R/apply_pop_density.R
In wpgp/popRF: Random Forest-Informed Population Disaggregation
Defines functions
apply_pop_density_constrained
apply_pop_density
#' Apply population density.
#'
#' @details Apply population density to a final RF prediction
#' (RF_pred +L1_pop)/RF_pred_ZS_sum.
#'
#' @rdname apply_pop_density
#' @param pop the name of the file which the administrative ID and the population
#' values are to be read from. The file should contain two columns
#' comma-separated with the value of administrative ID and population
#' without columns names. If it does not contain an absolute path, the
#' file name is relative to the current working directory.
#' @param mastergrid_filename census mask Path FileName
#' @param output_dir Path to the folder to save the outputs.
#' @param cores is a integer. Number of cores to use when executing the function,
#' which defaults to 4. If set to 0 or NULL max number of cores will be
#' used based on as many processors as the hardware and RAM allow.
#' @param rfg.countries.tag character of tag
#' @param quant logical. If FALSE then quant will not be calculated
#' @param blocks number of blocks suggesting for processing raster file.
#' @param verbose is logical. TRUE or FALSE: flag indicating whether to print
#' intermediate output from the function on the console, which might be
#' helpful for model debugging. Default is \code{verbose} = TRUE.
#' @param log is logical. TRUE or FALSE: flag indicating whether to print intermediate
#' output from the function on the log.txt file.
#' Default is \code{log} = FALSE.
#' @importFrom raster getValues writeRaster values
#' @importFrom plyr join
#' @importFrom utils stack
#' @return raster objects
#' @noRd
apply_pop_density <- function(pop,
mastergrid_filename,
output_dir,
cores=NULL,
rfg.countries.tag,
quant = TRUE,
blocks=NULL,
verbose=TRUE,
log=FALSE) {
silent <- if (verbose) FALSE else TRUE
# rasterising pop table
rfg.rst.pop.census.tif <- file.path(output_dir,
paste0("pop_census_mask_",
rfg.countries.tag, ".tif"))
rfg.rst.zonal.stats.rf.pred.tif<- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag,
"_ZS_sum.tif"))
rfg.predict.density.rf.pred <- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag, ".tif"))
rst.predict.density.rf.pred <- raster(rfg.predict.density.rf.pred)
df <- get_pop_census_all(pop)
zonal_raster <- raster(mastergrid_filename)
if (is.null(cores) | cores < 2 ){
v <- data.frame( raster::getValues(zonal_raster) )
colnames(v) <- c("v1")
colnames(df) <- c("v1","v2")
out <- plyr::join(v, df, type="left",by = "v1")[-1]
out.rst.pop.census.raster <- zonal_raster
raster::values(out.rst.pop.census.raster) <- out[[1]]
rst.pop.census <- raster::writeRaster(out.rst.pop.census.raster,
filename=rfg.rst.pop.census.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.rst.pop.census.raster)
zonal.stats.rf.pred.sum <- zonal(rst.predict.density.rf.pred,
zonal_raster,
fun="sum")
## Adjust column names:
colnames(zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
zonal.stats.rf.pred.sum <- zonal.stats.rf.pred.sum[sort.list(zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
zonal.stats.rf.pred.sum <- as.data.frame(zonal.stats.rf.pred.sum[zonal.stats.rf.pred.sum[,1] != 0, ])
colnames(zonal.stats.rf.pred.sum) <- c("v1","v2")
out.zonal.stats.rf.pred.sum <- plyr::join(v, zonal.stats.rf.pred.sum,
