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The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results.
First, standard "top-down" gridded population datasets are generated by models that either directly disaggregate administrative population counts to grid cells using satellite imagery (e.g. land cover and nighttime lights) and other spatial data (e.g. road and building locations), or non-uniformly disaggregate population counts using complex modeling approaches.
Because grid cells are approximately uniform in size and shape within a country, GridSample also allows for a population sample to be supplemented with a spatial oversample in remote areas which is attractive if survey results will be used to generate small area estimates or make interpolated surfaces [44] (Fig. 1).
GridSample requires that all input datasets are converted to raster format using the same grid cell dimensions as the population dataset.
The input raster datasets, plus a number of survey parameters, are used to randomly sample grid cells with probability proportionate to estimated population (PPES) size in a first step, and then optionally grow PSUs around selected seed cells until a minimum population threshold is achieved in a second step (Fig. 2).
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