Spatio-temporal change of support modeling with R

Spatio-temporal change of support methods are designed for statistical analysis on spatial and temporal domains which can differ from those of the observed data. Previous work introduced a parsimonious class of Bayesian hierarchical spatio-temporal models, which we refer to as STCOS, for the case of...

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Bibliographic Details
Published in:Computational statistics 2021-03, Vol.36 (1), p.749-780
Main Authors: Raim, Andrew M., Holan, Scott H., Bradley, Jonathan R., Wikle, Christopher K.
Format: Article
Language:English
Subjects:
Case studies
Congressional districts
Customization
Data analysis
Demographic variables
Economic Theory/Quantitative Economics/Mathematical Methods
Family income
Mathematics and Statistics
Methods
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Social factors
Software utilities
Statistical analysis
Statistical methods
Statistics
Variables
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Summary:Spatio-temporal change of support methods are designed for statistical analysis on spatial and temporal domains which can differ from those of the observed data. Previous work introduced a parsimonious class of Bayesian hierarchical spatio-temporal models, which we refer to as STCOS, for the case of Gaussian outcomes. Application of STCOS methodology from this literature requires a level of proficiency with spatio-temporal methods and statistical computing which may be a hurdle for potential users. The present work seeks to bridge this gap by guiding readers through STCOS computations. We focus on the R computing environment because of its popularity, free availability, and high quality contributed packages. The stcos package is introduced to facilitate computations for the STCOS model. A motivating application is the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. The STCOS methodology offers a principled approach to compute model-based estimates and associated measures of uncertainty for ACS variables on customized geographies and/or time periods. We present a detailed case study with ACS data as a guide for change of support analysis in R, and as a foundation which can be customized to other applications.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-020-01029-4