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First steps (meuse)

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5 First steps (meuse)
5.1 Introduction
This exercise introduces geostatistical tools that can be used to analyze various types of environmental data. It is not intended as a complete analysis of the example data set. Indeed some of the steps here can be questioned, expanded, compared, and improved. The emphasis is on seeing what and some of its add-in packages can do in combination with an open source GIS such as GIS. The last section demonstrates how to export produced maps to . This whole chapter is, in a way, a prerequisite to other exercises in the book. We will use the meuse data set, which is a classical geostatistical data set used frequently by the creator of the package to demonstrate various geostatistical analysis steps (Bivand et al., 2008, §8). The data set is documented in detail by Rikken and Van Rijn (1993), and Burrough and McDonnell (1998). It consists of 155 samples of top soil heavy metal concentrations (ppm), along with a number of soil and landscape variables. The samples were collected in a flood plain of the river Meuse, near the village Stein (Lat. 50° 58’ 16", Long. 5° 44’ 39"). Historic metal mining has caused the widespread dispersal of lead, zinc, copper and cadmium in the alluvial soil. The pollutants may constrain the land use in these areas, so detailed maps are required that identify zones with high concentrations. Our specific objective will be to generate a map of a heavy metal (zinc) in soil, and a map of soil liming requirement (binary variable) using point observations, and a range of auxiliary maps. Upon completion of this exercise, you will be able to plot and fit variograms, examine correlation between various variables, run spatial predictions using the combination of continuous and categorical predictors and visualize results in external GIS packages/browsers (SAGA GIS, ). If you are new to syntax, you should consider first studying some of the introductory books (listed in the section 3.4.2).
Google Earth
SAGA
R
gstat
Google Earth
R
5.2
Data import and exploration
Download the attached meuse.R script from the book’s homepage and open it in Tinn-R. First, open a new This directory will be empty at the beginning, but you will soon be able to see data sets that you will load, generate and/or export. Now you can run the script line by line. Feel free to experiment with the code and extend it as needed. Make notes if you experience any problems or if you are not able to perform some operation. Before you start processing the data, you will need to load the following packages:
R session and change the working directory to where all your data sets will be located (C:/meuse/).
> > > > > > >
library(maptools) library(gstat) library(rgdal) library(lattice) library(RSAGA) library(geoR) library(spatstat)
117
118 You can get a list of methods in each package with the help method, e.g.:
First steps (meuse)
> help(package="maptools")
The meuse data set is in fact available in the installation directory of the field observations by typing:
gstat package. You can load the
> data(meuse) > str(meuse)
'data.frame':
$ $ $ $ $ $ $ $ $ $ $ $ $ $
x : y : cadmium: copper : lead : zinc : elev : dist : om : ffreq : soil : lime : landuse: dist.m :
155 obs. of 14 variables: num 181072 181025 181165 181298 181307 ... num 333611 333558 333537 333484 333330 ... num 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ... num 85 81 68 81 48 61 31 29 37 24 ... num 299 277 199 116 117 137 132 150 133 80 ... num 1022 1141 640 257 269 ... num 7.91 6.98 7.80 7.66 7.48 ... num 0.00136 0.01222 0.10303 0.19009 0.27709 ... num 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ... Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ... Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ... Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ... num 50 30 150 270 380 470 240 120 240 420 ...
Zn
which shows a table with 155 observations of 14 variables. To get a complete description of this data set, type:
25 Frequency 0 5 10 15 20
> ?meuse Help for 'meuse' is shown in the browser
which will open your default web-browser and show the Html help page for this data set. Here you can also find what the abbreviated names for the variables mean. We will focus on mapping the following two variables: zinc — topsoil zinc concentration in ppm; and lime — the logical variable indicating whether the soil needs liming or not. Now we can start to visually explore the data set. For example, we can visualize the target variable with a histogram:
500
1000 meuse$zinc
1500
Fig. 5.1: Histogram plot for zinc (meuse data set).
> hist(meuse$zinc, breaks=25, col="grey")
which shows that the target variable is skewed towards lower values (Fig. 5.1), and it needs to be transformed before we can run any linear interpolation. To be able to use spatial operations in e.g. from the package, we must convert the imported table into a SpatialPointDataFrame, a point map (with attributes), using the coordinates method:
gstat
R
# 'attach coordinates' - convert table to a point map: > coordinates(meuse) <- ∼ x+y > str(meuse) Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots ..@ data :'data.frame': 155 obs. of 12 variables: .. ..$ cadmium: num [1:155] 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ... .. ..$ copper : num [1:155] 85 81 68 81 48 61 31 29 37 24 ...
5.2 Data import and exploration
119
.. ..$ lead : num [1:155] 299 277 199 116 117 137 132 150 133 80 ... .. ..$ zinc : num [1:155] 1022 1141 640 257 269 ... .. ..$ elev : num [1:155] 7.91 6.98 7.80 7.66 7.48 ... .. ..$ dist : num [1:155] 0.00136 0.01222 0.10303 0.19009 0.27709 ... .. ..$ om : num [1:155] 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ... .. ..$ ffreq : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ... .. ..$ soil : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ... .. ..$ lime : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... .. ..$ landuse: Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ... .. ..$ dist.m : num [1:155] 50 30 150 270 380 470 240 120 240 420 ... ..@ coords.nrs : int [1:2] 1 2 ..@ coords : num [1:155, 1:2] 181072 181025 181165 181298 181307 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "x" "y" ..@ bbox : num [1:2, 1:2] 178605 329714 181390 333611 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "x" "y" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slots .. .. ..@ projargs: chr NA
Note that the structure is now more complicated, with a nested structure and 5 ‘slots’1 (Bivand et al., 2008, §2): (1.) @data contains the actual data in a table format (a copy of the original dataframe minus the coordinates); (2.) @coords.nrs has the coordinate dimensions; (3.) @coords contains coordinates of each element (point);
sp; (5.) @proj4string contains the definition of projection system following the proj42 format.
(4.) @bbox stands for ‘bounding box’ — this was automatically estimated by The projection and coordinate system are at first unknown (listed as NA meaning ‘not applicable’). Coordinates are just numbers as far as it is concerned. We know from the data set producers that this map is in the so-called “Rijksdriehoek” or RDH (Dutch triangulation), which is extensively documented3 . This is a: stereographic projection (parameter +proj); on the Bessel ellipsoid (parameter +ellps); with a fixed origin (parameters +lat_0 and +lon_0); scale factor at the tangency point (parameter +k); the coordinate system has a false origin (parameters +x_0 and +y_0); the center of the ellipsoid is displaced with respect to the standard WGS84 ellipsoid (parameter +towgs84, with three distances, three angles, and one scale factor)4 ; It is possible to specify all this information with the CRS method; however, it can be done more simply if the datum is included in the European Petroleum Survey Group (EPSG) database5 , now maintained by the International Association of Oil & Gas producers (OGP). This database is included as text file (epsg) in the rgdal package, in the subdirectory library/rgdal/proj in the installation folder. Referring to the EPSG registry6 , we find the following entry:
R
1 2 3 4 5 6
This is the S4 objects vocabulary. Slots are components of more complex objects.
http://trac.osgeo.org/proj/ http://www.rdnap.nl
The so-called seven datum transformation parameters (translation + rotation + scaling); also known as the Bursa Wolf method.
