gs_rasterize

SpatialPolygons* object. Name of input shapefile
layer. Should be a SpatialPolygons object.

input_features

Character. Desired name of output raster layer.

output_raster

Raster* object. Raster with desired characteristics
(resolution, extent) of output raster.

template_raster

logical. If <code>TRUE</code>, any non-zero values will be converted
to one.

binary

character. Name of variable that output raster should inherit.

field

logical. Defines whether to overwrite if
<code>output_raster</code> already exists.

overwrite

character. Desired format of output raster file.

format

This function creates a raster layer that adopts values from a defined field
in a polygon layer, using <code>rasterize</code> from the raster package. This
function also converts values to binary if desired, where all zero values
are recorded as zero, and all non-zero values are recorded as one. This
function also saves the output raster in the working directory.

Multi-stage cluster surveys of households are commonly performed by
governments and programmes to monitor population-level demographic, social,
economic, and health outcomes. Generally, communities are sampled from
subpopulations (strata) in a first stage, and then households are listed and
sampled in a second stage. In this typical two-stage design, sampled
communities are the Primary Sampling Units (PSUs) and households are the
Secondary Sampling Units (SSUs). Census data typically serve as the sample
frame from which PSUs are selected. However, if census data are outdated
inaccurate, or too geographically course, gridded population data (such as
<http://www.worldpop.org.uk>) can be used as a sample frame instead.
GridSample (<doi:10.1186/s12942-017-0098-4>) generates PSUs from
gridded population data according to user-specified complex survey design
characteristics and household sample size. In gridded population sampling,
like census sampling, PSUs are selected within each stratum using a
serpentine sampling method, and can be oversampled in urban or rural areas to
ensure a minimum sample size in each of these important sub-domains.
Furthermore, because grid cells are uniform in size and shape, gridded
population sampling allows for samples to be representative of both the
population and of space, which is not possible with a census sample frame.

gs_rasterize: Rasterize polygon layer

Description

Usage

Arguments

Value