Distribute population data per census block across inhabitable land as defined by land_relative_density.
Raster data provided should be a snapshot of the population for a single time period (e.g. a year),
created with other package functions (e.g. rasterise_pop(), rasterise_buildings()).
If there are multiple years present in the intermediate datasets, the user will have to extract the relevant list element for
the single year of interest (see examples). All input data should have a similar projected coordinate reference system
specific to the target area.
pop_dasymap(
pop_polygons,
pop_perblock_count,
pop_perblock_density,
land_relative_density,
filename = NULL,
overwrite = TRUE,
...
)sf polygons of the population data for a single time period.
May be extracted from the pop_polygons list element generated by rasterise_pop().
Raster of population count per census block for a single time period.
May be extracted from the pop_count_rasters list element generated by rasterise_pop().
Raster of population density per census block for a single time period.
May be extracted from the pop_density_rasters list element generated by rasterise_pop().
Raster of the relative density (e.g. suitability, habitability) of land for a single time period.
May be extracted from results generated by rasterise_buildings() or rasterise_landuse().
character (optional). Export output raster to disk.
logical. Argument passed to terra::writeRaster(). If TRUE, filename is overwritten.
Other arguments passed to terra::writeRaster().
Raster of population density, based on relative density values of the land defined in land_relative_density.
if (FALSE) {
data(pop_sgp) # population census block polygons
data(landuse_sgp) # land use polygons
# transform to projected crs
pop_sgp <- sf::st_transform(pop_sgp, sf::st_crs(32648))
landuse_sgp <- sf::st_transform(landuse_sgp, sf::st_crs(32648))
# merge all census blocks for chosen year (2020) into single multi-polygon
# function requires that polygons are merged
city_boundaries <- pop_sgp %>%
dplyr::filter(year == 2020) %>%
sf::st_union() %>%
sf::st_as_sf() %>%
smoothr::fill_holes(threshold = units::set_units(1, 'km^2')) %>% # clean up
smoothr::drop_crumbs(threshold = units::set_units(1, 'km^2')) %>%
sf::st_make_valid()
buildings <- get_buildings_osm(place = city_boundaries,
date = as.Date('2021-01-01')) %>%
mutate(year = 2020)
# rasterise population, landuse & buildings
pop_rasters <- rasterise_pop(pop_sgp,
census_block = "subzone_n",
pop_count = "pop_count")
landuse_rasters <- rasterise_landuse(landuse_sgp,
land_use = 'lu_desc',
subset = c('1' = 'RESIDENTIAL',
'2' = 'COMMERCIAL & RESIDENTIAL',
'3' = 'RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY',
'4' = 'RESIDENTIAL / INSTITUTION'),
sf_pop = pop_sgp,
match_landuse_pop = 'recent')
buildings_rasters <- rasterise_buildings(buildings,
proxy_pop_density = 'levels',
year = 'year',
sf_pop = pop_sgp,
sf_landuse = landuse_sgp,
match_buildings_pop = 'closest')
# finally, perform dasymetric mapping on selected year (2020)
popdens_raster <- pop_dasymap(pop_polygons = pop_rasters$pop_polygons[[2]],
pop_perblock_count = pop_rasters$pop_count[[2]],
pop_perblock_density = pop_rasters$pop_density[[2]],
land_relative_density = buildings_rasters[[2]],
filename = 'buildings_popdensity.tif',
wopt = list(gdal=c('COMPRESS=LZW')))
}