fdmr: A Comprehensive R Package for Spatio-Temporal Modelling

Xueqing Yin, John M Aiken, Jonathan L Bamber

Research output: Contribution to conferenceConference Paperpeer-review

Abstract

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.
Original languageEnglish
DOIs
Publication statusPublished - 5 Sept 2023
EventThe Fourth Spatial Data Science Symposium - Online
Duration: 5 Sept 20236 Sept 2023
http://sdss2023.spatial-data-science.net/

Conference

ConferenceThe Fourth Spatial Data Science Symposium
Abbreviated titleSDSS 2023
Period5/09/236/09/23
Internet address

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  • 4Dm: 4Dmodeller

    Bamber, J. L. (Principal Investigator), Aiken, J. M. (Collaborator), Xueqing, X. (Researcher), Jones, G. M. (Engineer), Abele, A. K. (Researcher) & Westaway, R. M. (Manager)

    1/12/2231/05/24

    Project: Research

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