http://www.epsg-registry.org/ http://spatialreference.org/ref/epsg/28992/
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First steps (meuse)
# Amersfoort / RD New <28992> +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812 +units=m +no_defs <>
This shows that the Amersfoort / RD New system is EPSG reference 28992. Note that some older installations of GDAL do not carry the seven-transformation parameters that define the geodetic datum! Hence, you will need to add these parameters manually to your library/rgdal/proj/epsg file. Once you have set the correct parameters in the system, you can add the projection information to this data set using the CRS method:
> proj4string(meuse) <- CRS("+init=epsg:28992") > meuse@proj4string CRS arguments: +init=epsg:28992 +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812 +units=m +no_defs
so now the correct projection information is included in the proj4string slot and we will be able to transform this spatial layer to geographic coordinates, and then export and visualize further in . Once we have converted the table to a point map we can proceed with spatial exploration data analysis, e.g. we can simply plot the target variable in relation to sampling locations. A common plotting scheme used to display the distribution of values is the bubble method. In addition, we can import also a map of the river, and then display it together with the values of zinc (Bivand et al., 2008):
Google Earth
# > # > > >
load river (lines): data(meuse.riv) convert to a polygon map: tmp <- list(Polygons(list(Polygon(meuse.riv)), "meuse.riv")) meuse.riv <- SpatialPolygons(tmp) class(meuse.riv) [1] "SpatialPolygons" attr(,"package") [1] "sp"
> proj4string(meuse.riv) <- CRS("+init=epsg:28992") # plot together points and river: > bubble(meuse, "zinc", scales=list(draw=T), col="black", pch=1, maxsize=1.5, + sp.layout=list("sp.polygons", meuse.riv, col="grey"))
which will produce the plot shown in Fig. 5.2, left7 . Alternatively, you can also export the meuse data set to ESRI Shapefile format:
> writeOGR(meuse, ".", "meuse", "ESRI Shapefile")
which will generate four files in your working directory: meuse.shp (geometry), meuse.shx (auxiliary file), meuse.dbf (table with attributes), and meuse.prj (coordinate system). This shapefile you can now open in GIS and display using the same principle as with the bubble method (Fig. 5.2, right). Next, we import the gridded maps (40 m resolution). We will load them from the web repository8 :
SAGA
# > > + >
download the gridded maps: setInternet2(use=TRUE) # you need to login on the book's homepage first! download.file("http://spatial-analyst.net/book/system/files/meuse.zip", destfile=paste(getwd(), "meuse.zip", sep="/")) grid.list <- c("ahn.asc", "dist.asc", "ffreq.asc", "soil.asc")
7 8
See also http://r-spatial.sourceforge.net/gallery/ for a gallery of plots using meuse data set. This has some extra layers compared to the existing meusegrid data set that comes with the sp package.
5.2 Data import and exploration
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Fig. 5.2: Meuse data set and values of zinc (ppm): visualized in R (left), and in SAGA GIS (right).
# unzip the maps in a loop: > for(j in grid.list){ > fname <- zip.file.extract(file=j, zipname="meuse.zip") > file.copy(fname, paste("./", j, sep=""), overwrite=TRUE) > }
These are the explanatory variables that we will use to improve spatial prediction of the two target variables: (1.) ahn — digital elevation model (in cm) obtained from the LiDAR survey of the Netherlands9 ; (2.) dist — distance to river Meuse (in metres). (3.) ffreq — flooding frequency classes: (1) high flooding frequency, (2) medium flooding frequency, (3) no flooding; (4.) soil — map showing distribution of soil types, following the Dutch classification system: (1) Rd10A, (2) Rd90C-VIII, (3) Rd10C (de Fries et al., 2003); In addition, we can also unzip the 2 m topomap that we can use as the background for displays (Fig. 5.2, right):
# the 2 m topomap: > fname <- zip.file.extract(file="topomap2m.tif", zipname="meuse.zip") > file.copy(fname, "./topomap2m.tif", overwrite=TRUE)
We can load the grids to , also by using a loop operation:
R
> meuse.grid <- readGDAL(grid.list[1]) ahn.asc has GDAL driver AAIGrid and has 104 rows and 78 columns
9
http://www.ahn.nl
122
First steps (meuse)
# > > > >
fix the layer name: names(meuse.grid)[1] <- sub(".asc", "", grid.list[1]) for(i in grid.list[-1]) { meuse.grid@data[sub(".asc", "", i[1])] <- readGDAL(paste(i))$band1 } dist.asc has GDAL driver AAIGrid and has 104 rows and 78 columns ffreq.asc has GDAL driver AAIGrid and has 104 rows and 78 columns soil.asc has GDAL driver AAIGrid and has 104 rows and 78 columns
# set the correct coordinate system: > proj4string(meuse.grid) <- CRS("+init=epsg:28992")
Note that two of the four predictors imported (ffreq and soil) are categorical variables. However they are coded in the ASCII file as integer numbers, which does not recognize automatically. We need to tell that these are categories:
R
ArcInfo
R
> meuse.grid$ffreq <- as.factor(meuse.grid$ffreq) > table(meuse.grid$ffreq) 1 2 3 779 1335 989 > meuse.grid$soil <- as.factor(meuse.grid$soil) > table(meuse.grid$soil) 1 2 3 1665 1084 354
Flooding frequency classes Soil type classes
Distance to river
1.0
Elevation (cm)
6000 0.8 0.6 0.4 4000 0.2 0.0 3500 3000 5500 5000 4500
3 2 1
3 2 1
Fig. 5.3: Meuse auxiliary predictors.
If you examine at the structure of the meuse.grid object, you will notice that it basically has a similar structure to a SpatialPointsDataFrame, except this is an object with a grid topology:
Formal class 'SpatialGridDataFrame' [package "sp"] with 6 slots ..@ data :'data.frame': 8112 obs. of 4 variables: .. ..$ ahn : int [1:8112] NA NA NA NA NA NA NA NA NA NA ... .. ..$ dist : num [1:8112] NA NA NA NA NA NA NA NA NA NA ... .. ..$ ffreq: Factor w/ 3 levels "1","2","3": NA NA NA NA NA NA NA NA NA NA ... .. ..$ soil : Factor w/ 3 levels "1","2","3": NA NA NA NA NA NA NA NA NA NA ... ..@ grid :Formal class 'GridTopology' [package "sp"] with 3 slots .. .. ..@ cellcentre.offset: Named num [1:2] 178460 329620
5.2 Data import and exploration
123
.. .. .. ..- attr(*, "names")= chr [1:2] "x" "y" .. .. ..@ cellsize : num [1:2] 40 40 .. .. ..@ cells.dim : int [1:2] 78 104 ..@ grid.index : int(0) ..@ coords : num [1:2, 1:2] 178460 181540 329620 333740 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "x" "y" ..@ bbox : num [1:2, 1:2] 178440 329600 181560 333760 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "x" "y" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slots .. .. ..@ projargs: chr " +init=epsg:28992 +proj=sterea +lat_0=52.15616055 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bess"| __truncated__
Many of the grid nodes are unavailable (NA sign), so that it seems that the layers carry no information. To check that everything is ok, we can plot the four gridded maps together (Fig. 5.3):
ffreq.plt <- spplot(meuse.grid["ffreq"], + col.regions=grey(runif(length(levels(meuse.grid$ffreq)))), + main="Flooding frequency classes") dist.plt <- spplot(meuse.grid["dist"], + col.regions=grey(rev(seq(0,1,0.025))), + main="Distance to river") soil.plt <- spplot(meuse.grid["soil"], + col.regions=grey(runif(length(levels(meuse.grid$ffreq)))), + main="Soil type classes") ahn.plt <- spplot(meuse.grid["ahn"], + col.regions=grey(rev(seq(0,1,0.025))), + main="Elevation (cm)") > print(ffreq.plt, split=c(1, 1, 4, 1), more=TRUE) > print(dist.plt, split=c(2, 1, 4, 1), more=TRUE) > print(ahn.plt, split=c(3, 1, 4, 1), more=TRUE) > print(soil.plt, split=c(4, 1, 4, 1), more=TRUE)
5.2.1 Exploratory data analysis: sampling design
As noted in the preface, no geostatistician can promise high quality products without quality input point samples. To assess how representative and consistent the input data are, we can run some basic exploratory analysis to look at the point geometry and how well the environmental features are represented. We can start with point pattern analysis as implemented in the package, e.g. to determine the average spacing between the points (Baddeley, 2008):
spatstat
# > > + # >
coerce to a ppp object: mg_owin <- as.owin(meuse.grid["dist"]) meuse.ppp <- ppp(x=coordinates(meuse)[,1], y=coordinates(meuse)[,2], marks=meuse$zinc, window=mg_owin) plot(meuse.ppp) summary(dist.points) Min. 1st Qu. Median 43.93 77.88 107.40 Mean 3rd Qu. 111.70 137.70 Max. 353.00
which shows that the means shortest distance is 111 m. The following two questions are relevant for further analysis: (1) are the sampling locations distributed independently and uniformly over the area of interest (i.e. is there a significant clustering of locations)? (2) is the environmental feature space well represented? To answer the first question we can test the sampling design for Complete Spatial Randomness (CSR). CRS assumes that there are no regions in the study area where events are more likely to occur, and that the presence of a given event does not modify the probability of other events appearing nearby (Bivand et al., 2008).
