spTimer: Spatio-Temporal Bayesian Modeling UsingR

spTimer: Spatio-Temporal Bayesian Modeling Using R

Hierarchical Bayesian modeling of large point referenced space-time data are increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem specific and user written compu...

On Bayesian spatio-temporal modeling

Modeling of spatio-temporal of albedo over Iran

The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the high...

Surveillance of dengue vectors using spatio-temporal Bayesian modeling

BACKGROUND At present, dengue control focuses on reducing the density of the primary vector for the disease, Aedes aegypti, which is the only vulnerable link in the chain of transmission. The use of new approaches for dengue entomological surveillance is extremely important, since present methods are inefficient. With this in mind, the present study seeks to analyze the spatio-temporal dynamics...

Modeling Spatio-Temporal Networks

Growing importance of application domains such as location-based services and evacuation planning highlights the need for efficient modeling of spatio-temporal networks (e.g. road networks) that takes into account changes to the network over time. The model should provide the necessary framework for developing efficient algorithms that implement frequent operations posed on such networks. A fre...