Description Usage Arguments Details Value Note Author(s) References See Also Examples
Optimize a sample configuration for spatial interpolation. The criterion used is the mean squared shortest distance (MSSD) between sample points and prediction points.
1 2 3 4 
points 
Integer value, integer vector, data frame or matrix, or list.

candi 
Data frame or matrix with the candidate locations for the jittered points. 
schedule 
List with 11 named subarguments defining the control parameters of the cooling schedule.
See 
plotit 
(Optional) Logical for plotting the optimization results, including a) the progress of the
objective function, and b) the starting (gray circles) and current sample configuration (black dots), and
the maximum jitter in the x and ycoordinates. The plots are updated at each 10 jitters. When adding
points to an existing sample configuration, fixed points are indicated using black crosses. Defaults to

track 
(Optional) Logical value. Should the evolution of the energy state be recorded and returned
along with the result? If 
boundary 
(Optional) SpatialPolygon defining the boundary of the spatial domain. If missing and

progress 
(Optional) Type of progress bar that should be used, with options 
verbose 
(Optional) Logical for printing messages about the progress of the optimization. Defaults to

Details about the mechanism used to generate a new sample configuration out of the current sample
configuration by randomly perturbing the coordinates of a sample point are available in the help page of
spJitter
.
Spatial coverage sampling is based on the knowledge that the kriging variance depends upon the distance between sample points. As such, the better the spread of the sample points in the spatial domain, the smaller the kriging variance. This is similar to using a regular grid of sample points. However, a regular grid usually is suboptimal for irregularly shaped areas.
optimMSSD
returns an object of class OptimizedSampleConfiguration
: the optimized sample
configuration with details about the optimization.
objMSSD
returns a numeric value: the energy state of the sample configuration – the objective
function value.
The distance between two points is computed as the Euclidean distance between them. This computation assumes that the optimization is operating in the twodimensional Euclidean space, i.e. the coordinates of the sample points and candidate locations should not be provided as latitude/longitude. spsann has no mechanism to check if the coordinates are projected: the user is responsible for making sure that this requirement is attained.
This function was derived with modifications from the method known as spatial coverage sampling originally proposed by Brus, de Gruijter and van Groenigen (2006), and implemented in the Rpackage spcosa by Dennis Walvoort, Dick Brus and Jaap de Gruijter.
Alessandro SamuelRosa alessandrosamuelrosa@gmail.com
Brus, D. J.; de Gruijter, J. J.; van Groenigen, J.W. Designing spatial coverage samples using the kmeans clustering algorithm. In: P. Lagacherie,A. M.; Voltz, M. (Eds.) Digital soil mapping – an introductory perspective. Elsevier, v. 31, p. 183192, 2006.
de Gruijter, J. J.; Brus, D.; Bierkens, M.; Knotters, M. Sampling for natural resource monitoring. Berlin: Springer, p. 332, 2006.
Walvoort, D. J. J.; Brus, D. J.; de Gruijter, J. J. An R package for spatial coverage sampling and random sampling from compact geographical strata by kmeans. Computers and Geosciences. v. 36, p. 12611267, 2010.
[distanceFromPoints](https://CRAN.Rproject.org/package=raster)
,
[stratify](https://CRAN.Rproject.org/package=spcosa)
.
1 2 3 4 5 6 7 8 9  require(sp)
data(meuse.grid)
candi < meuse.grid[, 1:2]
schedule < scheduleSPSANN(chains = 1, initial.temperature = 5000000,
x.max = 1540, y.max = 2060, x.min = 0,
y.min = 0, cellsize = 40)
set.seed(2001)
res < optimMSSD(points = 10, candi = candi, schedule = schedule)
objSPSANN(res)  objMSSD(candi = candi, points = res)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.