make.spcosa {secr}

R Documentation

Construct Spatial Coverage Design

Description

A spatial coverage design places one cluster of detectors in each compact subregion of a region of interest. Equal subregions are determined by k-means clustering of pixels (Walvoort et al. 2010).

Usage

make.spcosa(n, cluster, region, rotation = 0, randomize = FALSE, maxtries = 100, 
    keep.mask = FALSE, ...)

Arguments

n	integer number of subregions
cluster	traps object defining a cluster of detectors
region	boundary of region of interest
rotation	numeric angular rotation of each cluster (negative for random)
randomize	logical; if TRUE then cluster is located at random within subregion
maxtries	integer maximum attempts to find random location
keep.mask	logical; if TRUE then a habitat mask with covariate ‘stratum’
...	other arguments passed to make.mask

Details

The region may be specified in any form acceptable as the ‘poly’ argument of make.mask (see also boundarytoSF).

The ... argument determines the coarseness of the discretization used to define the subregions, via the ‘nx’ or ‘spacing’ arguments of make.mask.

By default (randomize = FALSE) clusters are centred at subregion centroids. Otherwise (randomize = TRUE) clusters are centred in a randomly selected cell of each subregion, subject to the constraint that all detectors fall within the subregion. An error results if no cluster meeting the constraint is found in ‘maxtries’ attempts.

Slightly different partitions of ‘poly’ are generated depending on the value of the random seed, so for consistency this should be first set with set.seed.

The argument ‘rotation’ is applied separately to each cluster, as in trap.builder and unlike the argument ‘rotate’ of make.systematic.

Value

A traps object with n x nrow(cluster) detectors.

References

Walvoort, D., Brus, D., and de Gruijter, J. (2010) An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers & Geosciences 36:1261–1267.

See Also

make.mask, trap.builder, boundarytoSF, traps

Examples

# preliminaries
polygonfile <- system.file("extdata/possumarea.txt", package = "secr")
poly <- read.table(polygonfile, header = TRUE)
subgrid <- make.grid(3,3, spacing = 80)
set.seed(123)

# nx and keep.mask refer to the discretized region of interest
tr <- make.spcosa(n = 5, subgrid, poly, nx = 32, randomize = TRUE, 
          keep.mask = TRUE)

plot(attr(tr,'mask'), dots = FALSE, cov = 'stratum', legend = FALSE)
plot(tr, add = TRUE)
lines(poly)

---