DAST

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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:

  

``` r

# install.packages("remotes")

remotes::install_github("nripstein/DAST")

```

  

or the development branch from

``` r

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

  

```r

# 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](https://arxiv.org/abs/2101.04468).              | Default option for fast approximate fully Bayesian inference.                    |
| `TMB`  | Laplace approximation through [Template Model Builder](https://doi.org/10.18637/jss.v070.i05). | Fastest option, using Empirical Bayes instead of full Bayes.                                  |
| `MCMC` | [NUTS](https://jmlr.org/papers/v15/hoffman14a.html) algorithm implimented in [tmbstan](https://doi.org/10.1371/journal.pone.0197954). | 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.

  

```r
# 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:

```bibtex
@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}
}
```