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First steps (meuse)
The compatibility of a sampling design with CRS can be assessed by plotting the empirical function against the theoretical expectation (Bivand et al., 2008, p.160–163):
> env.meuse <- envelope(meuse.ppp, fun=Gest) Generating 99 simulations of CSR ... 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ... 91, 92, 93, 94, 95, 96, 97, 98, 99. > plot(env.meuse, lwd=list(3,1,1,1), main="CSR test (meuse)")
which will run 100 simulations using the given point pattern and derive confidence bands for a CSR using the so called G function — this measures the distribution of the distances from an arbitrary event to its nearest event (Diggle, 2003). The plot of distributions, actual versus expected CSR (Fig. 5.4), shows that the sampling design is somewhat clustered at shorter distances up to 75 m. Although the line of the observed distribution is in >80% of distance range outside the confidence bands (envelopes), we can say that the sampling plan is, in general, representative relative to geographical space. Next we look at the feature space coverage. For example, we can check whether there is a significant difference in the distribution of values at sampling locations and in the whole area of interest. To run this type of analysis we need to overlay sampling points and predictors to create an object10 with just the sample points, values of the target variable and of the feature-space predictors. We use the overlay method of the package to extract the values from the grid maps:
CSR test (meuse)
1.0 G(r) 0.0 0 0.2 0.4 0.6 0.8
50 r
100
150
sp
Fig. 5.4: Comparison of the confidence bands for the G function (Complete Spatial Randomness) and the actual observed distribution (bold line). Derived using the envelope method in spatstat.
> meuse.ov <- overlay(meuse.grid, meuse) > meuse.ov@data <- cbind(meuse.ov@data, meuse[c("zinc", "lime")]@data) > str(meuse.ov@data)
'data.frame':
$ $ $ $ $ $
ahn : dist : ffreq: soil : zinc : lime :
155 obs. of 6 variables: int 3214 3402 3277 3563 3406 3355 3428 3476 3522 3525 ... num 0.00136 0.01222 0.10303 0.19009 0.27709 ... Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ... Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ... num 1022 1141 640 257 269 ... Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
Now we can run some explanatory analyzes that focus on the feature space. First, we can visually compare the histograms of the maps with the histograms of values at point locations, e.g. by using a back to back histogram11 :
> > > + > +
library(Hmisc) options(digits=1) dist.histbb <- histbackback(meuse.ov$dist, meuse.grid$dist, prob=TRUE, xlab=c("sample","map"), main="Distance (m)") barplot(-dist.histbb$left, col="dark grey" , horiz=TRUE, space=0, add=TRUE, axes=FALSE)
10 11
Often refer to as the “regression matrix”. This requires installation of the package Hmisc.
5.2 Data import and exploration
125
> > + > + > > > > > >
barplot(dist.histbb$right, col="grey", horiz=TRUE, space=0, add=TRUE, axes=FALSE) ahn.histbb <- histbackback(meuse.ov$ahn, meuse.grid$ahn, prob=TRUE, xlab=c("sample","map"), main="AHN (cm)") barplot(-ahn.histbb$left, col="dark grey" , horiz=TRUE, space=0, add=TRUE, axes=FALSE) barplot(ahn.histbb$right, col="grey", horiz=TRUE, space=0, add=TRUE, axes=FALSE) par(mfrow=c(1,2)) print(dist.histbb, add=TRUE) print(ahn.histbb, add=FALSE) dev.off() options(digits=3)
Distance (m)
1.0
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sample
Fig. 5.5: Histogram for sampled values of dist and ahn (155 locations) versus the histogram of the raster map (all raster nodes). Produced using the histbackback method.
This will produce two histograms next to each other so that we can visually compare how well the samples represent the original feature space of the raster maps (Fig. 5.5). In the case of the points data set, we can see that the samples are misrepresenting higher elevations, but distances from the river are well represented. We can actually test if the histograms of sampled variables are significantly different from the histograms of original raster maps e.g. by using a non-parametric test such as the Kolmogorov-Smirnov test:
> ks.test(dist.histbb$left, dist.histbb$right) Two-sample Kolmogorov-Smirnov test data: dist.histbb$left and dist.histbb$right D = 0.2, p-value = 0.9945 alternative hypothesis: two-sided > ks.test(ahn.histbb$left, ahn.histbb$right) Two-sample Kolmogorov-Smirnov test data: ahn.histbb$left and ahn.histbb$right D = 0.7, p-value = 0.0001673 alternative hypothesis: two-sided Warning message: In ks.test(ahn.histbb$left, ahn.histbb$right) : cannot compute correct p-values with ties
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First steps (meuse)
which shows that the first two histograms (dist) do not differ much, but the second two (ahn) have significantly different distributions (D=0.7, p-value=0.0001673). Another test that you might do to compare the histograms is to run the correlation test12 :
> cor.test(ahn.histbb$left, ahn.histbb$right)
In the step of geographic analysis of the sampling design we will assess whether the sampling density within different soil mapping units (soil) is consistent. First, we look at how many points fall into each zone:
> summary(meuse.ov$soil) 1 2 3 97 46 12
then we need to derive the observed inspection density using:
# > + + # > + + # >
observed: inspdens.obs <- summary(meuse.ov$soil)[1:length(levels(meuse.ov$soil))]/ (summary(meuse.grid$soil)[1:length(levels(meuse.grid$soil))] *meuse.grid@grid@cellsize[[1]]^2) expected: inspdens.exp <- rep(length(meuse.ov$soil)/ (length(meuse.grid$soil[!is.na(meuse.grid$soil)]) *meuse.grid@grid@cellsize[[1]]^2), length(levels(meuse.ov$soil))) inspection density in no./ha: inspdens.obs*10000 1 2 3 0.364 0.265 0.212
> inspdens.exp*10000 [1] 0.312 0.312 0.312
which can also be compared by using the Kolmogorov-Smirnov test:
> ks.test(inspdens.obs, inspdens.exp) Two-sample Kolmogorov-Smirnov test data: inspdens.obs and inspdens.exp D = 0.667, p-value = 0.5176 alternative hypothesis: two-sided Warning message: In ks.test(inspdens.obs, inspdens.exp) : cannot compute correct p-values with ties
In this case, we see that inspection density is also significantly inconsistent considering the map of soil, which is not by chance (p-value=0.5176). We could also run a similar analysis for land cover types or any other factor-type predictors. So in summary, we can conclude for the meuse sampling design that: the average distance to the nearest neighbor is 111 m and the size of the area is 496 ha; the sampling intensity is 3 points per 10 ha, which corresponds to a grid cell size of about 15 m (Hengl, 2006); the sampling density varies in geographical space — sampling is significantly clustered for smaller distance (<75 m); the sampling is unrepresentative considering the maps of ahn and soil — higher elevations and soil class 3 are significantly under-sampled; These results do not mean that this data set is unsuitable for generating maps, but they do indicate that it has some limitations considering representativeness, independency and consistency requirements.
