optimizeNetwork {intamapInteractive}R Documentation

Optimization of networks

Description

This function allows to choose several options for optimizing a network of observation locations

Usage

optimizeNetwork(observations, predGrid, candidates, method, action,
nDiff, model, criterion = "MUKV", plotOptim = TRUE, nGridCells, nTry, nr_iterations, ...)

Arguments

observations object of class data.frame with x,y coordinates and possible covariates
predGrid object of class data.frame with x,y coordinates of prediction locations when method is "ssa"
candidates when method is "manual", candidates is the study area of class SpatialPolygonsDataFrame; for other methods, when action is "add", candidates is a data.frame with x,y coordinates or a SpatialPolygonsDataFrame
method character string: "spcov" for spatial coverage, "ssa" for spatial simulated annealing or "manual" for manual processing of the network
action character string indicating which type of action to simulate: "add" to add new measurement stations to the existing network or "del" to turn off existing stations
nDiff number of stations to add or delete
model variogram model to consider when criterion = "mukv"; object of class variogramModel, as generated by vgm
criterion Only in use for method "ssa": character string like "mukv"
plotOptim plot or not the result; TRUE by default
nGridCells when method is "spcov" and action is "add": string forcing the number gridcells to explore for candidate locations of new measurements
nTry the method will try nTry initial configurations and will keep the best solution in order to reduce the risk of ending up with an unfavorable solution
nr_iterations number of iterations before stoping
... other arguments to be passed on at lower level functions

Details

This function allows to build optimization scenarios with the choice of several options. The user should be aware of the following limitations:\ Method ssa is only implemented for the criterion mukv. Input candidates should preferably be a continuous domain such as SpatialPolygonsDataFrame When action = "add", candidates are taken from existing network observations

Value

Data frame with x,y coordinates of optimized locations

Author(s)

Olivier Baume

References

[1] http://www.intamap.org/

[2] D. J. Brus, G. B. M. Heuvelink (2007). Optimization of sample patterns for universal kriging of environmental variables, Geoderma, 138: 86-95 (2007).

[3] D. J. Brus, J. de Gruijter, J. van Groenigen (2006). Designing spatial coverage samples using the k-means clustering algorithm. In A. McBratney M. Voltz and P. Lagacherie, editor, Digital Soil Mapping: An Introductory Perspective, Developments in Soil Science, vol. 3., Elsevier, Amsterdam.

Examples

## Not run: 
library(intamapInteractive)
library(maptools)
# use SIC2004 dataset
data(sic2004)
# load a shape file of the study area
rpath = system.file("inst/external", package="intamapInteractive")
setwd(rpath)
candidates = readShapePoly("sic2004")
# load the input for the observation and prediction locations
# including the covariates for UK, and changing to more intuitive 
# variable names
coordinates(sic.val) = ~x+y
observations = sic.val["dayx"] 
predGrid= sic.grid
# limit the number of prediction locations to have faster UK 
# computations
nGrid = length(predGrid$x)
predGrid = predGrid[sample(seq(1,nGrid),1000),]
coordinates(predGrid) = ~x+y
# fit the variogram model (using function fit.variogram from package
# gstat)
model = fit.variogram(variogram(dayx~x+y, observations), vgm(50, "Sph", 250000, 250))
plot(variogram(dayx~x+y, observations), model=model)
# compute the Mukv of the current network
initMukv <- calculateMukv(observations, predGrid, model)
print(initMukv)
# delete optimally 20 stations from current network with method "ssa"
# (spatial simulated annealing) and criterion "mukv"

windows()
optim = optimizeNetwork(observations, predGrid, candidates, method = "ssa", 
            action = "del", nDiff = 20, model = model, criterion = "MUKV", plot = TRUE )
## End(Not run)

[Package intamapInteractive version 1.0-7 Index]