Get building polygons from OpenStreetMap — get_buildings

Download and process OpenStreetMap (OSM) building polygons within a specified geographical place, from the Geofabrik database. It is a wrapper around functions in the package osmextract, and processes the downloaded files for subsequent analyses. Refer to package osmextract for more details and options for input arguments when downloading the data.

get_buildings_osm(
  place,
  date = NULL,
  dir_raw = osmextract::oe_download_directory(),
  filename = NULL,
  driver = "GeoJSON",
  delete_dsn = TRUE,
  append = NA,
  ...
)

Arguments

place: sf object (with projected coordinate reference system). Geographical area to match with the (.osm.pbf) file in the data archive. Argument passed to osmextract::oe_match().
date: Date of OSM data snapshot to download. Object of class "Date" in format %Y-%m-%d. Refer to https://download.geofabrik.de for the specific dates available. Defaults to NULL (download the latest available data).
dir_raw: character. Directory to download the raw unprocessed OSM data. Passed to argument download_directory in osmextract::oe_read().
filename: character (optional). File path to export output data.
driver: character (optional). Name of driver used to export output data, passed to sf::st_write(). Defaults to "GeoJSON".
delete_dsn: logical (optional). Passed to sf::st_write().
append: defaults to NA, which raises an error if a layer exists. Passed to sf::st_write().
...: Other arguments passed to osmextract::oe_read().

Value

The processed building polygons (sf object).

Details

Data is filtered by key-value attributes, where building: is not NULL. The column levels is derived from building:levels; values were set to 1 if the extracted value is empty or NA, and set to NA if ≤ 0 (i.e. underground); values were then rounded up to the nearest whole number. The column area_m2 represents the building footprint area, and floorarea_m2 is calculated by multiplying the area_m2 by the number of levels.

Examples

if (FALSE) {
data(pop_sgp)
pop_sgp <- sf::st_transform(pop_sgp, sf::st_crs(32648)) # transform to projected crs

# 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()

# run function
get_buildings_osm(place = city_boundaries,
                  date = as.Date('2021-01-01'),
                  filename = 'buildings_osm-polygons_2021-01-01.geojson')
}