type="left",by = "v1")[-1]
out.zonal.stats.rf.pred.sum.raster <- zonal_raster
raster::values(out.zonal.stats.rf.pred.sum.raster) <- out.zonal.stats.rf.pred.sum[[1]]
rst.zonal.stats.rf.pred <- raster::writeRaster(out.zonal.stats.rf.pred.sum.raster,
filename=rfg.rst.zonal.stats.rf.pred.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.zonal.stats.rf.pred.sum.raster)
}else{
colnames(df) <- c("ADMINID", "ADMINPOP")
log_info("MSG", paste0("Rasterizing input population data."),
verbose=verbose, log=log)
rst.pop.census <- rasterize_parallel(zonal_raster,
df,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.pop.census.tif,
overwrite=TRUE,
silent=silent)
log_info("MSG",
paste0("Computing zonal statistics of final RF prediction layer."),
verbose=verbose, log=log)
out.zonal.stats.rf.pred.sum <- calculate_zs_parallel(rst.predict.density.rf.pred,
zonal_raster,
fun="sum",
cores=cores,
blocks=blocks,
silent=silent)
## Adjust column names:
colnames(out.zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
out.zonal.stats.rf.pred.sum <- out.zonal.stats.rf.pred.sum[sort.list(out.zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
out.zonal.stats.rf.pred.sum <- as.data.frame(out.zonal.stats.rf.pred.sum[out.zonal.stats.rf.pred.sum[,1] != 0, ])
log_info("MSG", paste0("Rasterizing the results of zonal statistics."),
verbose=verbose, log=log)
rst.zonal.stats.rf.pred <- rasterize_parallel(zonal_raster,
out.zonal.stats.rf.pred.sum,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.zonal.stats.rf.pred.tif,
overwrite=TRUE,
silent=silent)
rm(out.zonal.stats.rf.pred.sum)
}
rfg.predict.density.rf.pred.final <- file.path(output_dir,
paste0("ppp_",rfg.countries.tag,
".tif"))
r_calc <- (rst.predict.density.rf.pred * rst.pop.census)/rst.zonal.stats.rf.pred
writeRaster(r_calc,
filename=rfg.predict.density.rf.pred.final,
format="GTiff",
overwrite=TRUE,
NAflag=-99999,
datatype='FLT4S',
options=c("COMPRESS=LZW")
)
return(r_calc)
}
#' Apply population density constrained.
#'
#' @details Apply population density to a final constrained RF prediction
#' (RF_pred +L1_pop)/RF_pred_ZS_sum.
#'
#' @rdname apply_pop_density_constrained
#' @param pop the name of the file which the administrative ID and the
#' population values are to be read from. The file should contain two
#' columns comma-separated with the value of administrative ID and
#' population without columns names. If it does not contain an absolute
#' path, the file name is relative to the current working directory.
#' @param mastergrid_filename census mask Path FileName
#' @param output_dir Path to the folder to save the outputs.
#' @param cores is a integer. Number of cores to use when executing the function,
#' which defaults to 4. If set to 0 or NULL max number of cores will be
#' used based on as many processors as the hardware and RAM allow.
#' @param rfg.countries.tag character of tag
#' @param blocks number of blocks sugesting for processing raster file.
#' @param verbose is logical. TRUE or FALSE: flag indicating whether to print
#' intermediate output from the function on the console, which might be
#' helpful for model debugging. Default is \code{verbose} = TRUE.
#' @param log is logical. TRUE or FALSE: flag indicating whether to print
#' intermediate output from the function on the log.txt file.
#' Default is \code{log} = FALSE.
