Description Usage Arguments Details Value Note Author(s) References Examples
Optimize a sample configuration for spatial interpolation with a known linear model. A criterion is defined so that the sample configuration minimizes the mean or maximum kriging variance (MKV).
1 2 3 4 5 6 
points 
Integer value, integer vector, data frame or matrix, or list.

candi 
Data frame or matrix with the candidate locations for the jittered points. 
covars 
Data frame or matrix with the covariates in the columns. 
eqn 
Formula string that defines the dependent variable 
vgm 
Object of class 
krige.stat 
Character value defining the statistic that should be used to summarize the kriging
variance. Available options are 
... 
further arguments passed to 
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
.
optimMKV
returns an object of class OptimizedSampleConfiguration
: the optimized sample
configuration with details about the optimization.
objMKV
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 is based on the method originally proposed by Heuvelink, Brus and de Gruijter (2006) and implemented in the Rpackage intamapInteractive by Edzer Pebesma and Jon Skoien.
Alessandro SamuelRosa alessandrosamuelrosa@gmail.com
Brus, D. J.; Heuvelink, G. B. M. Optimization of sample patterns for universal kriging of environmental variables. Geoderma. v. 138, p. 8695, 2007.
Heuvelink, G. B. M.; Brus, D. J.; de Gruijter, J. J. Optimization of sample configurations for digital mapping of soil properties with universal kriging. In: Lagacherie, P.; McBratney, A. & Voltz, M. (Eds.) Digital soil mapping  an introductory perspective. Elsevier, v. 31, p. 137151, 2006.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Not run:
data(meuse.grid, package = "sp")
candi < meuse.grid[1:1000, 1:2]
covars < as.data.frame(meuse.grid)[1:1000, ]
vgm < gstat::vgm(psill = 10, model = "Exp", range = 500, nugget = 8)
schedule < scheduleSPSANN(
initial.temperature = 10, chains = 1, x.max = 1540, y.max = 2060,
x.min = 0, y.min = 0, cellsize = 40)
set.seed(2001)
res < optimMKV(
points = 10, candi = candi, covars = covars, eqn = z ~ dist,
vgm = vgm, schedule = schedule)
objSPSANN(res)  objMKV(
points = res, candi = candi, covars = covars, eqn = z ~ dist,
vgm = vgm)
## End(Not run)


Optimization of Sample Configurations using Spatial Simulated
Annealing
spsann version 2.10
(built on 20170623) is now loaded

[using universal kriging]

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====================================================================== 100%
100% of acceptance in the 1st chain
running time = 0.37 seconds[using universal kriging]
obj
0
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