Set thresholds of population density for urbanicity classifications within each region of the given type

Usage

setThresholdsByRegion(pop.mat, poppr, region.type = "area")

Arguments

pop.mat

pixellated population density data frame with variables region.type and pop

poppr

A table with population totals by region of the given type (e.g. poppa or poppsub from makePopIntegrationTab)

region.type

The variable name from poppr giving the region names. Defaults to "area"

Value

A list of region names and their urbanicity thresholds in population density

Details

Thresholds are set based on that region's percent population urban. Intended as a helper function of makePopIntegrationTab.

See also

makePopIntegrationTab

Author

John Paige

Examples

if (FALSE) { # \dontrun{
data(kenyaPopulationData)

#' # download Kenya GADM shapefiles from SUMMERdata github repository
githubURL <- "https://github.com/paigejo/SUMMERdata/blob/main/data/kenyaMaps.rda?raw=true"
tempDirectory = "~/"
mapsFilename = paste0(tempDirectory, "/kenyaMaps.rda")
if(!file.exists(mapsFilename)) {
  download.file(githubURL,mapsFilename)
}

# load it in
out = load(mapsFilename)
out
kenyaMesh <- fmesher::fm_as_fm(kenyaMesh)
adm1@data$NAME_1 = as.character(adm1@data$NAME_1)
adm1@data$NAME_1[adm1@data$NAME_1 == "Trans Nzoia"] = "Trans-Nzoia"
adm1@data$NAME_1[adm1@data$NAME_1 == "Elgeyo-Marakwet"] = "Elgeyo Marakwet"
adm2@data$NAME_1 = as.character(adm2@data$NAME_1)
adm2@data$NAME_1[adm2@data$NAME_1 == "Trans Nzoia"] = "Trans-Nzoia"
adm2@data$NAME_1[adm2@data$NAME_1 == "Elgeyo-Marakwet"] = "Elgeyo Marakwet"

# some Admin-2 areas have the same name
adm2@data$NAME_2 = as.character(adm2@data$NAME_2)
adm2@data$NAME_2[(adm2@data$NAME_1 == "Bungoma") & 
  (adm2@data$NAME_2 == "Lugari")] = "Lugari, Bungoma"
adm2@data$NAME_2[(adm2@data$NAME_1 == "Kakamega") & 
  (adm2@data$NAME_2 == "Lugari")] = "Lugari, Kakamega"
adm2@data$NAME_2[(adm2@data$NAME_1 == "Meru") & 
  (adm2@data$NAME_2 == "Igembe South")] = "Igembe South, Meru"
adm2@data$NAME_2[(adm2@data$NAME_1 == "Tharaka-Nithi") & 
  (adm2@data$NAME_2 == "Igembe South")] = "Igembe South, Tharaka-Nithi"

# The spatial area of unknown 8 is so small, it causes problems unless 
# its removed or unioned with another subarea. Union it with neighboring 
# Kakeguria:
newadm2 = adm2
unknown8I = which(newadm2$NAME_2 == "unknown 8")
newadm2$NAME_2[newadm2$NAME_2 %in% c("unknown 8", "Kapenguria")] <- "Kapenguria + unknown 8"
admin2.IDs <- newadm2$NAME_2

newadm2@data = cbind(newadm2@data, NAME_2OLD = newadm2@data$NAME_2)
newadm2@data$NAME_2OLD = newadm2@data$NAME_2
newadm2@data$NAME_2 = admin2.IDs
newadm2$NAME_2 = admin2.IDs
temp <- terra::aggregate(as(newadm2, "SpatVector"), by="NAME_2")

library(sf)
temp <- sf::st_as_sf(temp)
temp <- sf::as_Spatial(temp)

tempData = newadm2@data[-unknown8I,]
tempData = tempData[order(tempData$NAME_2),]
newadm2 <- sp::SpatialPolygonsDataFrame(temp, tempData, match.ID = F)
adm2 = newadm2

# download 2014 Kenya population density TIF file

githubURL <- paste0("https://github.com/paigejo/SUMMERdata/blob/main/data/", 
                    "Kenya2014Pop/worldpop_total_1y_2014_00_00.tif?raw=true")
popTIFFilename = paste0(tempDirectory, "/worldpop_total_1y_2014_00_00.tif")
if(!file.exists(popTIFFilename)) {
  download.file(githubURL,popTIFFilename)
}

# load it in
pop = terra::rast(popTIFFilename)

east.lim = c(-110.6405, 832.4544)
north.lim = c(-555.1739, 608.7130)

require(fields)

data(kenyaPopulationData)

# Now generate a general population integration table at 5km resolution, 
# based on subarea (Admin-2) x urban/rural population totals. This takes 
# ~1 minute
pop.matKenya <- makePopIntegrationTab(
  km.res=5, pop=pop, domain.map.dat=adm0,
  east.lim=east.lim, north.lim=north.lim, map.projection=projKenya,
  poppa = poppaKenya, poppsub=poppsubKenya, 
  area.map.dat = adm1, subarea.map.dat = adm2,
  areaNameVar = "NAME_1", subareaNameVar="NAME_2")

out = setThresholdsByRegion(pop.matKenya, poppaKenya)
out

out = setThresholdsByRegion(pop.matKenya, poppsubKenya, region.type="subarea")
out
} # }