FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets
Dublin Core
Title
FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets
Subject
: basic areal units, EM algorithm, fixed rank kriging, spatial random effects model,
spatial prediction.
spatial prediction.
Description
FRK is an R software package for spatial/spatio-temporal modeling and prediction
with large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used manifolds (in Euclidean space and on the surface of the sphere), for both
spatial and spatio-temporal fields. It differs from many of the packages for spatial modeling and prediction by avoiding stationary and isotropic covariance and variogram models,
instead constructing a spatial random effects (SRE) model on a fine-resolution discretized
spatial domain. The discrete element is known as a basic areal unit (BAU), whose introduction in the software leads to several practical advantages. The software can be used to
(i) integrate multiple observations with different supports with relative ease; (ii) obtain
exact predictions at millions of prediction locations (without conditional simulation); and
(iii) distinguish between measurement error and fine-scale variation at the resolution of
the BAU, thereby allowing for reliable uncertainty quantification. The temporal component is included by adding another dimension. A key component of the SRE model is
the specification of spatial or spatio-temporal basis functions; in the package, they can
be generated automatically or by the user. The package also offers automatic BAU construction, an expectation-maximization (EM) algorithm for parameter estimation, and
functionality for prediction over any user-specified polygons or BAUs. Use of the package
is illustrated on several spatial and spatio-temporal datasets, and its predictions and the
model it implements are extensively compared to others commonly used for spatial prediction and modeling
with large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used manifolds (in Euclidean space and on the surface of the sphere), for both
spatial and spatio-temporal fields. It differs from many of the packages for spatial modeling and prediction by avoiding stationary and isotropic covariance and variogram models,
instead constructing a spatial random effects (SRE) model on a fine-resolution discretized
spatial domain. The discrete element is known as a basic areal unit (BAU), whose introduction in the software leads to several practical advantages. The software can be used to
(i) integrate multiple observations with different supports with relative ease; (ii) obtain
exact predictions at millions of prediction locations (without conditional simulation); and
(iii) distinguish between measurement error and fine-scale variation at the resolution of
the BAU, thereby allowing for reliable uncertainty quantification. The temporal component is included by adding another dimension. A key component of the SRE model is
the specification of spatial or spatio-temporal basis functions; in the package, they can
be generated automatically or by the user. The package also offers automatic BAU construction, an expectation-maximization (EM) algorithm for parameter estimation, and
functionality for prediction over any user-specified polygons or BAUs. Use of the package
is illustrated on several spatial and spatio-temporal datasets, and its predictions and the
model it implements are extensively compared to others commonly used for spatial prediction and modeling
Creator
Andrew Zammit-Mangion
Source
https://www.jstatsoft.org/article/view/v098i04
Publisher
University of Wollongong
Date
May 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Andrew Zammit-Mangion, “FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets,” Repository Horizon University Indonesia, accessed May 9, 2025, https://repository.horizon.ac.id/items/show/8190.