rtopDisc will discretize an area for
regularization or calculation of Ghosh-distance
# S3 method for rtop
rtopDisc(object, params = list(),...)
# S3 method for SpatialPolygonsDataFrame
rtopDisc(object, params = list(), bb = bbox(object), ...)
# S3 method for SpatialPolygons
rtopDisc(object, params = list(), bb = bbox(object), ...)
# S3 method for rtopVariogram
rtopDisc(object, params = list(), ...)The function returns a list of discretized areas, or if called with an rtop-object as argument, the object with lists of discretizations of the observations and prediction locations (if part of the object). If the function is called with an rtopVariogram (usually this is an internal call), the list contains discretized pairs of hypothetical objects from each bin of the semivariogram with a centre-to-centre distance equal to the average distance between the objects in a certain bin.
object of class SpatialPolygons or SpatialPolygonsDataFrame
or rtopVariogram,
or an object with class rtop that includes one of the above
boundary box, usually modified to be the common boundary box for two spatial object
possibility to pass parameters to modify the default parameters for
the rtop package, set in getRtopParams. Typical parameters
to modify for this function are:
rresol = 100; minimum number of discretization points in areas
hresol = 5; number of discretization points in one direction for areas in binned variograms
hstype = "regular"; sampling type for binned variograms
rstype = "rtop"; sampling type for real areas
Possibility to pass individual parameters
Jon Olav Skoien
There are different options for discretizing the objects. When the areas
from the bins are discretized, the options are random or regular sampling,
regular sampling is the default.
For the real areas, regular sampling appears to have computational advantages compared
with random sampling. In addition to the traditional regular sampling, rtop
also offers a third type of sampling which assures that the same discretization
points are used for overlapping areas.
Starting with a coarse grid covering the region of interest, this will for a certain support be refined till a requested minimum number of points from the grid is within the support. In this way, for areal supports, the number of points in the area with the largest number of points will be approximately four times the requested minimum number of points. This methods also assure that points used to discretize a large support will be reused when discretizing smaller supports within the large one, e.g. subcatchments within larger catchments.
Skoien J. O., R. Merz, and G. Bloschl. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10:277-287, 2006.
Skoien, J. O., Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., Viglione, A., 2014. Rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences, 67.
rtopVariogram