12
Also known as the test of no correlation because it computes t-value for correlation coefficient being equal to zero.
5.3 Zinc concentrations
127
5.3 Zinc concentrations
5.3.1 Regression modeling
The main objective of regression-kriging analysis is to build a regression model by using the explanatory gridded maps. We have previously estimated values of explanatory maps and target variables in the same table (overlay operation), so we can start by visually exploring the relation between the target variable and the continuous predictors e.g. by using a smoothed scatterplot (Fig. 5.6):
> par(mfrow = c(1, 2)) > scatter.smooth(meuse.ov$dist, meuse.ov$zinc, span=18/19, + col="grey", xlab="Distance to river (log)", ylab="Zinc (ppm)") > scatter.smooth(meuse.ov$ahn, meuse.ov$zinc, span=18/19, + col="grey", xlab="Elevation (cm)", ylab="Zinc (ppm)")
which shows that the values of zinc decrease as the distance from (water) streams and elevation increases. This supports our knowledge about the area — the majority of heavy metals has been originated from fresh water deposition. The relation seems to be particulary clear, but it appears to be non-linear as the fitted lines are curved.
q q
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Fig. 5.6: Scatterplots showing the relation between zinc and distance from river, and elevation.
Another useful analysis relevant for the success of regression modeling is to look at the multicolinearity of predictors. Some predictors show the same feature, i.e. they are not independent. For example, dist.asc and ahn.asc maps are correlated:
> pairs(zinc ∼ ahn+dist, meuse.ov) > cor(meuse.grid$ahn, meuse.grid$dist, use="complete.obs") [1] 0.294
To visualize the relationship between the target variable and the classified predictors we used a grouped
boxplot; this also allows us to count the samples in each class (Fig. 5.7): > > + + > + + > > par(mfrow=c(1,2)) boxplot(log1p(meuse.ov$zinc) ∼ meuse.ov$soil, col=grey(runif(length(levels(meuse.ov$soil)))), xlab="Soil type classes", ylab="Zinc (ppm)") boxplot(log1p(meuse.ov$zinc) ∼ meuse.ov$ffreq, col=grey(runif(length(levels(meuse.ov$soil)))), xlab="Flooding frequency classes", ylab="Zinc (ppm)") dev.off() boxplot(log1p(meuse.ov$zinc) ∼ meuse.ov$soil, plot=FALSE)$n
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First steps (meuse)
5.0 5.5 6.0 6.5 7.0 7.5
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q
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q q
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Fig. 5.7: Boxplots showing differences in zinc values (log-scale) between various soil and flooding frequency mapping units.
[1] 97 46 12 > boxplot(log1p(meuse.ov$zinc) [1] 73 53 29
which indicates that soil class "1" carries significantly higher zinc content than the remaining two classes. Note that there are only 12 field samples in the soil class "3", but still enough13 to fit a regression model. Now that we have some idea of the qualitative relation between the predictors and target variable, we proceed with fitting a regression model. We will first try to explain variation in the target variable by using all possible physical predictors — continuous and categorical. Because the target variable is heavily skewed towards lower values, we will use its transform to fit a linear model:

meuse.ov$ffreq, plot=FALSE)$n
> lm.zinc <- lm(log1p(zinc) > summary(lm.zinc) Call: lm(formula = log1p(zinc)

ahn+dist+ffreq+soil, meuse.ov)

ahn + dist + ffreq + soil, data = meuse.ov) Max 1.3669 value 7.23 -1.26 -6.97 -4.68 -3.43 -3.89 -0.59 Pr(>|t|) 2.3e-11 0.21069 9.7e-11 6.5e-06 0.00078 0.00015 0.55302
Residuals: Min 1Q Median 3Q -0.8421 -0.2794 0.0036 0.2469
Coefficients: Estimate Std. Error t (Intercept) 8.114955 1.121854 ahn -0.000402 0.000320 dist -1.796855 0.257795 ffreq2 -0.434477 0.092897 ffreq3 -0.415166 0.121071 soil2 -0.368315 0.094768 soil3 -0.097237 0.163533 --Signif. codes: 0 '***' 0.001 '**'
*** *** *** *** ***
0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.43 on 148 degrees of freedom Multiple R-squared: 0.658, Adjusted R-squared: 0.644 F-statistic: 47.4 on 6 and 148 DF, p-value: <2e-16
13
By a rule of thumb, we should have at least 5 observations per mapping unit to be able to fit a reliable model.
5.3 Zinc concentrations
129
The lm method has automatically converted factor-variables into indicator (dummy) variables. The summary statistics show that our predictors are significant in explaining the variation in log1p(zinc). However, not all of them are equally significant; some could probably be left out. We have previously demonstrated that some predictors are cross-correlated (e.g. dist and ahn). To account for these problems, we will do the following: first, we will generate indicator maps to represent all classes of interest:
> > > > > >
meuse.grid$soil1 <- ifelse(meuse.grid$soil=="1", 1, 0) meuse.grid$soil2 <- ifelse(meuse.grid$soil=="2", 1, 0) meuse.grid$soil3 <- ifelse(meuse.grid$soil=="3", 1, 0) meuse.grid$ffreq1 <- ifelse(meuse.grid$ffreq=="1", 1, 0) meuse.grid$ffreq2 <- ifelse(meuse.grid$ffreq=="2", 1, 0) meuse.grid$ffreq3 <- ifelse(meuse.grid$ffreq=="3", 1, 0)
so that we can convert all grids to principal components to reduce their multi-dimensionality14 :
> pc.predmaps <- prcomp( ∼ ahn+dist+soil1+soil2+soil3+ffreq1+ffreq2+ffreq3, + scale=TRUE, meuse.grid) > biplot(pc.predmaps, xlabs=rep(".", length(pc.predmaps$x[,1])), arrow.len=0.1, + xlab="First component", ylab="Second component")
After the principal component analysis, we need to convert the derived PCs (10) to grids, since they have lost their spatial reference. This will take few steps:
> # > > # > # > > > # > > > >
pc.comps <- as.data.frame(pc.predmaps$x) insert grid index: meuse.grid$nrs <- seq(1, length(meuse.grid@data[[1]])) meuse.grid.pnt <- as(meuse.grid["nrs"], "SpatialPointsDataFrame") mask NA grid nodes: maskpoints <- as.numeric(attr(pc.predmaps$x, "dimnames")[[1]]) attach coordinates: pc.comps$X <- meuse.grid.pnt@coords[maskpoints, 1] pc.comps$Y <- meuse.grid.pnt@coords[maskpoints, 2] coordinates(pc.comps) <- ∼ X + Y convert to a grid: gridded(pc.comps) <- TRUE pc.comps <- as(pc.comps, "SpatialGridDataFrame") proj4string(pc.comps) <- meuse.grid@proj4string names(pc.comps) [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
overlay the points and PCs again, and re-fit a regression model:
> meuse.ov2 <- overlay(pc.comps, meuse) > meuse.ov@data <- cbind(meuse.ov@data, meuse.ov2@data)
Because all predictors should now be independent, we can reduce their number by using step-wise regression:
> lm.zinc <- lm(log1p(zinc) ∼ PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8, meuse.ov) > step.zinc <- step(lm.zinc) > summary(step.zinc) Call: lm(formula = log1p(zinc) ~ PC1 + PC2 + PC3 + PC4 + PC6, data=meuse.ov) Residuals: Min 1Q Median -0.833465 -0.282418 0.000112
14
3Q Max 0.261798 1.415499
An important assumption of linear regression is that the predictors are mutually independent (Kutner et al., 2004).
