Modeling Big, Heterogeneous, Non-Gaussian
Spatial and Spatio-Temporal Data Using FRK
Dublin Core
Title
Modeling Big, Heterogeneous, Non-Gaussian
Spatial and Spatio-Temporal Data Using FRK
Spatial and Spatio-Temporal Data Using FRK
Subject
areal data, basis functions, big data, change-of-support, fixed rank kriging, nonGaussian data, spatial statistics
Description
Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent,
and their analysis is needed in a variety of disciplines. FRK is an R package for spatial
and spatio-temporal modeling and prediction with very large data sets that, to date,
has only supported linear process models and Gaussian data models. In this paper, we
describe a major upgrade to FRK that allows for non-Gaussian data to be analyzed in a
generalized linear mixed model framework. These vastly more general spatial and spatiotemporal models are fitted using the Laplace approximation via the software TMB. The
existing functionality of FRK is retained with this advance into non-Gaussian models; in
particular, it allows for automatic basis-function construction, it can handle both pointreferenced and areal data simultaneously, and it can predict process values at any spatial
support from these data. This new version of FRK also allows for the use of a large
number of basis functions when modeling the spatial process, and thus it is often able
to achieve more accurate predictions than previous versions of the package in a Gaussian
setting. We demonstrate innovative features in this new version of FRK, highlight its ease
of use, and compare it to alternative packages using both simulated and real data set
and their analysis is needed in a variety of disciplines. FRK is an R package for spatial
and spatio-temporal modeling and prediction with very large data sets that, to date,
has only supported linear process models and Gaussian data models. In this paper, we
describe a major upgrade to FRK that allows for non-Gaussian data to be analyzed in a
generalized linear mixed model framework. These vastly more general spatial and spatiotemporal models are fitted using the Laplace approximation via the software TMB. The
existing functionality of FRK is retained with this advance into non-Gaussian models; in
particular, it allows for automatic basis-function construction, it can handle both pointreferenced and areal data simultaneously, and it can predict process values at any spatial
support from these data. This new version of FRK also allows for the use of a large
number of basis functions when modeling the spatial process, and thus it is often able
to achieve more accurate predictions than previous versions of the package in a Gaussian
setting. We demonstrate innovative features in this new version of FRK, highlight its ease
of use, and compare it to alternative packages using both simulated and real data set
Creator
Matthew Sainsbury-Dale
Source
file:///C:/Users/User/Downloads/v108i10%20(2).pdf
Publisher
atthew Sainsbury-Dale
University of Wollongong
University of Wollongong
Date
March 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Matthew Sainsbury-Dale, “Modeling Big, Heterogeneous, Non-Gaussian
Spatial and Spatio-Temporal Data Using FRK,” Repository Horizon University Indonesia, accessed April 7, 2025, https://repository.horizon.ac.id/items/show/8323.
Spatial and Spatio-Temporal Data Using FRK,” Repository Horizon University Indonesia, accessed April 7, 2025, https://repository.horizon.ac.id/items/show/8323.