#' @importFrom raster getValues writeRaster values
#' @importFrom plyr join
#' @importFrom utils stack
#' @return raster objects
#' @noRd
apply_pop_density_constrained <- function(pop,
mastergrid_filename,
output_dir,
cores=NULL,
rfg.countries.tag,
blocks=NULL,
verbose=TRUE,
log=FALSE) {
silent <- if (verbose) FALSE else TRUE
# rasterising pop table
rfg.rst.pop.census.tif <- file.path(output_dir,
paste0("pop_census_mask_",
rfg.countries.tag, "_const.tif"))
rfg.rst.zonal.stats.rf.pred.tif<- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag,
"_ZS_sum_const.tif"))
rfg.predict.density.rf.pred <- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag,
"_const.tif"))
rst.predict.density.rf.pred <- raster(rfg.predict.density.rf.pred)
df <- get_pop_census_all(pop)
zonal_raster <- raster(mastergrid_filename)
if (is.null(cores) | cores < 2 ){
v <- data.frame( raster::getValues(zonal_raster) )
colnames(v) <- c("v1")
colnames(df) <- c("v1","v2")
out <- plyr::join(v, df, type="left",by = "v1")[-1]
out.rst.pop.census.raster <- zonal_raster
raster::values(out.rst.pop.census.raster) <- out[[1]]
rst.pop.census <- raster::writeRaster(out.rst.pop.census.raster,
filename=rfg.rst.pop.census.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.rst.pop.census.raster)
zonal.stats.rf.pred.sum <- zonal(rst.predict.density.rf.pred,
zonal_raster,
fun="sum")
## Adjust column names:
colnames(zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
zonal.stats.rf.pred.sum <- zonal.stats.rf.pred.sum[sort.list(zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
zonal.stats.rf.pred.sum <- as.data.frame(zonal.stats.rf.pred.sum[zonal.stats.rf.pred.sum[,1] != 0, ])
colnames(zonal.stats.rf.pred.sum) <- c("v1","v2")
out.zonal.stats.rf.pred.sum <- plyr::join(v, zonal.stats.rf.pred.sum,
type="left",by = "v1")[-1]
out.zonal.stats.rf.pred.sum.raster <- zonal_raster
raster::values(out.zonal.stats.rf.pred.sum.raster) <- out.zonal.stats.rf.pred.sum[[1]]
rst.zonal.stats.rf.pred <- raster::writeRaster(out.zonal.stats.rf.pred.sum.raster,
filename=rfg.rst.zonal.stats.rf.pred.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.zonal.stats.rf.pred.sum.raster)
}else{
colnames(df) <- c("ADMINID", "ADMINPOP")
log_info("MSG",
paste0("Rasterizing input population data using constarined mastegrid"),
verbose=verbose, log=log)
rst.pop.census <- rasterize_parallel(zonal_raster,
df,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.pop.census.tif,
overwrite=TRUE,
silent=silent)
log_info("MSG",
paste0("Computing zonal statistics of constarined final RF prediction layer."),
verbose=verbose, log=log)
out.zonal.stats.rf.pred.sum <- calculate_zs_parallel(rst.predict.density.rf.pred,
zonal_raster,
fun="sum",
cores=cores,
blocks=blocks,
silent=silent)
## Adjust column names:
colnames(out.zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
out.zonal.stats.rf.pred.sum <- out.zonal.stats.rf.pred.sum[sort.list(out.zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
out.zonal.stats.rf.pred.sum <- as.data.frame(out.zonal.stats.rf.pred.sum[out.zonal.stats.rf.pred.sum[,1] != 0, ])
log_info("MSG",
paste0("Rasterizing the results of zonal statistics."),
verbose=verbose, log=log)
rst.zonal.stats.rf.pred <- rasterize_parallel(zonal_raster,
out.zonal.stats.rf.pred.sum,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.zonal.stats.rf.pred.tif,
overwrite=TRUE,
silent=silent)
rm(out.zonal.stats.rf.pred.sum)
}
rfg.predict.density.rf.pred.final <- file.path(output_dir,
paste0("ppp_",
rfg.countries.tag,
"_const.tif"))
r_calc <- (rst.predict.density.rf.pred * rst.pop.census)/rst.zonal.stats.rf.pred
writeRaster(r_calc,
filename=rfg.predict.density.rf.pred.final,
format="GTiff",
overwrite=TRUE,
NAflag=-99999,
datatype='FLT4S',
options=c("COMPRESS=LZW")
)
return(r_calc)
}
wpgp/popRF documentation built on April 27, 2023, 10:13 p.m.
R Package Documentation
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Defines functions apply_pop_density_constrained apply_pop_density
#' Apply population density.
#'
#' @details Apply population density to a final RF prediction
#' (RF_pred +L1_pop)/RF_pred_ZS_sum.
