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Get building polygons from OpenStreetMap

Source: R/get_buildings_osm.R
get_buildings_osm.Rd

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.

Usage

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. 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')
}