simPtsOptNet: Design of optimal sampling networks through the simultaneous points method

Description

Search for an optimum set of simultaneous additional points to an initial network that minimize the average standard error of kriging, using a genetic algorithm. It takes, as input for the optimization, the minimum and maximum values of the coordinates that enclose the study area. This function uses previous samples information to direct additional sampling for minimum average standard error. The algorithm generates random sampling schemes.

Usage

simPtsOptNet(formula, loc=NULL, data, fitmodel, BLUE=FALSE, n, popSize, 
generations, xmin, ymin, xmax, ymax, plotMap=FALSE, spMap=NULL, ...)

Arguments

formula

formula that defines the interpolation method to be used. In this parameter, a dependent variable is defined as a linear model of independent variables. Suppose the dependent variable has name z, for ordinary and simple kriging use the formula z~1; for simple kriging also define beta; for universal kriging, suppose z is linearly dependent on x and y, use the formula z~x+y. See the gstat package for details

loc

object of class Spatial, or (deprecated) formula that defines the spatial data locations (coordinates) such as ~x+y; see the gstat package for details

data

data frame containing the dependent variable, independent variables, and coordinates; see the gstat package for details

fitmodel

variogram model of dependent variable (or its residuals), defined by a call to vgm or fit.variogram; see the gstat package for details

BLUE

logical; if TRUE return the BLUE trend estimates only, if FALSE return the BLUP predictions (kriging); see predict.gstat in the gstat package for details

number of additional points to be added to the original network

popSize

population size; see the genalg package for details

generations

number of iterations. Usually, hundreds or thousands are required. See the genalg package for details

xmin

minimum x-coordinate of the study area

ymin

minimum y-coordinate of the study area

xmax

maximum x-coordinate of the study area

ymax

maximum y-coordinate of the study area

plotMap

logical; if TRUE, the optimized spatial locations of additional points are plotted

spMap

an object of class Spatial; it must be provided if plotMap is set to TRUE

...

other arguments to be passed to gstat or rbga

Value

an object of class rbga containing the population and the evaluation of the objective function for each chromosome in the last generation, the best and mean evaluation value in each generation, and additional information

References

Santacruz, A., Rubiano, Y., Melo, C., 2014. Evolutionary optimization of spatial sampling networks designed for the monitoring of soil carbon. In: Hartemink, A., McSweeney, K. (Eds.). Soil Carbon. Series: Progress in Soil Science. (pp. 77-84). Springer. [link]

Santacruz, A., 2011. Evolutionary optimization of spatial sampling networks. An application of genetic algorithms and geostatistics for the monitoring of soil organic carbon. Editorial Acadmica Espaola. 183 p. ISBN: 978-3-8454-9815-7 (In Spanish) [link]

Delmelle, E., 2005. Optimization of second-phase spatial sampling using auxiliary information. Ph.D. Thesis, Dept. Geography, State University of New York, Buffalo, NY.

Examples

Run this code

## Not run: 
# ## Load data
# data(COSha30)
# data(COSha30map)
# data(lalib)
# 
# ## Calculate the sample variogram for data, generate the variogram model and  
# ## fit ranges and/or sills from the variogram model to the sample variogram
# ve <- variogram(CorT~ 1, loc=~x+y, data=COSha30, width = 236.0536)
# PSI <- 0.0001531892; RAN <- 1031.8884; NUG <- 0.0001471817
# m.esf <- vgm(PSI, "Sph", RAN, NUG)
# (m.f.esf <- fit.variogram(ve, m.esf))
# 
# ## Number of additional points to be added to the network
# npoints <- 5
# 
# ## Optimize the location of the additional points
# ## Only 20 generations are evaluated in this example (using ordinary kriging)
# ## Users can visualize how the location of the additional points is optimized 
# ## if plotMap is set to TRUE
# old.par <- par(no.readonly = TRUE)
# par(ask=FALSE)
# optnets <- simPtsOptNet(CorT~ 1, loc=~x+y, COSha30, m.f.esf, n=npoints, 
#     popSize=30, generations=20, xmin=bbox(lalib)[1], ymin=bbox(lalib)[2], 
#     xmax=bbox(lalib)[3], ymax=bbox(lalib)[4], plotMap=TRUE, spMap=lalib)
# par(old.par)
# 
# ## Summary of the genetic algorithm results
# summary(optnets, echo=TRUE)
# 
# ## Graph of best and mean evaluation value of the objective function 
# ## (average standard error) along generations
# plot(optnets)
# 
# ## Find and plot the best set of additional points (best chromosome) in   
# ## the population in the last generation
# (bnet <- bestnet(optnets))
# l1 = list("sp.polygons", lalib)
# l2 = list("sp.points", bnet, col="green", pch="*", cex=5)
# spplot(COSha30map, "var1.pred", main="Location of the optimized points", 
#     col.regions=bpy.colors(100), scales = list(draw =TRUE), xlab ="East (m)", 
#     ylab = "North (m)", sp.layout=list(l1,l2))
# 
# ## Average standard error of the optimized additional points
# min(optnets$evaluations)
# ## End(Not run)

## Multivariate prediction is also enabled:
## Not run: 
# ve <- variogram(CorT~ DA30, loc=~x+y, data=COSha30, width = 236.0536)
# (m.f.esf <- fit.variogram(ve, m.esf))
# 
# optnetsMP <- simPtsOptNet(CorT~ DA30, loc=~x+y, COSha30, m.f.esf, n=npoints, 
#     popSize=30, generations=25, xmin=bbox(lalib)[1], ymin=bbox(lalib)[2], 
#     xmax=bbox(lalib)[3], ymax=bbox(lalib)[4], plotMap=TRUE, spMap=lalib)
# ## End(Not run)