Polygonise population density raster to population count per (building) polygon

Convert raster of population density (e.g. from pop_dasymap() output) to population count per polygon (adjacent polygons with similar pixel values are merged). Thus, if rasters supplied to pop_dasymap() are at a spatial resolution small enough to delineate individual buildings, conversion to polygons using this function would reflect the population count per building. Input data should have a projected coordinate reference system specific to the target area.

pop_density_polygonise(
  input_raster,
  write = FALSE,
  dsn,
  driver = "GeoJSON",
  overwrite = TRUE,
  delete_dsn = TRUE,
  ...
)

Arguments

input_raster

Population density raster as a SpatRaster object (terra::rast()). Output from pop_dasymap() may be used.

write

Whether or not to export the output. Defaults to FALSE.

dsn

Argument passed to sf::st_write().

driver

character. Argument passed to sf::st_write(). Defaults to 'GeoJSON'.

overwrite

logical. Argument passed to sf::st_write(). Defaults to TRUE.

delete_dsn

Argument passed to sf::st_write(). Defaults to TRUE.

...

Other arguments passed to sf::st_write().

Value

Building sf polygons with column popcount.

Examples

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')) %>%
   dplyr::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')


# 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')))


# finally, convert to population count per building polygon
pop_density_polygonise(input_raster = popdens_raster,
                       write = TRUE,
                       dsn = 'buildings_popcount.geojson')
}