#'
#' @rdname apply_pop_density
#' @param pop the name of the file which the administrative ID and the population
#' values are to be read from. The file should contain two columns
#' comma-separated with the value of administrative ID and population
#' without columns names. If it does not contain an absolute path, the
#' file name is relative to the current working directory.
#' @param mastergrid_filename census mask Path FileName
#' @param output_dir Path to the folder to save the outputs.
#' @param cores is a integer. Number of cores to use when executing the function,
#' which defaults to 4. If set to 0 or NULL max number of cores will be
#' used based on as many processors as the hardware and RAM allow.
#' @param rfg.countries.tag character of tag
#' @param quant logical. If FALSE then quant will not be calculated
#' @param blocks number of blocks suggesting for processing raster file.
#' @param verbose is logical. TRUE or FALSE: flag indicating whether to print
#' intermediate output from the function on the console, which might be
#' helpful for model debugging. Default is \code{verbose} = TRUE.
#' @param log is logical. TRUE or FALSE: flag indicating whether to print intermediate
#' output from the function on the log.txt file.
#' Default is \code{log} = FALSE.
#' @importFrom raster getValues writeRaster values
#' @importFrom plyr join
#' @importFrom utils stack
#' @return raster objects
#' @noRd
apply_pop_density <- function(pop,
mastergrid_filename,
output_dir,
cores=NULL,
rfg.countries.tag,
quant = TRUE,
blocks=NULL,
verbose=TRUE,
log=FALSE) {
silent <- if (verbose) FALSE else TRUE
# rasterising pop table
rfg.rst.pop.census.tif <- file.path(output_dir,
paste0("pop_census_mask_",
rfg.countries.tag, ".tif"))
rfg.rst.zonal.stats.rf.pred.tif<- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag,
"_ZS_sum.tif"))
rfg.predict.density.rf.pred <- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag, ".tif"))
rst.predict.density.rf.pred <- raster(rfg.predict.density.rf.pred)
df <- get_pop_census_all(pop)
zonal_raster <- raster(mastergrid_filename)
if (is.null(cores) | cores < 2 ){
v <- data.frame( raster::getValues(zonal_raster) )
colnames(v) <- c("v1")
colnames(df) <- c("v1","v2")
out <- plyr::join(v, df, type="left",by = "v1")[-1]
out.rst.pop.census.raster <- zonal_raster
raster::values(out.rst.pop.census.raster) <- out[[1]]
rst.pop.census <- raster::writeRaster(out.rst.pop.census.raster,
filename=rfg.rst.pop.census.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.rst.pop.census.raster)
zonal.stats.rf.pred.sum <- zonal(rst.predict.density.rf.pred,
zonal_raster,
fun="sum")
## Adjust column names:
colnames(zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
zonal.stats.rf.pred.sum <- zonal.stats.rf.pred.sum[sort.list(zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
zonal.stats.rf.pred.sum <- as.data.frame(zonal.stats.rf.pred.sum[zonal.stats.rf.pred.sum[,1] != 0, ])
colnames(zonal.stats.rf.pred.sum) <- c("v1","v2")
out.zonal.stats.rf.pred.sum <- plyr::join(v, zonal.stats.rf.pred.sum,
type="left",by = "v1")[-1]
out.zonal.stats.rf.pred.sum.raster <- zonal_raster
raster::values(out.zonal.stats.rf.pred.sum.raster) <- out.zonal.stats.rf.pred.sum[[1]]
