Description Usage Arguments Details Value Author(s) References See Also Examples

Identifies and removes biases from measurement networks

1 |

`object` |
Data frame with observations with same format as |

`regCode` |
the column name of regions in the data polygons, if existing |

`...` |
further arguments to the bias correction methods called, see details below |

Many data sets can consist of data coming from a large number of different measurement networks, using different measurement devices or applying different methods for post-processing the observations. Some of these networks can exist in the same area, e.g. when different authorities are measuring the same, but at different locations (one of them in cities, the other one close to lakes), some networks will only exist as neighbouring networks (networks operated by a municipality or a country). Local networks can also be grouped together as one national data-base, which can again be merged into an international data-base.

One challenge with the merging into data-bases is that there will be inconsistencies between measurements in the different networks, which will again cause difficulties when attempting to map the observations, as done in the intamap-package. The intention of this function is therefore to call other functions that are able to identify and remove such differences, which can be referred to as biases between the networks.

There are at the moment two methods available for bias correction, "UK" and "LM".
"UK" is a universal kriging based approach implemented in
`findBiasUK`

. This method can only deal with biases between
neigbouring networks, but is well capable of taking covariates into account.
"LM" is based on local methods for estimating differences between networks, and
is implemented in `findLocalBias`

and `findRegionalBias`

.
The choice between the methods is given by the parameter `biasRemovalMethod`

in the parameter element of the object, set in `getIntamapParams`

,
called from createIntamapObject.

The function will remove biases according to the settings of the parameters
`removeBias`

.
Below is a list of the functions available for bias corrections. See each individual
function for more information about usage.

findBiasUKThe universal kriging based function for finding biases between neighbouring networks

findLocalBiasFind biases for ovelapping networks

removeLocalBiasRemoves biases between ovelapping networks

findBoundaryLinesFind points that define adjacent boundaries between regions

findRegionalBiasFind biases for neighbouring networks

removeRegionalBiasRemove biases between neighbouring networks

Data frame with observations, with the identified biases removed.

Jon Olav Skoien

Skoien, J. O., O. P. Baume, E. J. Pebesma, and G. B. M. Heuvelink. 2010. Identifying and removing heterogeneities between monitoring networks. Environmetrics 21(1), 66-84.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
## Not run:
data(meuse)
data(meuse.grid)
observations = data.frame(x = meuse$x,y = meuse$y,value = log(meuse$zinc))
coordinates(observations) = ~x+y
gridded(meuse.grid) = ~x+y
pBoundaries = spsample(observations, 8, "regular",bb = bbox(observations) +
matrix(c(-400,-400,400,400),ncol=2),offset=c(0,0))
gridded(pBoundaries) = TRUE
cs = pBoundaries@grid@cellsize[1]/2
dx = cs/5
Srl = list()
nb = dim(coordinates(pBoundaries))[1]
for (i in 1:nb) {
pt1 = coordinates(pBoundaries)[i,]
x1 = pt1[1]-cs
x2 = pt1[1]+cs
y1 = pt1[2]-cs
y2 = pt1[2]+cs
boun = data.frame(x=c(seq(x1,x2,dx),rep(x2,11),seq(x2,x1,-dx),rep(x1,11)),
y=c(rep(y1,11),seq(y1,y2,dx),rep(y2,11),seq(y2,y1,-dx)))
coordinates(boun) = ~x+y
boun = Polygon(boun)
Srl[[i]] = Polygons(list(boun),ID = as.character(i))
}
pBoundaries = SpatialPolygonsDataFrame(SpatialPolygons(Srl),
data = data.frame(ID=c(1:nb)))
observations$ID = over(observations, geometry(pBoundaries))
blines = findBoundaryLines(pBoundaries,regCode = "ID")
object = createIntamapObject(observations,meuse.grid,boundaryLines = blines,
params = list(removeBias = "regionalBias"))
object = biasCorr(object,regCode= "ID")
object$regionalBias$regionalBias
pBoundaries$bias = NA
pBoundaries$bias[object$regionalBias$regionalBias$ID] = object$regionalBias$regionalBias$ols
spplot(pBoundaries,"bias",sp.layout = list(list("sp.points",observations)))
## End(Not run)
``` |

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