GRTSmaster_habitats
data source or a
10-layered variant as a SpatRaster objectR/GRTSmh.R
read_GRTSmh.Rd
By default, the GRTSmaster_habitats
data source is returned as a
single-layered SpatRaster object with decimal integer ranking numbers as values.
If brick = TRUE
, a ten-layered SpatRaster is
returned (data source GRTSmh_brick
; resolution 32 m)
with the decimal integer ranking numbers of 10 hierarchical levels of the
GRTS cell addresses, including the one from GRTSmaster_habitats
(with GRTS cell addresses at the resolution level).
The coordinate reference system is 'BD72 / Belgian Lambert 72'
(EPSG-code 31370).
read_GRTSmh( file = file.path(locate_n2khab_data(), c("10_raw/GRTSmaster_habitats/GRTSmaster_habitats.tif", "20_processed/GRTSmh_brick/GRTSmh_brick.tif")), brick = FALSE )
file | The absolute or relative file path of the data source.
The default follows the data management advice in the
vignette on data storage (run |
---|---|
brick | Logical; determines whether the single- or ten-layered SpatRaster is returned. See the Details section. |
A single- or a ten-layered SpatRaster object, always with 21041043 cells.
If the package is configured to use the raster package
(see n2khab_options()
), a RasterLayer is
returned if brick = FALSE
and a RasterBrick if brick = TRUE
.
The data source GRTSmaster_habitats
, provided and documented in
Zenodo, is a monolayered GeoTIFF
file covering the whole of Flanders and the Brussels Capital Region at a
resolution of 32 m.
Its values are unique decimal integer ranking numbers from the GRTS
algorithm applied to the Flemish and Brussels area.
Beware that not all GRTS ranking numbers are present in the data source, as
the original GRTS raster has been clipped with the Flemish outer borders
(i.e., not excluding the Brussels Capital Region).
The GRTS algorithm uses a quadrant-recursive, hierarchically randomized
function that maps the unit square to the unit interval, resulting in a
base-4 GRTS address for each location (see
read_GRTSmh_base4frac
).
The ranking numbers in GRTSmaster_habitats
are base-10 numbers and
follow the reverse
hierarchical order: each consecutive subset of ranking numbers corresponds
to a spatially balanced sample of locations.
Hence, it allows dynamical sample sizes.
More information on the GRTS algorithm can be found in Stevens & Olsen (2003,
2004) and in the GRTS and
spsurvey packages.
Depending on the value of the brick
argument, the function either
returns the GRTSmaster_habitats
data source as a
single-layered SpatRaster (brick = FALSE
), or (brick = TRUE
)
returns the GRTSmh_brick
data source as a
ten-layered SpatRaster (resolution 32 m)
with the decimal integer ranking numbers of 10 hierarchical levels of the
GRTS cell addresses, including the one from GRTSmaster_habitats
(with GRTS cell addresses at the resolution level).
The GRTSmh_brick
data source is a processed dataset (ten-layered
GeoTIFF), available at
Zenodo, and can only be
returned by the function when it is already present as a file.
See R-code in the
n2khab-preprocessing repository for its creation from
the GRTSmaster_habitats
data source.
Both GeoTIFFs (GRTSmaster_habitats
, GRTSmh_brick
) use the
INT4S
datatype.
The higher-level ranking numbers of the ten-layered variant allow spatially
balanced
samples at lower spatial resolution than that of 32 m, and can also be
used for aggregation purposes.
The provided hierarchical levels correspond to the resolutions vector
32 * 2^(0:9)
(minimum: 32 meters, maximum: 16384 meters), with
the corresponding SpatRaster layers named as level0
to level9
.
Stevens D.L. & Olsen A.R. (2003). Variance estimation for spatially balanced samples of environmental resources. Environmetrics 14 (6): 593–610. doi:10.1002/env.606 .
Stevens D.L. & Olsen A.R. (2004). Spatially Balanced Sampling of Natural Resources. Journal of the American Statistical Association 99 (465): 262–278. doi:10.1198/016214504000000250 .
Other functions involved in processing the 'GRTSmaster_habitats' data source:
convert_base4frac_to_dec()
,
convert_dec_to_base4frac()
,
read_GRTSmh_base4frac()
,
read_GRTSmh_diffres()
if (FALSE) { # This example supposes that your working directory or a directory up to 10 # levels above has # the 'n2khab_data' folder AND that the latest version of the # 'GRTSmaster_habitats' data source is present in the default subdirectory. # In all other cases, this example won't work but at least you can consider # what to do. r <- read_GRTSmh() r r10 <- read_GRTSmh(brick = TRUE) r10 }