rst.zonal.stats.rf.pred <- raster::writeRaster(out.zonal.stats.rf.pred.sum.raster,
filename=rfg.rst.zonal.stats.rf.pred.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.zonal.stats.rf.pred.sum.raster)
}else{
colnames(df) <- c("ADMINID", "ADMINPOP")
log_info("MSG", paste0("Rasterizing input population data."),
verbose=verbose, log=log)
rst.pop.census <- rasterize_parallel(zonal_raster,
df,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.pop.census.tif,
overwrite=TRUE,
silent=silent)
log_info("MSG",
paste0("Computing zonal statistics of final RF prediction layer."),
verbose=verbose, log=log)
out.zonal.stats.rf.pred.sum <- calculate_zs_parallel(rst.predict.density.rf.pred,
zonal_raster,
fun="sum",
cores=cores,
blocks=blocks,
silent=silent)
## Adjust column names:
colnames(out.zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
out.zonal.stats.rf.pred.sum <- out.zonal.stats.rf.pred.sum[sort.list(out.zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
out.zonal.stats.rf.pred.sum <- as.data.frame(out.zonal.stats.rf.pred.sum[out.zonal.stats.rf.pred.sum[,1] != 0, ])
log_info("MSG", paste0("Rasterizing the results of zonal statistics."),
verbose=verbose, log=log)
rst.zonal.stats.rf.pred <- rasterize_parallel(zonal_raster,
out.zonal.stats.rf.pred.sum,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.zonal.stats.rf.pred.tif,
overwrite=TRUE,
silent=silent)
rm(out.zonal.stats.rf.pred.sum)
}
rfg.predict.density.rf.pred.final <- file.path(output_dir,
paste0("ppp_",rfg.countries.tag,
".tif"))
r_calc <- (rst.predict.density.rf.pred * rst.pop.census)/rst.zonal.stats.rf.pred
writeRaster(r_calc,
filename=rfg.predict.density.rf.pred.final,
format="GTiff",
overwrite=TRUE,
NAflag=-99999,
datatype='FLT4S',
options=c("COMPRESS=LZW")
)
return(r_calc)
}
#' Apply population density constrained.
#'
#' @details Apply population density to a final constrained RF prediction
#' (RF_pred +L1_pop)/RF_pred_ZS_sum.
#'
#' @rdname apply_pop_density_constrained
#' @param pop the name of the file which the administrative ID and the
#' population values are to be read from. The file should contain two
#' columns comma-separated with the value of administrative ID and
#' population without columns names. If it does not contain an absolute
#' path, the file name is relative to the current working directory.
#' @param mastergrid_filename census mask Path FileName
#' @param output_dir Path to the folder to save the outputs.
#' @param cores is a integer. Number of cores to use when executing the function,
#' which defaults to 4. If set to 0 or NULL max number of cores will be
#' used based on as many processors as the hardware and RAM allow.
#' @param rfg.countries.tag character of tag
#' @param blocks number of blocks sugesting for processing raster file.
#' @param verbose is logical. TRUE or FALSE: flag indicating whether to print
#' intermediate output from the function on the console, which might be
#' helpful for model debugging. Default is \code{verbose} = TRUE.
#' @param log is logical. TRUE or FALSE: flag indicating whether to print
#' intermediate output from the function on the log.txt file.
#' Default is \code{log} = FALSE.
