R: Jura data set

jura {gstat} | R Documentation

## Jura data set

### Description

The jura data set from Pierre Goovaerts' book (see references below). It contains four `data.frame`s: prediction.dat, validation.dat and transect.dat and juragrid.dat, and three `data.frame`s 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.frame`s 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)

[Package gstat version 2.0-3 Index]