spCovAdd {intamapInteractive} | R Documentation |
This function spCovAdd allows to build optimization scenarios based on spatial coverage method.
spCovAdd( observations, candidates, nDiff, nGridCells, plotOptim = TRUE, nTry, ... )
observations |
object of class data.frame with x,y coordinates |
candidates |
a SpatialPolygonsDataFrame to explore: in use when optimizing
the implementation of new measurement stations to an existing network |
nDiff |
number of stations to add or delete |
nGridCells |
number of grid cells to work on spatial coverage strafication |
plotOptim |
logical; to plot the result or not |
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 |
... |
other arguments to be passed on at lower level functions such as
stratify |
This function allows to build optimization scenarios based on spatial coverage method.
The scenario action is "add". To add new measurement locations to the running network,
the function uses function stratify
from package spcosa
.
Function stratify adds new strata to the domain study.
data.frame
of optimized locations
Olivier Baume
[2] 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.