HierarchicalCurdling: Generate a Landscape Using Hierarchical Curdling
In ecomodtools: Ecological Modelling Toolbox

Description Usage Arguments Value Author(s) References See Also Examples

A function to generate a multidimensional landscape with each cell assigned a value of zero or one based on a the successive assignment of habitat at different spatial scales.

1	HierarchicalCurdling(DimExtents, HierBoxes, HierProp)

`DimExtents`	An integer vector with the length of the desired output in each dimension. `length(DimExtents)` gives the number of dimensions in the output.
`HierBoxes`	A matrix of integer values describing the size of the assignment boxes in each dimension and at each spatial hierarchy. Each row of the matrix represents a different hierarchy. The columns within each row describes the extent of the allocation volume in each dimension. If `ncol(HierBoxes)` is less than `length(DimExtents)` then the values in each row are recycled. `HierBoxes` can also be an integer vector, in which case the allocation volume is assumed to be of the same extent in each dimension and each value simply represents this extent at each hierarchy. `nrow(HierBoxes)` (or `length(HierBoxes)` if `HierBoxes` is a vector) gives the number of hierarchical sweeps.
`HierProp`	An integer vector containing the number of allocation volumes to retain at each hierarchical sweep. If `length(HierProp)` is less than `nrow(HierBoxes)` (or `length(HierBoxes)` if `HierBoxes` is a vector) then the values in `HierProp` are recycled.

A vector of length prod(DimExtents) and with a dimensional extent attribute set to DimExtents. Each element contains a zero or a one depending on the outcome of the hierarcical allocation.

Joseph Chipperfield <joechip90@googlemail.com>

With, K. A. (1997) The application of neutral landscape models in conservation biology. Conservation Biology, 11, 1069-1080.

With, K. A. and King A. W. (1997) The use and misuse of neutral landscapes models in ecology. Oikos, 79, 219-229.

PercolationMap SpectralSynthesis

# Create a 512 x 512 landscape allocated on three hierarchical levels.  On the
# first hierarchical divsion the landscape is divded into 64 cells of 64 x 64
# units each.  48 of these 64 cells are randomly selected as habitable.  Each
# of selected cells in the first hierarchical division is split again into 64
# cells of 8 x 8 units each.  48 of these cells are randomly selected for
# allocation in the final level.  The final hierarchical allocation, divides
# the remaining selected cells into 64 1 x 1 cells.  32 of these 64 cells are
# randomly selected as habitable

# Dimension of output landscape
dims.in <- rep(512,2)
# Number of cells to select at each hierarchical level
props <- c(48,48,32)
# Create matrix of allocation units at each hierarchical level
hier.mat <- rep(NA, length(props) * length(dims.in))
dim(hier.mat) <- c(length(props), length(dims.in))
hier.mat[1,] <- c(64,64)	# 64 x 64 allocation cells in first hierarchy
hier.mat[2,] <- c(8,8)		# 8 x 8 allocation cells in second hierarchy
hier.mat[3,] <- c(1,1)		# 1 x 1 allocation cells in third hierarchy
# Run the hierarchical curdling
output.map <- HierarchicalCurdling(dims.in, hier.mat, props)
image(output.map, col = c("black", "white"))