sfclust:

Bayesian Spatial Functional Clustering (original) (raw)

Introduction

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.

Installation

sfclust relies on the INLApackage 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")

Basic Usage

Suppose you have a spatio-temporal stars object namedstars_object that contains variables such ascases 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)
result

Acknowledgments

We 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.