This package is published on CRAN. Install it with:
I have tried to keep dependencies to a minimum. The package requires
terra for loading and merging geospatial data. If these are not already installed on your machine, the
install_github line will ask to install them. We also use the
bcmaps package in this vignette to define a study region (but it’s not a requirement of the package).
library(sf) #> Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1 library(terra) #> terra version 1.4.22 library(bcmaps) #> The bcmapsdata package is no longer required to be installed for bcmaps to function. #> Layers are now cached as needed to 'C:\Users\deank\AppData\Local/R/cache/R/bcmaps' using the bcdata package. library(rasterbc)
rasterbc is a data-retrieval tool. Start by setting a storage directory for the raster layers
# replace 'C:/rasterbc_data' with your own path datadir_bc('C:/rasterbc_data', quiet=TRUE)
datadir_bc will set the data directory to the path provided in its first argument. Setting the first argument to
NA (or failing to call
getdata_bc) will cause the package to select a temporary directory (
base::tempdir) that is emptied after your R session ends. We recommended to pick a more permanent location, as in the example above, so that files only need to be downloaded once.
quiet=FALSE (the default), the function will ask users to confirm that
rasterbc should write files to the supplied directory, and warn if this directory contains any existing files/folders. Note that if the storage directory has existing files with names matching those fetched by the
rasterbc package, those data will be overwritten by calls of the form
getdata_bc(..., force.dl=TRUE) (the default is
In future sessions, users can set
quiet=TRUE to skip the interactive prompt and suppress warnings about existing data.
This path string is stored as an R option. View it using:
datadir_bc() #> current data storage path: C:/rasterbc_data
Depending on the geographical extent of interest and the number different layers requested, the storage demands can be high. For example, if every layer is downloaded, then around 30 GB of space is needed. Make sure you have selected a drive with enough free space for your project.
To demonstrate this package we’ll need a polygon covering a (relatively) small geographical extent in BC. Start by loading the bcmaps package and grabbing the polygons for the BC provincial boundary and the Central Okanagan Regional District
# define and load the geometry = 'Regional District of Central Okanagan' example.name = bc_bound() bc.bound.sf #> bc_bound_hres was updated on 2021-11-16 = regional_districts() districts.sf = districts.sf[districts.sf$ADMIN_AREA_NAME==example.name, ] example.sf # plot against map of BC = findblocks_bc(type='sfc') blocks plot(st_geometry(blocks), main=example.name, border='red') plot(st_geometry(bc.bound.sf), add=TRUE, col=adjustcolor('blue', alpha.f=0.2)) plot(st_geometry(example.sf), add=TRUE, col=adjustcolor('yellow', alpha.f=0.5)) text(st_coordinates(st_centroid(st_geometry(blocks))), labels=blocks$NTS_SNRC, cex=0.5)
The Okanagan polygon is shown in yellow, against a red grid that partitions the geographic extent of the province into 89 smaller regions, called mapsheets (here I am calling them “blocks”). This is the NTS/SNRC grid used by Natural Resources Canada for their topographic maps, with each mapsheet identified by a unique number-letter code.
rasterbc uses this grid to package data into blocks for distribution. It is lazy-loaded as the
sf object returned by
findblocks_bc(type='sfc'), which we copied to variable
blocks in the chunk above
print(blocks) #> Simple feature collection with 89 features and 1 field #> Geometry type: POLYGON #> Dimension: XY #> Bounding box: xmin: 199960.5 ymin: 331658 xmax: 1874986 ymax: 1745737 #> Projected CRS: NAD83 / BC Albers #> First 10 features: #> NTS_SNRC geometry #> 1 092B POLYGON ((1299175 340112.5,... #> 2 092C POLYGON ((1149647 333772, 1... #> 3 092E POLYGON ((854708.4 444669.3... #> 4 092F POLYGON ((1001221 442634.1,... #> 5 092G POLYGON ((1147733 444738.3,... #> 6 092H POLYGON ((1294127 450980.4,... #> 7 083C POLYGON ((1548365 807589.3,... #> 8 083D POLYGON ((1411944 794023.1,... #> 9 083E POLYGON ((1402477 905139.5,... #> 10 082E POLYGON ((1440286 461355.4,...
