jollofR (version 0.6.5)

spray1: spray1: Disaggregates population counts at high-resolution grid cells in the absence population and building counts - for one-level only

Description

This function disaggregates population estimates at grid cell levels using the building counts of each grid cell to first disaggregate the admin unit's total population across the grid cells. Then, each grid cell's total count is further disaggregated into groups of interest using the admin's proportions.

Usage

spray1(df, rdf, class, rclass, output_dir, verbose = TRUE)

Value

A list of data frame objects of the output files including the disaggregated population proportions and population totals along with the corresponding measures of uncertainties (lower and upper bounds of 95-percent credible intervals) for each demographic characteristic. In addition, a file containing the model performance/model fit evaluation metrics is also produced.

Arguments

df

A data frame object containing sample data (often partially observed) on different demographic groups population. It contains the admin's total populatioin count to be disaggregated as well as other key variables as defined within the 'toydata'.

rdf

A gridded data frame object containing key information on the grid cells. Variables include the admin_id which must be identical to the one in the admin level data. It contains GPS coordinates. i.e, longitude (lon) and Latitude (lat) of the grid cell's centroids.

class

These are the categories of the variables of interest. For example, for educational level, it could be 'no education', 'primary education', 'secondary education', 'tertiary education'.

rclass

This is a user-defined names of the files to be saved in the output folder.

output_dir

This is the directory with the name of the output folder where the disaggregated population proportions and population totals are automatically saved.

verbose

Logical. If TRUE, prints progress messages. Default is TRUE.

Examples

# \donttest{
if (requireNamespace("INLA", quietly = TRUE)) {
 library(raster) # load relevant libraries
 library(dplyr)
 library(terra)
 data(toydata) # load toy data

 # run 'cheesepop' admin unit disaggregation function
 result <- cheesepop(df = toydata$admin,output_dir = tempdir())
 class <- class <- names(toydata$admin %>% dplyr::select(starts_with("age_")))
 rclass <- paste0("TOY_population_v1_0_age",1:12)

 # run spray1 grid cell disaggregation function
 result2 <- spray1(df = result$full_data, rdf = toydata$grid, class, rclass, output_dir = tempdir())
 ras2<- rast(paste0(output_dir = tempdir(), "/pop_TOY_population_v1_0_age4.tif"))
 plot(ras2) # visulize of the raster files produced
}
# }