data modeling is a pervasive challenge in remote sensing
data analysis that involves field data. Typical approaches to addressing this
challenge aggregate information to a coarser common scale, i.e., compatible
resolutions. Such pre-processing aggregation to a common resolution simplifies
analysis, but potentially causes information loss and hence compromised
inference and predictive performance. To incorporate finer information to
enhance prediction performance, we develop a new Bayesian method aimed at
improving predictive accuracy and uncertainty quantification. The main
contribution of this work is an efficient algorithm that enables full Bayesian
inference using finer resolution data while optimizing computational and
storage costs. The algorithm is developed and applied to a forest damage
assessment for the 2018 Adrian storm in Carinthia, Austria, which uses field
data and high-resolution LiDAR measurements. Simulation studies demonstrate
that this approach substantially improves prediction accuracy and stability,
providing more reliable inference to support forest management decisions.Comment: 15 pages, 10 figure