Identify the polygon containing a location at multiple scales.

point(x, ...)

# S4 method for goc
point(x, coords, ...)



A goc object produced by GOC.


Additional arguments (not used).


A two column matrix or a SpatialPoints object giving the coordinates of points of interest.


A list with elements:


a matrix with elements giving the id of the polygon from the goc, where rows give points of interest and columns give thresholds;


is a matrix with elements giving the area of patches in a polygon (in cell counts), where rows give points of and columns give thresholds;


the same for core area of patches;


gives the patch area (in cell counts) averaged for all points of interest (defined by O'Brien et al. 2006);


is the same for the core area of patches.


See MPG for warning related to areal measurements.


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. (2013a) Finding the functional grain: comparing methods for scaling resistance surfaces. Landscape Ecology 28:1269-1291.

Galpern, P., M. Manseau. (2013b) Modelling the influence of landscape connectivity on animal distribution: a functional grain approach. Ecography 36:1004-1016.

Galpern, P., M. Manseau, A. Fall. (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis, and application for conservation. Biological Conservation 144:44-55.

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.

See also

GOC, distance


## Load raster landscape tiny <- raster::raster(system.file("extdata/tiny.asc", package = "grainscape")) ## Create a resistance surface from a raster using an is-becomes reclassification tinyCost <- raster::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 <- MPG(cost = tinyCost, patch = tinyCost == 1) ## Extract a representative subset of 5 grains of connectivity tinyPatchGOC <- GOC(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 <- point(tinyPatchGOC, loc)