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First steps (meuse)
Coefficients: Estimate Std. Error t value (Intercept) 5.6398 0.0384 146.94 PC1 -0.3535 0.0242 -14.59 PC2 -0.0645 0.0269 -2.40 PC3 -0.0830 0.0312 -2.66 PC4 0.0582 0.0387 1.50 PC6 -0.2407 0.0617 -3.90 --Signif. codes: 0 '***' 0.001 '**' 0.01
Pr(>|t|) < 2e-16 < 2e-16 0.01756 0.00869 0.13499 0.00014
*** *** * ** ***
'*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.426 on 149 degrees of freedom Multiple R-squared: 0.661, Adjusted R-squared: 0.65 F-statistic: 58.2 on 5 and 149 DF, p-value: <2e-16
The resulting models shows that there are only two predictors that are highly significant, and four that are marginally significant, while four predictors can be removed from the list. You should also check the diagnostic plots for this regression model to see if the assumptions15 of linear regression are met. 5.3.2 Variogram modeling We proceed with modeling of the variogram, which will be later used to make predictions using universal kriging in . Let us first compute the sample (experimental) variogram with the variogram method of the package:
gstat
gstat
> zinc.svar <- variogram(log1p(zinc) > plot(zinc.svar)

1, meuse)
This shows that the semivariance reaches a definite sill at a distance of about 1000 m (Fig. 5.8, left). We can use the automatic fitting method16 in to fit a suitable variogram model. This method requires some initial parameters. We can set them using the following rule of thumb:
gstat
Nugget is zero; Sill is the total (nonspatial) variance of the data set; Range is one-quarter of the diagonal of the bounding box. In , we can code this by using:
R
> zinc.vgm <- fit.variogram(zinc.svar, model=zinc.ivgm) > zinc.vgm 1 2 model psill range Nug 0.000 0 Exp 0.714 449
> zinc.vgm.plt <- plot(zinc.svar, zinc.vgm, pch="+", pl=TRUE, + col="black", main="log1p(zinc)")
The idea behind using default values for initial variogram is that the process can be automated, without need to visually examine each variogram; although, for some variograms the automated fit may not converge to a reasonable solution (if at all). In this example, the fitting runs without a problem and you should get something like Fig. 5.8. In order to fit the regression-kriging model, we actually need to fit the variogram for the residuals:
15 16
Normally distributed, symmetric residuals around the regression line; no heteroscedascity, outliers or similar unwanted effects. The fit.variogram method uses weighted least-squares.
5.3 Zinc concentrations
131
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Fig. 5.8: Variogram for original variable, and regression residuals.
> > + + > >
zinc.rsvar <- variogram(residuals(step.zinc) ∼ 1, meuse.ov) zinc.ivgm <- vgm(nugget=0, model="Exp", range=sqrt(diff(meuse@bbox["x",])^2 + diff(meuse@bbox["y",])^2)/4, psill=var(residuals(step.zinc))) zinc.rvgm <- fit.variogram(zinc.rsvar, model=zinc.ivgm) zinc.rvgm 1 2 model psill range Nug 0.0546 0 Exp 0.1505 374
> + # > > > >
zinc.rvgm.plt <- plot(zinc.rsvar, zinc.rvgm, pc="+", pl=FALSE, col="black", main="Residuals") synchronize the two plots: zinc.rvgm.plt$x.limits <- zinc.vgm.plt$x.limits zinc.rvgm.plt$y.limits <- zinc.vgm.plt$y.limits print(zinc.vgm.plt, split=c(1,1,2,1), more=TRUE) print(zinc.rvgm.plt, split=c(2,1,2,1), more=FALSE)
which shows a somewhat different picture than in the case of the original variable (zinc.vgm): the sill parameter is now much smaller, as you can notice from the plot (Fig. 5.8, right). This is expected because that the regression model (§5.3.1) has already explained 65% of the variation in the target variable. 5.3.3 Spatial prediction of Zinc Once we have fitted both the regression model (deterministic part of variation) and the variogram for residuals (stochastic, spatially-autocorrelated part of variation), we can proceed with regression-kriging. This method is implemented in the ’s generic spatial prediction method called krige:
gstat
> zinc.rk <- krige(step.zinc$call$formula, meuse.ov, pc.comps, zinc.rvgm) [using universal kriging] # back-transform the values: > zinc.rk$var1.rk <- expm1(zinc.rk$var1.pred)
132
First steps (meuse)
where step.zinc$call$formula is the regression model estimated in the previous section:
> step.zinc$call$formula log1p(zinc)

PC1 + PC2 + PC3 + PC4 + PC6
where zinc.rvgm is the fitted residual variogram, and expm1 is back-transformation function. For a comparison, we can make predictions at all locations of interest also using ordinary kriging:
> zinc.ok <- krige(log1p(zinc) [using ordinary kriging]

1, meuse, meuse.grid["soil"], zinc.vgm)
> zinc.ok$var1.rk <- expm1(zinc.ok$var1.pred)
OK predictions (zinc)
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+ + + + + + + + ++ + ++ + + + + + + + ++ + ++ + + ++ + + ++ + + + ++ + + + + + ++ + + + + + + + ++ + + + + + + + + + + + + + + + + + ++ + + + ++ + + + + + + + + + + ++ + + + + + + + + + ++ + + + ++ + + + + ++ + + + + + + ++ + + + ++ + + + + + + + + ++ + + + + + + + + + + + + + + + + + + + +
RK predictions (zinc)
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+ + + + + + + + ++ + ++ + + + + + + + ++ + ++ + + ++ + + ++ + + + ++ + + + + + ++ + + + + + + + ++ + + + + + + + + + + + + + + + + + ++ + + + ++ + + + + + + + + + + ++ + + + + + + + + + ++ + + + ++ + + + + ++ + + + + + + ++ + + + ++ + + + + + + + + ++ + + + + + + + + + + + + + + + + + + + +
1600 1400 1200 1000 800 600 400 200
1600 1400 1200 1000 800 600 400 200
Fig. 5.9: Ordinary kriging vs regression-kriging: spatial prediction of zinc.
