TY - CONF
T1 - fdmr: A Comprehensive R Package for Spatio-Temporal Modelling
AU - Yin, Xueqing
AU - Aiken, John M
AU - Bamber, Jonathan L
PY - 2023/9/5
Y1 - 2023/9/5
N2 - Spatio-temporal data analysis is crucial in various research fields. However, modelling large-scale spatio-temporal data presents challenges such as high computational demands, complex correlation structures, and the separation of mixed sources. To address these issues, we are developing "fdmr", a robust and user-friendly R package designed to model spatio-temporal data within a Bayesian framework. The "fdmr" package offers a comprehensive solution for visualizing, analyzing and modelling different types of spatio-temporal data in various disciplines. By incorporating Bayesian hierarchical models, "fdmr" allows for the flexible integration of prior knowledge and data uncertainty into the modelling process. By utilizing the Integrated Nested Laplace Approximations (INLA) algorithm and the stochastic partial differential equations (SPDE) method for model inference, "fdmr" significantly reduces the computational complexity of handling high-resolution spatio-temporal data. Furthermore, "fdmr" provides intuitive and interactive visual analytics tools that facilitate the exploration of data patterns across both space and time. This paper aims to introduce the "fdmr" package, and outline its core modelling framework through an example study on the spread of COVID-19 infection rates in England from 19 December, 2020 to 20 March, 2021.
AB - Spatio-temporal data analysis is crucial in various research fields. However, modelling large-scale spatio-temporal data presents challenges such as high computational demands, complex correlation structures, and the separation of mixed sources. To address these issues, we are developing "fdmr", a robust and user-friendly R package designed to model spatio-temporal data within a Bayesian framework. The "fdmr" package offers a comprehensive solution for visualizing, analyzing and modelling different types of spatio-temporal data in various disciplines. By incorporating Bayesian hierarchical models, "fdmr" allows for the flexible integration of prior knowledge and data uncertainty into the modelling process. By utilizing the Integrated Nested Laplace Approximations (INLA) algorithm and the stochastic partial differential equations (SPDE) method for model inference, "fdmr" significantly reduces the computational complexity of handling high-resolution spatio-temporal data. Furthermore, "fdmr" provides intuitive and interactive visual analytics tools that facilitate the exploration of data patterns across both space and time. This paper aims to introduce the "fdmr" package, and outline its core modelling framework through an example study on the spread of COVID-19 infection rates in England from 19 December, 2020 to 20 March, 2021.
U2 - 10.25436/E27C7F
DO - 10.25436/E27C7F
M3 - Conference Paper
T2 - The Fourth Spatial Data Science Symposium
Y2 - 5 September 2023 through 6 September 2023
ER -