Real world example

A typical use case is working with an existing GRASS project database.
GRASS stores all data in a structured gisdbase consisting of locations and mapsets.

Raster and vector data are fully managed inside this structure.
This example shows how to integrate large external datasets into GRASS.

Downloading census data

We use German 2011 census data as a real-world big data example.
The dataset contains over 35 million grid points with multiple attributes.

# Download census grid data
wget https://www.zensus2011.de/SharedDocs/Downloads/DE/Pressemitteilung/DemografischeGrunddaten/csv_Bevoelkerung_100m_Gitter.zip?__blob=publicationFile&v=3

Key properties of the dataset:

• Provided in a typical governmental open-data format
• Large enough to demonstrate big data workflows
• Spatially explicit and well suited for raster and vector analysis

require(link2GI)
require(curl)

# Create project folder structure
dirs <- link2GI::createFolders(
  root_folder = file.path(tempdir(), "link2GI_examples"),
  folders     = c("run/")
)

# Download census data
url <- "https://www.zensus2011.de/SharedDocs/Downloads/DE/Pressemitteilung/DemografischeGrunddaten/csv_Bevoelkerung_100m_Gitter.zip?__blob=publicationFile&v=3"
zipfile <- file.path(dirs$path_run, "testdata.zip")
curl::curl_download(url, zipfile)

# Extract CSV file
unzip(
  zipfile,
  files = grep("[.]csv$", unzip(zipfile, list = TRUE)$Name, value = TRUE),
  exdir = dirs$path_run,
  junkpaths = TRUE,
  overwrite = TRUE
)

Preprocessing the data

The dataset is essentially gridded xyz data. Each row represents one 100 m × 100 m grid cell.

# Fast CSV import
xyz <- data.table::fread(
  file.path(dirs$path_run, "Zensus_Bevoelkerung_100m-Gitter.csv")
)

head(xyz)

Because the data is already gridded, rasterization is straightforward. We only need to identify x, y, and value columns.

require(RColorBrewer)
require(terra)
require(mapview)

# Detect coordinate and value columns
cn <- names(xyz)
num_cols <- cn[vapply(xyz, is.numeric, logical(1))]
xcol <- num_cols[1]
ycol <- num_cols[2]
vcol <- num_cols[3]

# Build xyz matrix
xyz3 <- xyz[, c(xcol, ycol, vcol)]
names(xyz3) <- c("x", "y", "z")

# Create raster from xyz table
r <- terra::rast(xyz3, type = "xyz")
terra::crs(r) <- "EPSG:3035"

# Visualize raster
p <- colorRampPalette(brewer.pal(8, "Reds"))
mapview::mapviewOptions(mapview.maxpixels = min(2e6, ncell(r)))
mapview::mapview(
  r,
  col.regions = p,
  at = c(-1, 10, 25, 50, 100, 500, 1000, 2500),
  legend = TRUE
)

Setting up a GRASS project

We now create a permanent GRASS location from the raster object. linkGRASS() searches for an installed GRASS version and initializes it.

require(link2GI)

link2GI::linkGRASS(
  x = r,
  epsg = 3035,
  gisdbase = file.path(tempdir(), "link2GI_examples"),
  location = "microzensus2011"
)

Importing raster data into GRASS

The raster is written to GeoTIFF before import. Any GDAL-supported format would also work.

require(rgrass)
require(terra)

# Write raster to GeoTIFF
tif_file <- file.path(dirs$path_run, "Zensus_Bevoelkerung_100m-Gitter.tif")
terra::writeRaster(r, tif_file, overwrite = TRUE)

# Register raster as external GRASS dataset
rgrass::execGRASS(
  "r.external",
  flags = "o",
  input  = tif_file,
  output = "Zensus_Bevoelkerung_100m_Gitter",
  band   = "1"
)

# Inspect raster metadata
rgrass::execGRASS("r.info", map = "Zensus_Bevoelkerung_100m_Gitter")

Importing data as GRASS vector points

We first convert the xyz table into an sf point object. Each grid cell becomes a single point feature.

require(sf)

xyz_sf <- sf::st_as_sf(
  xyz,
  coords = c("x_mp_100m", "y_mp_100m"),
  crs = "EPSG:3035",
  agr = "constant"
)

plot(sf::st_geometry(xyz_sf))

The GRASS database already exists. We therefore link to it and import the vector points.

require(link2GI)
require(rgrass)

# Reuse existing GRASS location
link2GI::linkGRASS(
  x = xyz_sf,
  epsg = 3035,
  gisdbase = file.path(tempdir(), "link2GI_examples"),
  location = "microzensus2011",
  gisdbase_exist = TRUE
)

# Import sf object as GRASS vector
vname <- "Zensus_Bevoelkerung_100m"
sf2gvec(
  x = xyz_sf,
  obj_name = vname,
  gisdbase = file.path(tempdir(), "link2GI_examples"),
  location = "microzensus2011",
  gisdbase_exist = TRUE
)

# Inspect imported vector data
rgrass::execGRASS("g.list", type = "vector")
rgrass::execGRASS("v.info", map = vname)