and compare the two maps side-by-side:
# > > + + + > + + + > >
display limits: at.zinc <- seq(min(meuse$zinc),max(meuse$zinc),sd(meuse$zinc)/5) zinc.ok.plt <- spplot(zinc.ok["var1.rk"], col.regions=grey(rev(seq(0,0.97,1/length(at.zinc)))), at=at.zinc, main="OK predictions (zinc)", sp.layout=list("sp.points",pch="+", col="black", meuse)) zinc.rk.plt <- spplot(zinc.rk["var1.rk"], col.regions=grey(rev(seq(0,0.97,1/length(at.zinc)))), at=at.zinc, main="RK predictions (zinc)", sp.layout=list("sp.points", pch="+", col="black", meuse)) print(zinc.ok.plt, split=c(1,1,2,1), more=T) print(zinc.rk.plt, split=c(2,1,2,1), more=F)
Visually, there are some clear differences between the two maps. The regression-kriging map shows much more local detail (the maps showing distance from river and soil and flooding frequency classes are reflected in the output map); the locations of hot-spots on both maps are, on the other hand, very similar. But visual comparison is not enough. Hence, we also would like to see which technique is more accurate. To achieve this, we use use the leave-one-out cross validation method, as implemented in the krige.cv method of package (Pebesma, 2004). To run cross-validations, we simply use the built-in krive.cv method:
gstat
5.4 Liming requirements
133
> cross.zinc.ok <- krige.cv(log1p(zinc) ∼ 1, meuse.ov, + zinc.vgm, verbose=FALSE) # show no output > cross.zinc.rk <- krige.cv(step.zinc$call$formula, meuse.ov, + zinc.rvgm, verbose=FALSE)
You will notice that the kriging system is solved once for each input data point. To evaluate the crossvalidation, we can compare RMSE summaries (§1.4), and in particular the standard deviations of the errors (field residual of the cross-validation object). To estimate how much of variation has been explained by the two models, we can run:
# amount of variation explained by the models: > 1-var(cross.zinc.ok$residual, na.rm=T)/var(log1p(meuse$zinc)) [1] 0.701 > 1-var(cross.zinc.rk$residual, na.rm=T)/var(log1p(meuse$zinc)) [1] 0.773
which shows that OK is not much worse than RK — RK is a better predictor, but the difference is only 7%. This is possibly because variables dist and soil are also spatially continuous, and because the samples are equally spread in geographic space. Indeed, if you look at Fig. 5.9 again, you will notice that the two maps do not differ much. Note also that amount of variation explained by RK geostatistical model is about 80%, which is satisfactory.
5.4
Liming requirements
5.4.1 Fitting a GLM
In the second part of this exercise, we will try to interpolate a categorical variable using the same regressionkriging model. This variable is not as simple as zinc, since it ranges from 0 to 1 i.e. it is a binomial variable. We need to respect that property of the data, and try to fit it using a GLM (Kutner et al., 2004): (Pc ) = µ = g −1 (q · β) (5.4.1)
where E(P) is the expected probability of class c (Pc ∈ [0, 1]), q · β is the linear regression model, and g is the link function. Because the target variable is a binomial variable, we need to use the logit link function: g(µ) = µ+ = ln µ 1−µ (5.4.2)
so the Eq.(5.4.1) becomes logistic regression (Kutner et al., 2004). How does this works in ? Instead of fitting a simple linear model (lm), we can use a more generic glm method with the logit link function (Fig. 5.10, left):
R
> + > # >
glm.lime <- glm(lime ∼ PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8, meuse.ov, family=binomial(link="logit")) step.lime <- step(glm.lime) check if the predictions are within 0-1 range: summary(round(step.lime$fitted.values, 2)) Min. 1st Qu. Median 0.000 0.010 0.090 Mean 3rd Qu. 0.284 0.555 Max. 0.920
What you do not see from your session is that the GLM model is fitted iteratively, i.e. using a more sophisticated approach than if we would simply fit a lm (e.g. using ordinary least square — no iterations). To learn more about the GLMs and how are they fitted and how to interpret the results see Kutner et al. (2004). Next, we can predict the values17 at all grid nodes using this model:
17
R
Important: note that we focus on values in the transformed scale, i.e. logits.
134
First steps (meuse)
> p.glm <- predict(glm.lime, newdata=pc.comps, type="link", se.fit=T) > str(p.glm) List of 3 $ fit : Named num [1:3024] 2.85 2.30 2.25 1.83 2.77 ... ..- attr(*, "names")= chr [1:3024] "68" "144" "145" "146" ... $ se.fit : Named num [1:3024] 1.071 0.813 0.834 0.729 1.028 ... ..- attr(*, "names")= chr [1:3024] "68" "144" "145" "146" ... $ residual.scale: num 1
which shows that the spatial structure of the object was lost. Obviously, we will not be able to display the results as a map until we convert it to a gridded data frame. This takes few steps:
# > > > > >
convert to a gridded layer: lime.glm <- as(pc.comps, "SpatialPointsDataFrame") lime.glm$lime <- p.glm$fit gridded(lime.glm) <- TRUE lime.glm <- as(lime.glm, "SpatialGridDataFrame") proj4string(lime.glm) <- meuse.grid@proj4string
5.4.2
Variogram fitting and final predictions
The remaining residuals we can interpolate using ordinary kriging. This is assuming that the residuals follow an approximately normal distribution. If the GLM we use is appropriate, this should indeed be the case. First, we estimate the variogram model:
> # > + + > +
hist(residuals(step.lime), breaks=25, col="grey") residuals are normal; lime.ivgm <- vgm(nugget=0, model="Exp", range=sqrt(diff(meuse@bbox["x",])^2 + diff(meuse@bbox["y", ])^2)/4, psill=var(residuals(step.lime))) lime.rvgm <- fit.variogram(variogram(residuals(step.lime) ∼ 1, meuse.ov), model=lime.ivgm)
GLM fit (logits)
Variogram for residuals
+ + 543 477 + 533 564 + + 500 452 + + 574 + + + + 4 589 457 + + 57 41957 547 + 415 + 299
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Fig. 5.10: Measured and predicted (GLM with binomial function) values for lime variable (left); variogram for the GLM residuals (right).
5.4 Liming requirements
135
Liming requirements
1.0 0.9 333000 0.8 0.7 332000 0.6 0.5 0.4 0.3 0.2 330000 0.1 179000 180000 181000
331000
Fig. 5.11: Liming requirements predicted using regression-kriging; as shown in R (left) and in SAGA GIS (right).
which shows that the variogram is close to pure nugget effect (Fig. 5.10, right)18 . We can still interpolate the residuals using ordinary kriging:
> lime.rk <- krige(residuals(step.lime) [using ordinary kriging]

1, meuse.ov, pc.comps, lime.rvgm)
and then add back to the predicted regression part of the model:
> > # > > + + +
lime.rk$var1.rk <- lime.glm$lime + lime.rk$var1.pred lime.rk$var1.rko <- exp(lime.rk$var1.rk)/(1 + exp(lime.rk$var1.rk)) write to a GIS format: write.asciigrid(lime.rk["var1.rko"], "lime_rk.asc", na.value=-1) lime.plt <- spplot(lime.rk["var1.rko"], scales=list(draw=T), at=seq(0.05, 1, 0.05), col.regions=grey(rev(seq(0, 0.95, 0.05))), main="Liming requirements", sp.layout=list("sp.polygons", col="black", meuse.riv))
After you export the resulting map to GIS, a first step is to visually explore the maps to see how well the predicted values match the field observations (Fig. 5.11). Note that the map has problems predicting the right class at several isolated locations. To estimate the accuracy of predicting the right class, we can use:
SAGA
> > > > >
lime.ov <- overlay(lime.rk, meuse) lime.ov@data <- cbind(lime.ov@data, meuse["lime"]@data) library(mda) library(vcd) # kappa statistics Kappa(confusion(lime.ov$lime, as.factor(round(lime.ov$var1.rko, 0)))) value ASE Unweighted 0.678 0.0671 Weighted 0.678 0.0840
which shows that in 68% of cases the predicted liming requirement class matches the field records.
