sfclust provides a Bayesian framework for clustering spatio-temporal data, supporting both Gaussian and non-Gaussian responses. The approach enforces spatial adjacency constraints, ensuring that clusters consist of neighboring regions with similar temporal dynamics.
The package implements the methodology described in “Bayesian
Spatial Functional Data Clustering: Applications in Disease
Surveillance”, available at arXiv:2407.12633. In
addition to the core clustering algorithm, sfclust offers
tools for model diagnostics, visualization, and result
summarization.
sfclust relies on the INLA
package for efficient Bayesian inference. Install it with:
install.packages("INLA", dependencies = TRUE,
repos = c(getOption("repos"), INLA = "https://inla.r-inla-download.org/R/stable")
)Once INLA is installed, you can install the development
version of sfclust from GitHub:
devtools::install_github("ErickChacon/sfclust")Suppose you have a spatio-temporal stars object named
stars_object that contains variables such as
cases and expected (the expected number of
cases). The following code fits a spatial functional clustering model,
where each cluster’s mean trend is modeled with a temporal random walk
and an unstructured random effect:
form <- cases ~ f(idt, model = "rw1") + f(id, model = "iid")
result <- sfclust(stars_object, formula = form, family = "poisson", E = expected, niter = 1000)
resultWe thank the authors of “Bayesian Clustering of Spatial Functional Data with Application to a Human Mobility Study During COVID-19”, by Bohai Zhang, Huiyan Sang, Zhao Tang Luo, and Hui Huang (DOI:10.1214/22-AOAS1643, Annals of Applied Statistics, 2023), for making their supplementary code publicly available (DOI:10.1214/22-AOAS1643SUPPB). Our implementation builds upon their clustering algorithm and uses their code for generating spanning trees. We are grateful for their contributions and inspiration.