hip.polygon: Draws a Halton Iterative Partition (HIP) sample from a...
In tmcd82070/SDraw: Spatially Balanced Samples of Spatial Objects

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

Draws a Halton Iterative Partition (HIP) sample from a SpatialPoints* object.

1	hip.polygon(x, n, bases = c(2, 3), J = c(8, 5))

`x`	A `SpatialPolygons` or `SpatialPolygonsDataFrame` object. This object must contain at least 1 polygon. `x` is the 2-dimensional polygon resource from which samples are taken.
`n`	Target sample size. Target number of locations to draw from the set of points contained in `x`. If the sample size returned is less than the desired sample size, increase `n` until the desired sample size is reached.
`bases`	2X1 vector of Halton bases. These must be co-prime.
`J`	A 2X1 vector of base powers. `J[1]` is for horizontal, `J[2]` for vertical dimension. `J` determines the size and shape of the smallest Halton boxes. There are `bases[1]^J[1]` vertical columns of Halton boxes over `x`'s bounding box, and `bases[2]^J[2]` horizontal rows of Halton boxes over the bounding box, for a total of `prod(bases^J)` boxes. The dimension of each box is `c(dx,dy)/` `(bases^J)`, where `c(dx,dy)` are the horizontal and vertical extents of `x`'s bounding box.

A brief description of Halton Iterative Partition (HIP) sampling for polygons: Given a set of Halton Iterative Partition parameters x (SpatialPoints* object), n (sample size), bases, and J, a lattice of Halton boxes is constructed iteratively over the bounding box of the input points. This results in prod(bases^J) Halton boxes on the bounding box to cover all points in the point resource. The target should be one point per box, or prod(bases^J) == n. The Halton index of all boxes is computed and assigned to points that lie in each box. Finally, a random number between 0 and the largest Halton index is drawn, and the next n coordinates in the mapped real numbers are taken as the sample.

A SpatialPoints* object containing locations in the HIP sample.

Additional attributes of the output object, beyond those which make it a SpatialPoints*, are:

frame: Name of the input sampling frame.
frame.type: Type of resource in sampling frame. (i.e., "polygon").
sample.type: Type of sample drawn. (i.e., "HIP").
J: Exponents of the bases used to form the lattice of Halton boxes. This is either the input J, or the J vector computed by halton.indices.
bases: Bases of the Halton sequence used to draw the sample.
hl.bbox: The bounding box around points in x used to draw the sample. See halton.indices.

Michael Kleinsasser, Aidan McDonald

Robertson, B., McDonald, T., Price, C., and Brown, J. (2018) "Halton iterative partitioning: spatially balanced sampling via partitioning", Environmental and Ecological Statistics, v25, p. 305–323.

hip.point, SDraw, bas.polygon

# Draw sample of cities in the state of Washington
data(WA)
samp <- hip.polygon( WA, 100, J = c(3,2))
attributes(samp)$frame
attributes(samp)$frame.type
attributes(samp)$sample.type
attributes(samp)$bases
attributes(samp)$J
attributes(samp)$hl.bbox