gsGOCPoint: Identify the polygons containing locations in grains of...
In grainscape: Grains of connectivity and minimum planar graph modelling of landscape connectivity (Windows only)

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

Given a gsGOC object identify the polygon containing a location at multiple scales.

1	gsGOCPoint(gsGOC, coords)

`gsGOC`	A `gsGOC` object produced by `gsGOC`
`coords`	A two column matrix or a `SpatialPoints` object giving the coordinates of points of interest

A list object with elements: $pointPolygon is a matrix with elements giving the id of the polygon from the gsGOC, where rows give points of interest and columns give thresholds
$pointTotalPatchArea is a matrix with elements giving the area of patches in a polygon (in cell counts), where rows give points of interest and columns give thresholds
$pointTotalCoreArea is the same for core area of patches
$pointECS gives the patch area (in cell counts) averaged for all points of interest (defined by O'Brien et al. 2006)
$pointECSCore is the same for the core area of patches

See gsMPG for warning related to areal measurements.

Paul Galpern (pgalpern@gmail.com)

Fall, A., M.-J. Fortin, M. Manseau, D. O'Brien. (2007) Spatial graphs: Principles and applications for habitat connectivity. Ecosystems. 10:448:461

Galpern, P., M. Manseau, P.J. Wilson. (2012) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Molecular Ecology 21:3996-4009.

O'Brien, D., M. Manseau, A. Fall, and M.-J. Fortin. (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: An application of graph theory. Biological Conservation 130:70-83.

gsGOC, gsGOCDistance

## Not run: 
## Load raster landscape
tiny <- raster(system.file("extdata/tiny.asc", package="grainscape"))

## Create a resistance surface from a raster using an is-becomes reclassification
tinyCost <- reclassify(tiny, rcl=cbind(c(1, 2, 3, 4), c(1, 5, 10, 12)))

## Produce a patch-based MPG where patches are resistance features=1
tinyPatchMPG <- gsMPG(cost=tinyCost, patch=tinyCost==1)

## Extract a representative subset of 5 grains of connectivity
tinyPatchGOC <- gsGOC(tinyPatchMPG, nThresh=5)

## Three sets of coordinates in the study area
loc <- cbind(c(30, 60, 90), c(30, 60, 90))

## Find the GOC polygon containing these three locations
## for each of the 5 grains of connectivity
tinyPts <- gsGOCPoint(tinyPatchGOC, loc)

## End(Not run)