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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}
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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