Let’s download Canada’s 1:250,000 digital elevation model (CDEM) layers corresponding to the yellow polygon. For the full BC extent, these rasters would occupy around 1.2GB of space. But we only want the smaller extent corresponding to the polygon. There are three blocks (totalling about 20 MB) which overlap with our region of interest
findblocks_bc(example.sf) #>  "092H" "082E" "082L"
fetch them using the command:
getdata_bc(geo=example.sf, collection='dem', varname='dem')
You should see progress bars for a series of three downloads, and once finished, the paths of the downloaded files are printed to the console. Note that if a block has been downloaded already (eg. by a
getdata_bc call with a different
geo argument), the existing copy will be detected, and the download skipped. eg. repeat the call…
getdata_bc(geo=example.sf, collection='dem', varname='dem') #> all 3 block(s) found in local data storage. Nothing to download #>  "C:/rasterbc_data/dem/blocks/dem_092H.tif" #>  "C:/rasterbc_data/dem/blocks/dem_082E.tif" #>  "C:/rasterbc_data/dem/blocks/dem_082L.tif"
… and nothing is downloaded, because the data are there already. Verify by loading one of the files as
= file.path(datadir_bc(), 'dem/blocks/dem_092H.tif') tif.path #> current data storage path: C:/rasterbc_data = terra::rast(tif.path) example.raster print(example.raster) #> class : SpatRaster #> dimensions : 1212, 1525, 1 (nrow, ncol, nlyr) #> resolution : 100, 100 (x, y) #> extent : 1286588, 1439088, 450888, 572088 (xmin, xmax, ymin, ymax) #> coord. ref. : +proj=aea +lat_0=45 +lon_0=-126 +lat_1=50 +lat_2=58.5 +x_0=1000000 +y_0=0 +datum=NAD83 +units=m +no_defs #> source : dem_092H.tif #> name : dem_092H #> min value : 7.653875 #> max value : 2608.961 plot(example.raster, main='elevation (metres)')
To display the elevation data for the entire district, we need to combine the three blocks downloaded earlier. This can be done using
opendata_bc, which loads all required blocks, merges them into a single layer, crops and masks as needed, and then loads into memory the returned
= opendata_bc(example.sf, collection='dem', varname='dem') example.tif #> creating mosaic of 3 block(s) #> clipping layer...masking layer...done print(example.tif) #> class : SpatRaster #> dimensions : 622, 893, 1 (nrow, ncol, nlyr) #> resolution : 100, 100 (x, y) #> extent : 1426588, 1515888, 539188, 601388 (xmin, xmax, ymin, ymax) #> coord. ref. : +proj=aea +lat_0=45 +lon_0=-126 +lat_1=50 +lat_2=58.5 +x_0=1000000 +y_0=0 +datum=NAD83 +units=m +no_defs #> source : memory #> name : dem #> min value : 340.43 #> max value : 2153.29 plot(example.tif, main=paste(example.name, ': elevation')) plot(st_geometry(example.sf), add=TRUE) plot(st_geometry(blocks), add=TRUE, border='red')
If you’re opening the downloaded data right away, you can skip the
getdata_bc call. The argument
dl (by default
opendata_bc will cause it to call
getdata_bc automatically to download any missing blocks.
Any simple features object of class
sfc can be used for the argument
geo, provided its geometry intersects with the provincial boundary of BC. The intended usage is for the user to delineate their region of interest as a
(MULTI)POLYGON object (here,
example.sf is a
MULTIPOLYGON). Geometries of other classes (such
SpatialPolygons, as defined by
sp; or data frames containing coordinates of vertices) can often be coerced to
sf using a command like
sf::st_as_sf(other_geometry_class_object). Alternatively, users can directly download individual blocks by specifying their NTS/SNRC codes, eg. the next chunk plots the DEM again, using the codes:
= findblocks_bc(example.sf) example.codes = opendata_bc(example.codes, collection='dem', varname='dem') example.tif #> creating mosaic of 3 block(s) #> done plot(example.tif, main=paste('NTS/SNRC mapsheets ', paste(example.codes, collapse=', '), ': elevation')) plot(st_geometry(blocks), add=TRUE, border='red') plot(st_geometry(example.sf), add=TRUE) text(st_coordinates(st_centroid(st_geometry(blocks))), labels=blocks$NTS_SNRC, cex=0.5)