utm.maas {GRASS} | R Documentation |
The utm.maas
data frame has 98 rows and 13 columns.
It records contamination of the environment by selected metals along the Dutch bank of the River Maas just north of Maastricht and contains the following columns (topsoil data were
collected as bulked samples in 1990 within a radius of 5m according to
Burrough and McDonnell 1998: 102, 309):
data(utm.maas)
This data frame contains the following columns:
Maas river bank soil pollution data
The Maas river bank soil pollution data (Limburg, The Netherlands) are sampled along the Dutch bank of the river Maas (Meuse) north of Maastricht. This is a flood plain of the river Maas, not far from where the Maas enters the Netherlands (Borgharen, Itteren, about 3 to 5 km north of Maastricht).
The river Maas is at the north-west border of the project area, traversing
the area in north-east direction. Burrough et al. 1998 use a subset of the
same data set in their book (reduced area). The data have been re-projected
for the GRASS/R interface from the Dutch standard coordinate system (TDN
coordinates in stereographic projection) to UTM coordinate system zone 32,
on WGS84 ellipsoid. A GRASS LOCATION has to be defined with following
parameters: projection UTM, ellipsoid WGS84, zone 32, north 5652930, south
5650610, west 269870, east 272460, nsres 10, ewres 10, rows 232, cols 259.
The pre-defined location including the data stored column-wise in sites
lists can be downloaded from the GRASS web site, and is also contained in
the "grassmeta" object maas.metadata
.
Maas river bank dat GRASS LOCATION, http://grass.itc.it/statsgrass/maas_grass_location.tar.gz
The functions in this package are intended to work with the GRASS geographical information system. The examples for wrapper functions will will work whether or not R is running in GRASS, and whether or not the current location is that of the data set used for the examples. Examples of interface functions will however (from version 0.2-2) only work outside GRASS, to avoid possible overwriting of GRASS database locations and/or files.
Burrough, P. & McDonnell, R. (1998) Principles of geographical information systems, New York: Oxford, pp. 309–311; Rikken, M.G.J., Van Rijn, R.P.G., 1993. Soil pollution with heavy metals — An Inquiry into Spatial Variation, Cost of Mapping and the Risk evaluation of Copper, Cadmium, Lead and Zinc in the Floodplains of the Meuse West of Stein, The Netherlands. Doctoraalveldwerkverslag, Dept. of Physical Geography, Utrecht University.
data(utm.maas) Zn.o <- as.ordered(cut(utm.maas$Zn, labels=c("insignificant", "low", "medium", "high", "crisis"), breaks=c(100, 200, 400, 700, 1000, 2000), include.lowest=TRUE)) table(Zn.o) plot(utm.maas$east, utm.maas$north, pch=unclass(Zn.o), xlab="", ylab="", asp=1) legend(x=c(269900, 270500), y=c(5652000, 5652700), pch=c(1:5), legend=levels(Zn.o)) cat("Burrough & McDonnell p. 107\n") round(tapply(utm.maas$Zn, as.factor(utm.maas$Fldf), mean), 2) round(tapply(utm.maas$Zn, as.factor(utm.maas$Fldf), sd), 2) round(tapply(log(utm.maas$Zn), as.factor(utm.maas$Fldf), mean), 3) round(tapply(log(utm.maas$Zn), as.factor(utm.maas$Fldf), sd), 3) round(exp(round(tapply(log(utm.maas$Zn), as.factor(utm.maas$Fldf), mean), 3)), 2) hist(utm.maas$Zn, breaks=seq(0,2000,100), col="grey") hist(log(utm.maas$Zn), breaks=seq(3.5,8.5,0.25), col="grey") t.test(utm.maas$Zn[utm.maas$Fldf == 2], utm.maas$Zn[utm.maas$Fldf == 3]) cat("NB: B&McD p. 108, their relative variance is (1-(RSquared))\n") anova(lm(Zn ~ as.factor(Fldf), data=utm.maas)) summary(lm(Zn ~ as.factor(Fldf), data=utm.maas)) anova(lm(log(Zn) ~ as.factor(Fldf), data=utm.maas)) summary(lm(log(Zn) ~ as.factor(Fldf), data=utm.maas))