4Dmodeller was a follow-on project from the ERC-funded GlobalMass grant (www.globalmass.eu) that advanced the use of space-time statistical inference to separate global sea level rise into its different sources.
We worked to generalise this approach into a dedicated software tool, 4Dmodeller, capable of solving a wider range of large-scale, space-time (i.e. four-dimensional) problems. Such a tool has the potential to transform many disciplines, not just in Earth and environmental sciences, but also in areas such as public health and national security, and has the potential to attract huge commercial interest, for example in extreme weather risk assessment for insurance and reinsurance.
The 4Dmodeller software and a range of tutorials are now available at https://4dmodeller.github.io/fdmr/. A paper describing the package has also been published in the Journal of Open Source Software.
- Aiken JM, Jones G, Yin X, Abele AK, Woods C, Westaway RM and Bamber JL, 2025. 4DModeller: a spatio-temporal modelling package. Journal of Open Source Software, 10(106), 7047. 10.21105/joss.07047
- Yin X, Aiken JM, Harris R and Bamber JL, 2024. A Bayesian spatio-temporal model of COVID-19 spread in England. Scientific Reports 14(10335). 10.1038/s41598-024-60964-0
- Yin X, Aiken JM and Bamber JL, 2023. fdmr: A Comprehensive R Package for Spatio-Temporal Modelling. UC Santa Barbara: Center for Spatial Studies. 10.25436/E27C7F
- Yin X, Aiken JM, Harris R and Bamber JL, 2023. Spatio-temporal spread of COVID-19 and its associations with socioeconomic, demographic and environmental factors in England: A Bayesian hierarchical spatio-temporal model. arXiv:2308.09404.
- Aiken JM, Yin X, Royston S, Ziegler Y and Bamber JL, 2023. From Sea Level Rise to COVID-19: Extending a Bayesian Hierarchical Model to unfamiliar problems with the 4D-Modeller framework, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1680. 10.5281/zenodo.10132463.
This work is supported by UKRI grant EP/X022641/1