18 The higher the nugget, the more the algorithm will smooth the residuals. In the case of pure nugget effect, it does not make any difference if we use only results of regression, or if we add interpolated residuals to the regression predictions.
136
First steps (meuse)
5.5 Advanced exercises
5.5.1 Geostatistical simulations A problem with kriging is that it over-smooths reality; especially processes that exhibits a nugget effect in the variogram model. The kriging predictor is the “best linear unbiased predictor” (BLUP) at each point, but the resulting field is commonly smoother than in reality (recall Fig. 1.4). This causes problems when running distributed models, e.g. erosion and runoff, and also gives a distorted view of nature to the decision-maker. A more realistic visualization of reality is achieved by the use of conditional geostatistical simulations: the sample points are taken as known, but the interpolated points reproduce the variogram model including the local noise introduced by the nugget effect. The same krige method in can be used to generate simulations, by specifying the optional nsim (“number of simulations”) argument. It’s interesting to create several ‘alternate realities’, each of which is equally-probable. We can re-set ’s random number generator with the set.seed method to ensure that the simulations will be generated with the same random number seed19 , and then generate four realizations:
gstat R
> set.seed(25) > zinc.rksim <- krige(step.zinc$call$formula, meuse.ov, pc.comps, + zinc.rvgm, nsim=4, nmax=100) drawing 4 GLS realisations of beta... [using conditional Gaussian simulation]
Now back-transform the predicted values, and plot all four simulations together:
# > > > > +
back-transform the values: for(i in 1:length(zinc.rksim@data)){ zinc.rksim@data[,i] <- expm1(zinc.rksim@data[,i]) } spplot(zinc.rksim, col.regions=grey(c(rev(seq(0,0.97,1/length(at.zinc))),0)), at=at.zinc, main="RK simulations of log(zinc)")
RK simulations of log(zinc)
sim1 sim2 sim3 sim4
1800 1600 1400 1200 1000 800 600 400 200
Fig. 5.12: Four simulations of zinc using the fitted regression kriging model. Back-transformed to original scale (ppm). Compare with Fig. 5.9.
which shows that the general pattern of the zinc distribution repeats in each simulation (Fig. 5.12). However, we can also notice that some small features are not as clear as they look in Fig. 5.9. For example, it is relatively hard to notice the borders of the soil units, which in this case change from simulation to simulation. This confirms that the best predictor of zinc is the distance to the river (dist.asc map). To produce simulations of liming requirements, we can run (Bivand et al., 2008, p.230):
19
Hence differences between the simulated realizations are due only to the different values of the model parameters.
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OK simulations of lime
sim1 sim2 sim3 sim4
1.0
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0.6
0.4
0.2
0.0
Fig. 5.13: Four simulations of liming requirements (indicator variable) using ordinary kriging. Compare with Fig. 5.11.
# fit a variogram: > lime.ovgm <- fit.variogram(variogram(I(lime == 1) ∼ 1, meuse), vgm(1, "Sph", 800, 1)) > lime.sim <- krige(I(lime == 1) ∼ 1, meuse, meuse.grid, lime.ovgm, + nsim=4, indicators=TRUE, nmax=40) drawing 4 GLS realisations of beta... [using conditional indicator simulation] > spplot(lime.sim, col.regions=grey(c(rev(seq(0,0.9,0.05)), 0)) + main="OK simulations of lime")
the result is shown in Fig. 5.13. Note that in the case of liming requirements, there is a distinct difference between the variogram of the original variable and of the residuals: most of spatially-correlated variation in the lime requirements can be explained with auxiliary predictors, so that the variogram of residuals shows pure nugget effect (Fig. 5.10, right). 5.5.2 Spatial prediction using
SAGA GIS
gstat SAGA
Although has relatively limited geostatistical functionality, it contains a number of modules that are of interest for geostatistical mapping: multiple linear regression (points and grids), ordinary kriging, and regression-kriging. To start, we can examine if the algorithms implemented in and are the same. We can use the same model parameters estimated in section 5.3 to produce predictions of log1p(zinc). First, we need to prepare vector and gridded maps in format:
SAGA
SAGA
# > > # > > > > > +
export the point data (transformed!): meuse.ov$log1p_zinc <- log1p(meuse.ov$zinc) writeOGR(meuse.ov["log1p_zinc"], ".", "zinc", "ESRI Shapefile") export the grids to SAGA format: PCs.list <- names(step.zinc$model)[-1] for(i in PCs.list){ write.asciigrid(pc.comps[i], paste(i, ".asc", sep=""), na.value=-9999) } rsaga.esri.to.sgrd(in.grids=set.file.extension(PCs.list, ".asc"), out.sgrds=set.file.extension(PCs.list, ".sgrd"), in.path=getwd())
and then make the predictions by using the Universal kriging module in
SAGA (Fig. 5.14):
# regression-kriging using the same parameters fitted previously: > gridcell <- pc.comps@grid@cellsize[1]
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First steps (meuse)
> rsaga.geoprocessor(lib="geostatistics_kriging", module=8, + param=list(GRID="zinc_rk_SAGA.sgrd", SHAPES="zinc.shp", + GRIDS=paste(set.file.extension(PCs.list, ".sgrd"), collapse=";", sep=""), + BVARIANCE=F, BLOCK=F, FIELD=1, BLOG=F, MODEL=1, TARGET=0, + USER_CELL_SIZE=gridcell, NUGGET=zinc.rvgm$psill[1], SILL=zinc.rvgm$psill[2], + RANGE=zinc.rvgm$range[2], INTERPOL=0, + USER_X_EXTENT_MIN=pc.comps@bbox[1,1]+gridcell/2, + USER_X_EXTENT_MAX=pc.comps@bbox[1,2]-gridcell/2, + USER_Y_EXTENT_MIN=pc.comps@bbox[2,1]+gridcell/2, + USER_Y_EXTENT_MAX=pc.comps@bbox[2,2]-gridcell/2)) SAGA CMD 2.0.4 library path: library name: module name : author :
C:/Progra∼1/saga_vc/modules geostatistics_kriging Universal Kriging (Global) (c) 2003 by O.Conrad
Load shapes: zinc.shp... ready Load grid: PC1.sgrd... ready ... Load grid: PC6.sgrd... ready Parameters Grid: [not set] Variance: [not set] Points: zinc.shp Attribute: log1p_zinc Create Variance Grid: no Target Grid: user defined Variogram Model: Exponential Model Block Kriging: no Block Size: 100.000000 Logarithmic Transformation: no Nugget: 0.054588 Sill: 0.150518 Range: 374.198454 Linear Regression: 1.000000 Exponential Regression: 0.100000 Power Function - A: 1.000000 Power Function - B: 0.500000 Grids: 5 objects (PC1.sgrd, PC2.sgrd, PC3.sgrd, PC4.sgrd, PC6.sgrd)) Grid Interpolation: Nearest Neighbor Save grid: zinc_rk_SAGA.sgrd...
