secr (version 5.0.0)

make.spcosa: 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, ...)

Value

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

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.

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)