#' @importFrom raster getValues writeRaster values
#' @importFrom plyr join
#' @importFrom utils stack
#' @return raster objects
#' @noRd
apply_pop_density_constrained <- function(pop,
mastergrid_filename,
output_dir,
cores=NULL,
rfg.countries.tag,
blocks=NULL,
verbose=TRUE,
log=FALSE) {
silent <- if (verbose) FALSE else TRUE
# rasterising pop table
rfg.rst.pop.census.tif <- file.path(output_dir,
paste0("pop_census_mask_",
rfg.countries.tag, "_const.tif"))
rfg.rst.zonal.stats.rf.pred.tif<- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag,
"_ZS_sum_const.tif"))
rfg.predict.density.rf.pred <- file.path(output_dir,
paste0("predict_density_rf_pred_",
rfg.countries.tag,
"_const.tif"))
rst.predict.density.rf.pred <- raster(rfg.predict.density.rf.pred)
df <- get_pop_census_all(pop)
zonal_raster <- raster(mastergrid_filename)
if (is.null(cores) | cores < 2 ){
v <- data.frame( raster::getValues(zonal_raster) )
colnames(v) <- c("v1")
colnames(df) <- c("v1","v2")
out <- plyr::join(v, df, type="left",by = "v1")[-1]
out.rst.pop.census.raster <- zonal_raster
raster::values(out.rst.pop.census.raster) <- out[[1]]
rst.pop.census <- raster::writeRaster(out.rst.pop.census.raster,
filename=rfg.rst.pop.census.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.rst.pop.census.raster)
zonal.stats.rf.pred.sum <- zonal(rst.predict.density.rf.pred,
zonal_raster,
fun="sum")
## Adjust column names:
colnames(zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
zonal.stats.rf.pred.sum <- zonal.stats.rf.pred.sum[sort.list(zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
zonal.stats.rf.pred.sum <- as.data.frame(zonal.stats.rf.pred.sum[zonal.stats.rf.pred.sum[,1] != 0, ])
colnames(zonal.stats.rf.pred.sum) <- c("v1","v2")
out.zonal.stats.rf.pred.sum <- plyr::join(v, zonal.stats.rf.pred.sum,
type="left",by = "v1")[-1]
out.zonal.stats.rf.pred.sum.raster <- zonal_raster
raster::values(out.zonal.stats.rf.pred.sum.raster) <- out.zonal.stats.rf.pred.sum[[1]]
rst.zonal.stats.rf.pred <- raster::writeRaster(out.zonal.stats.rf.pred.sum.raster,
filename=rfg.rst.zonal.stats.rf.pred.tif,
format="GTiff",
datatype='FLT4S',
overwrite=TRUE,
options=c("COMPRESS=LZW"),
NAflag=-99999)
rm(out.zonal.stats.rf.pred.sum.raster)
}else{
colnames(df) <- c("ADMINID", "ADMINPOP")
log_info("MSG",
paste0("Rasterizing input population data using constarined mastegrid"),
verbose=verbose, log=log)
rst.pop.census <- rasterize_parallel(zonal_raster,
df,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.pop.census.tif,
overwrite=TRUE,
silent=silent)
log_info("MSG",
paste0("Computing zonal statistics of constarined final RF prediction layer."),
verbose=verbose, log=log)
out.zonal.stats.rf.pred.sum <- calculate_zs_parallel(rst.predict.density.rf.pred,
zonal_raster,
fun="sum",
cores=cores,
blocks=blocks,
silent=silent)
## Adjust column names:
colnames(out.zonal.stats.rf.pred.sum) <- c("ADMINID", "sum")
## Sort the stats by Admin ID:
out.zonal.stats.rf.pred.sum <- out.zonal.stats.rf.pred.sum[sort.list(out.zonal.stats.rf.pred.sum[,1]), ]
## Return the zonal stats excluding "Admin ID 0":
out.zonal.stats.rf.pred.sum <- as.data.frame(out.zonal.stats.rf.pred.sum[out.zonal.stats.rf.pred.sum[,1] != 0, ])
log_info("MSG",
paste0("Rasterizing the results of zonal statistics."),
verbose=verbose, log=log)
rst.zonal.stats.rf.pred <- rasterize_parallel(zonal_raster,
out.zonal.stats.rf.pred.sum,
cores=cores,
blocks=blocks,
NAflag=NULL,
datatype=NULL,
filename=rfg.rst.zonal.stats.rf.pred.tif,
overwrite=TRUE,
silent=silent)
rm(out.zonal.stats.rf.pred.sum)
}
rfg.predict.density.rf.pred.final <- file.path(output_dir,
paste0("ppp_",
rfg.countries.tag,
"_const.tif"))
r_calc <- (rst.predict.density.rf.pred * rst.pop.census)/rst.zonal.stats.rf.pred
writeRaster(r_calc,
filename=rfg.predict.density.rf.pred.final,
format="GTiff",
overwrite=TRUE,
NAflag=-99999,
datatype='FLT4S',
options=c("COMPRESS=LZW")
)
return(r_calc)
}
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