gstat: jura – R documentation

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jura

Jura data set

Description

The jura data set from Pierre Goovaerts' book (see references below). It contains four data.frames: prediction.dat, validation.dat and transect.dat and juragrid.dat, and three data.frames with consistently coded land use and rock type factors, as well as geographic coordinates. The examples below show how to transform these into spatial (sp) objects in a local coordinate system and in geographic coordinates, and how to transform to metric coordinate reference systems.

Usage

data(jura)

Format

The data.frames prediction.dat and validation.dat contain the following fields:

Xloc: X coordinate, local grid km
Yloc: Y coordinate, local grid km
Landuse: see book and below
Rock: see book and below
Cd: mg cadmium kg^-1 topsoil
Co: mg cobalt kg^-1 topsoil
Cr: mg chromium kg^-1 topsoil
Cu: mg copper kg^-1 topsoil
Ni: mg nickel kg^-1 topsoil
Pb: mg lead kg^-1 topsoil
Zn: mg zinc kg^-1 topsoil

The data.frame juragrid.dat only has the first four fields. In addition the data.frames jura.pred, jura.val and jura.grid also have inserted third and fourth fields giving geographic coordinates:

long: Longitude, WGS84 datum
lat: Latitude, WGS84 datum

Note

The points data sets were obtained from http://home.comcast.net/~pgoovaerts/book.html, which seems to be no longer available; the grid data were kindly provided by Pierre Goovaerts.

The following codes were used to convert prediction.dat and validation.dat to jura.pred and jura.val (see examples below):

Rock Types: 1: Argovian, 2: Kimmeridgian, 3: Sequanian, 4: Portlandian, 5: Quaternary.

Land uses: 1: Forest, 2: Pasture (Weide(land), Wiese, Grasland), 3: Meadow (Wiese, Flur, Matte, Anger), 4: Tillage (Ackerland, bestelltes Land)

Points 22 and 100 in the validation set (validation.dat[c(22,100),]) seem not to lie exactly on the grid originally intended, but are kept as such to be consistent with the book.

Georeferencing was based on two control points in the Swiss grid system shown as Figure 1 of Atteia et al. (see above) and further points digitized on the tentatively georeferenced scanned map. RMSE 2.4 m. Location of points in the field was less precise.

Author(s)

Data preparation by David Rossiter (dgr2@cornell.edu) and Edzer Pebesma; georeferencing by David Rossiter

References

Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. Oxford Univ. Press, New-York, 483 p. Appendix C describes (and gives) the Jura data set.

Atteia, O., Dubois, J.-P., Webster, R., 1994, Geostatistical analysis of soil contamination in the Swiss Jura: Environmental Pollution 86, 315-327

Webster, R., Atteia, O., Dubois, J.-P., 1994, Coregionalization of trace metals in the soil in the Swiss Jura: European Journal of Soil Science 45, 205-218

Examples

data(jura)
summary(prediction.dat)
summary(validation.dat)
summary(transect.dat)
summary(juragrid.dat)

# the following commands were used to create objects with factors instead
# of the integer codes for Landuse and Rock:
## Not run: 
  jura.pred = prediction.dat
  jura.val = validation.dat
  jura.grid = juragrid.dat

  jura.pred$Landuse = factor(prediction.dat$Landuse, 
	labels=levels(juragrid.dat$Landuse))
  jura.pred$Rock = factor(prediction.dat$Rock, 
	labels=levels(juragrid.dat$Rock))
  jura.val$Landuse = factor(validation.dat$Landuse, 
	labels=levels(juragrid.dat$Landuse))
  jura.val$Rock = factor(validation.dat$Rock, 
	labels=levels(juragrid.dat$Rock))

## End(Not run)

# the following commands convert data.frame objects into spatial (sp) objects
#   in the local grid:
require(sp)
coordinates(jura.pred) = ~Xloc+Yloc
coordinates(jura.val) = ~Xloc+Yloc
coordinates(jura.grid) = ~Xloc+Yloc
gridded(jura.grid) = TRUE

# the following commands convert the data.frame objects into spatial (sp) objects
#   in WGS84 geographic coordinates
# example is given only for jura.pred, do the same for jura.val and jura.grid
# EPSG codes can be found by searching make_EPSG()
jura.pred <- as.data.frame(jura.pred)
coordinates(jura.pred) = ~ long + lat
proj4string(jura.pred) = CRS("+init=epsg:4326")
# display in Google Earth
## Not run: 
if (require(maptools)) {
  kmlPoints(jura.pred,
		kmlfile="JuraPred.kml",
		kmlname="Jura Prediction Points",name=row.names(jura.pred@data),
		description=paste(jura.pred$Landuse, jura.pred$Rock, sep="/"))


  if (require(rgdal)) {
  # transform to UTM 32N
    jura.pred.utm32n = spTransform(jura.pred,
                                CRS("+init=epsg:32632"))
    coordnames(jura.pred.utm32n) = c("E","N")

    # transform to Swiss grid system CH1903 / LV03
    jura.pred.ch = spTransform(jura.pred,
                             CRS("+init=epsg:21781"))
    coordnames(jura.pred.ch) = c("X","Y")
  }
}

## End(Not run)

gstat

Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

v2.0-7

GPL (>= 2.0)

Authors

Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Benedikt Graeler [aut]

Initial release