Jura data set
jura.RdThe 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 \(\mbox{kg}^{-1}\) topsoil
- Co
mg cobalt \(\mbox{kg}^{-1}\) topsoil
- Cr
mg chromium \(\mbox{kg}^{-1}\) topsoil
- Cu
mg copper \(\mbox{kg}^{-1}\) topsoil
- Ni
mg nickel \(\mbox{kg}^{-1}\) topsoil
- Pb
mg lead \(\mbox{kg}^{-1}\) topsoil
- Zn
mg zinc \(\mbox{kg}^{-1}\) 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
Author
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
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.
Examples
data(jura)
summary(prediction.dat)
#> Xloc Yloc Landuse Rock
#> Min. :0.626 Min. :0.580 Min. :1.000 Min. :1.000
#> 1st Qu.:2.282 1st Qu.:1.487 1st Qu.:2.000 1st Qu.:2.000
#> Median :3.043 Median :2.581 Median :3.000 Median :2.000
#> Mean :2.980 Mean :2.665 Mean :2.548 Mean :2.699
#> 3rd Qu.:3.665 3rd Qu.:3.752 3rd Qu.:3.000 3rd Qu.:3.000
#> Max. :4.920 Max. :5.690 Max. :4.000 Max. :5.000
#> Cd Co Cr Cu
#> Min. :0.1350 Min. : 1.552 Min. : 8.72 Min. : 3.96
#> 1st Qu.:0.6375 1st Qu.: 6.520 1st Qu.:27.44 1st Qu.: 11.02
#> Median :1.0700 Median : 9.760 Median :34.84 Median : 17.60
#> Mean :1.3091 Mean : 9.303 Mean :35.07 Mean : 23.73
#> 3rd Qu.:1.7150 3rd Qu.:11.980 3rd Qu.:42.22 3rd Qu.: 27.82
#> Max. :5.1290 Max. :17.720 Max. :67.60 Max. :166.40
#> Ni Pb Zn
#> Min. : 4.20 Min. : 18.96 Min. : 25.20
#> 1st Qu.:13.80 1st Qu.: 36.52 1st Qu.: 55.00
#> Median :20.56 Median : 46.40 Median : 73.56
#> Mean :19.73 Mean : 53.92 Mean : 75.08
#> 3rd Qu.:25.42 3rd Qu.: 60.40 3rd Qu.: 89.92
#> Max. :53.20 Max. :229.56 Max. :219.32
summary(validation.dat)
#> Xloc Yloc Landuse Rock Cd
#> Min. :0.491 Min. :0.524 Min. :1.00 Min. :1.00 Min. :0.3250
#> 1st Qu.:2.207 1st Qu.:1.593 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:0.6765
#> Median :3.001 Median :2.389 Median :3.00 Median :2.00 Median :1.1865
#> Mean :2.921 Mean :2.546 Mean :2.41 Mean :2.36 Mean :1.2343
#> 3rd Qu.:3.716 3rd Qu.:3.339 3rd Qu.:3.00 3rd Qu.:3.00 3rd Qu.:1.6350
#> Max. :4.745 Max. :5.285 Max. :4.00 Max. :5.00 Max. :3.7800
#> Co Cr Cu Ni
#> Min. : 1.652 Min. : 3.32 Min. : 3.552 Min. : 1.98
#> 1st Qu.: 7.950 1st Qu.:28.44 1st Qu.: 9.150 1st Qu.:15.28
#> Median :10.060 Median :34.54 Median : 16.140 Median :21.28
#> Mean : 9.793 Mean :34.88 Mean : 23.218 Mean :20.76
#> 3rd Qu.:12.490 3rd Qu.:40.59 3rd Qu.: 23.190 3rd Qu.:25.36
#> Max. :20.600 Max. :70.00 Max. :154.600 Max. :43.68
#> Pb Zn
#> Min. : 18.68 Min. : 25.00
#> 1st Qu.: 35.31 1st Qu.: 53.19
#> Median : 47.00 Median : 73.92
#> Mean : 56.48 Mean : 77.96
#> 3rd Qu.: 60.10 3rd Qu.: 90.40
#> Max. :300.00 Max. :259.84
summary(transect.dat)
#> X Rock.type Block.Ni Cd
#> Min. :1.000 Min. :1.000 Min. : 6.611 Min. :0.135
#> 1st Qu.:2.312 1st Qu.:1.000 1st Qu.:17.938 1st Qu.:0.655
#> Median :3.625 Median :2.000 Median :20.242 Median :1.317
#> Mean :3.625 Mean :2.047 Mean :19.988 Mean :1.486
#> 3rd Qu.:4.938 3rd Qu.:2.000 3rd Qu.:23.565 3rd Qu.:1.961
#> Max. :6.250 Max. :4.000 Max. :37.047 Max. :3.925
#> NA's :96
#> Ni
#> Min. : 4.20
#> 1st Qu.:13.31
#> Median :20.52
#> Mean :19.62
#> 3rd Qu.:24.98
#> Max. :43.68
#> NA's :90
summary(juragrid.dat)
#> Xloc Yloc Landuse Rock
#> Min. :0.300 Min. :0.100 Forest : 986 Argovian :1185
#> 1st Qu.:2.050 1st Qu.:1.550 Pasture:1553 Kimmeridgian:2036
#> Median :3.000 Median :2.450 Meadow :3247 Sequanian :1628
#> Mean :2.884 Mean :2.558 Tillage: 171 Portlandian : 316
#> 3rd Qu.:3.750 3rd Qu.:3.400 Quaternary : 792
#> Max. :5.100 Max. :5.900
# the following commands were used to create objects with factors instead
# of the integer codes for Landuse and Rock:
if (FALSE) { # \dontrun{
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))
} # }
# 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")
#> Warning: GDAL Message 1: +init=epsg:XXXX syntax is deprecated. It might return a CRS with a non-EPSG compliant axis order.