DAST (DisAggregation in Space and Time)

DAST fits spatial disaggregation models for areal data observed on maps where polygon boundaries change over time. It combines polygon responses, optional fine-scale covariates, and population rasters to infer fine-scale spatial risk surfaces.

Installation

You can install from GitHub with:

# install.packages("remotes")
remotes::install_github("nripstein/DAST")

or the development branch from

remotes::install_github("nripstein/DAST", ref = "development")

Data Requirements

polygon_shapefile_list: one sf polygon object per time point, with area_id and response columns by default.
covariate_rasters_list: optional matching list of terra::SpatRaster covariate stacks.
aggregation_rasters_list: optional matching list of terra::SpatRaster aggregation or population rasters; if omitted, uniform aggregation weights are used.
Polygon and raster inputs should use compatible coordinate reference systems and aligned raster grids within each time point.

Workflow

# polygon_list: list of sf polygon objects, one per time point
# covariate_list: optional list of terra::SpatRaster covariate stacks
# aggregation_list: optional list of terra::SpatRaster aggregation/population rasters
dat <- prepare_data_mmap(
	polygon_shapefile_list = polygon_list,
	covariate_rasters_list = covariate_list,
	aggregation_rasters_list = aggregation_list
)
fit <- disag_model_mmap(dat, engine = "AGHQ")
pred <- predict(fit)

Predictions are returned as fine-scale rate or risk surfaces. When the aggregation raster represents population or exposure, expected fine-cell counts can be obtained by multiplying the predicted surface by the matching aggregation raster.

Fitting Algorithms

It is straightforward to use the model-fitting algorithm of your choice by specifying an engine argument in disag_model_mmap().

Engine	Description	Recommended use
`AGHQ`	Approximate Bayesian inference using Adaptive Gauss-Hermite Quadrature.	Default option for fast approximate fully Bayesian inference.
`TMB`	Laplace approximation through Template Model Builder.	Fastest option, using Empirical Bayes instead of full Bayes.
`MCMC`	NUTS algorithm implimented in tmbstan.	Provides asymptotically exact posterior sampling, but is very slow; `predict()` is not currently implemented.

Passing Engine-Specific Arguments

Use engine.args to pass arguments specific to the fitting algorithm selected.

# AGHQ controls
fit_aghq <- disag_model_mmap(
	dat,
	engine = "AGHQ",
	engine.args = list(
		aghq_k = 2,
		optimizer = "BFGS"
	)
)
# TMB controls
fit_tmb <- disag_model_mmap(
	dat,
	engine = "TMB",
	engine.args = list(
		iterations = 1000,
		hess_control_ndeps = 1e-4
	)
)
# MCMC controls via tmbstan
fit_mcmc <- disag_model_mmap(
	dat,
	engine = "MCMC",
	engine.args = list(
		chains = 4,
		iter = 2000,
		warmup = 1000
	)
)
summary(fit_mcmc)

Citation

If you use DAST, please cite:

@misc{ripstein2026spatiotemporal,
  title         = {Spatio-Temporal Disaggregation with Changing Areal Boundaries},
  author        = {Ripstein, Noah and Brown, Patrick and Stafford, Jamie},
  year          = {2026},
  eprint        = {2606.25074},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2606.25074}
}