Hierarchical spatially varying coefficient and temporal dynamic process models using spTDyn

Abstract

Bayesian hierarchical spatio-temporal models are becoming increasingly important due to the increasing availability of space-time data in various domains. In this paper we develop a user friendly R package, spTDyn, for spatio-temporal modelling. It can be used to fit models with spatially varying and temporally dynamic coefficients. The former is used for modelling the spatially varying impact of explanatory variables on the response caused by spatial misalignment. This issue can arise when the covariates only vary over time, or when they are measured over a grid and hence do not match the locations of the response point-level data. The latter is to examine the temporally varying impact of explanatory variables in space-time data due, for example, to seasonality or other time-varying effects. The spTDyn package uses Markov chain Monte Carlo sampling written in C, which makes computations highly efficient, and the interface is written in R making these sophisticated modelling techniques easily accessible to statistical analysts. The models and software, and their advantages, are illustrated using temperature and ozone space-time data.

Keywords:

• Bayesian modelling
• spatially varying coefficient
• spatio-temporal dynamic linear model
• Gibbs sampling

Acknowledgements

The authors are grateful to the anonymous reviewers for their constructive comments. They would like to acknowledge the advice provided by Dr Mark Howden, Dr Steve Crimp and Mr Garry Hopwood of CSIRO. The authors also wish to acknowledge the support of the Agriculture Flagship, CSIRO, for funding this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. See, http://www.epa.gov/region1/airquality/avg8hr.html