Visually (Fig. 5.14), the results look as if there is no difference between the two pieces of software. We can then load back the predictions into to compare if the results obtained with and match exactly:
R
gstat
SAGA
> + > > >
rsaga.sgrd.to.esri(in.sgrds="zinc_rk_SAGA", out.grids="zinc_rk_SAGA.asc", out.path=getwd()) zinc.rk$SAGA <- readGDAL("zinc_rk_SAGA.asc")$band1 plot(zinc.rk$SAGA, zinc.rk$var1.pred, pch=19, xlab="SAGA", ylab="gstat") lines(3:8, 3:8, col="grey", lwd=4)
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which shows that both software programs implement the same algorithm, but there are some small differences between the predicted values that are possibly due to rounding effect.
Fig. 5.14: Comparing results from SAGA (left map) and parameters estimated in section 5.3.
gstat (right map):
regression-kriging using the same model
Next, we want to compare the computational efficiency of and , i.e. the processing time. To emphasize the difference in computation time, we will use a somewhat larger grid (2 m), and then re-run ordinary kriging in both software packages:
gstat
SAGA
> meuse.grid2m <- readGDAL("topomap2m.tif") topomap2m.tif has GDAL driver GTiff and has 1664 rows and 1248 columns > proj4string(meuse.grid2m) <- meuse.grid@proj4string
Processing speed can be measured from seconds:
R by using the system.time method, which measures the elapsed
> system.time(krige(log1p(zinc) [using ordinary kriging] user system elapsed 319.14 7.96 353.44
and now the same operation in

1, meuse, meuse.grid2m, zinc.vgm))
SAGA:
> cellsize2 <- meuse.grid2m@grid@cellsize[1] > system.time(rsaga.geoprocessor(lib="geostatistics_kriging", module=6, + param=list(GRID="zinc_ok_SAGA.sgrd", SHAPES="zinc.shp", BVARIANCE=F, BLOCK=F, + FIELD=1, BLOG=F, MODEL=1, TARGET=0, USER_CELL_SIZE=cellsize2, + NUGGET=zinc.vgm$psill[1], SILL=zinc.vgm$psill[2], RANGE=zinc.rvgm$range[2], + USER_X_EXTENT_MIN=meuse.grid2m@bbox[1,1]+cellsize2/2, + USER_X_EXTENT_MAX=meuse.grid2m@bbox[1,2]-cellsize2/2, + USER_Y_EXTENT_MIN=meuse.grid2m@bbox[2,1]+cellsize2/2, + USER_Y_EXTENT_MAX=meuse.grid2m@bbox[2,2]-cellsize2/2))) user 0.03 system elapsed 0.71 125.69
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First steps (meuse)
We can see that will be faster for processing large data sets. This difference will become even larger if we would use large point data sets. Recall that the most ‘expensive’ operation for any geostatistical mapping is the derivation of distances between points. Thus, by limiting the search radius one can always increase the processing speed. The problem is that a software needs to initially estimate which points fall within the search radius, hence it always has to take into account location of all points. Various quadtree and similar algorithms then exist to speed up the neighborhood search algorithm (partially available in also), but their implementation can differ between various programming languages. Note also that it is not really a smart idea to try to visualize large maps in . graphics plots grids as vectors; each grid cell is plotted as a separate polygon, which takes a huge amount of RAM for large grids, and can last up to few minutes. on other hand can handle and display grids 10 million pixels on a standard PC without any delays (Fig. 5.14). When you move to other exercises you will notice that we will typically use to fit models, to run predictions and visualize results, and to visualize and explore final products.
SAGA
gstat
RR
R
SAGA SAGA
Google Earth
5.5.3
Geostatistical analysis in
geoR
We start by testing the variogram fitting functionality of . However, before we can do any analysis, we need to convert our point map ( ) to geodata format:
sp
geoR
geoR
> zinc.geo <- as.geodata(meuse.ov["zinc"]) > str(zinc.geo) List of 2 $ x , y : num [1:155, 1:2] 181072 181025 181165 181298 181307 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:155] "300" "455" "459" "540" ... .. ..$ : chr [1:2] "x" "y" $ data : num [1:155] 1022 1141 640 257 269 ... - attr(*, "class")= chr "geodata"
0° ° 45° ° 90° ° 135° °
1.0
semivariance
0.8
semivariance
0.6
0.8
1.0
1.2
1.4
Fitted variogram (ML)
q
q
q q q q
0.6
q q q
0.4
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q q
0.2
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q q
0.0
0
500 distance
1000
0.0 0
500 distance
1000
Fig. 5.15: Anisotropy (left) and variogram model fitted using the Maximum Likelihood (ML) method (right). The confidence bands (envelopes) show the variability of the sample variogram estimated using simulations from a given set of model parameters.
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which shows much simpler structure than a SpatialPointsDataFrame. A geodata-type object contains only: a matrix with coordinates of sampling locations (coords), values of target variables (data), matrix with coordinates of the polygon defining the mask map (borders), vector or data frame with covariates (covariate). To produce the two standard variogram plots (Fig. 5.15), we will run:
> # > # > > + >
par(mfrow=c(1,2)) anisotropy ("lambda=0" indicates log-transformation): plot(variog4(zinc.geo, lambda=0, max.dist=1500, messages=FALSE), lwd=2) fit variogram using likfit: zinc.svar2 <- variog(zinc.geo, lambda=0, max.dist=1500, messages=FALSE) zinc.vgm2 <- likfit(zinc.geo, lambda=0, messages=FALSE, ini=c(var(log1p(zinc.geo$data)),500), cov.model="exponential") zinc.vgm2 likfit: estimated model parameters: beta tausq sigmasq phi " 6.1553" " 0.0164" " 0.5928" "500.0001" Practical Range with cor=0.05 for asymptotic range: 1498 likfit: maximised log-likelihood = -1014
# generate confidence bands for the variogram: > env.model <- variog.model.env(zinc.geo, obj.var=zinc.svar2, model=zinc.vgm2) variog.env: variog.env: variog.env: variog.env: generating 99 simulations (with 155 points each) using grf adding the mean or trend computing the empirical variogram for the 99 simulations computing the envelops
> plot(zinc.svar2, envelope=env.model); lines(zinc.vgm2, lwd=2); > legend("topleft", legend=c("Fitted variogram (ML)"), lty=c(1), lwd=c(2), cex=0.7) > dev.off()
where variog4 is a method that generates semivariances in four directions, lambda=0 is used to indicate the type of transformation20 , likfit is the generic variogram fitting method, ini is the given initial variogram, and variog.model.env calculates confidence limits for the fitted variogram model. Parameters tausq and sigmasq corresponds to nugget and sill parameters; phi is the range parameter. In general, offers much richer possibilities for variogram modeling than . From Fig. 5.15(right) we can see that the variogram fitted using this method does not really go through all points (compare with Fig. 5.8). This is because the ML method discounts the potentially wayward influence of sample variogram at large inter-point distances (Diggle and Ribeiro Jr, 2007). Note also that the confidence bands (envelopes) also confirm that the variability of the empirical variogram increases with larger distances. Now that we have fitted the variogram model, we can produce predictions using the ordinary kriging model. Because does not work with objects, we need to prepare the prediction locations:
geoR
gstat
geoR
sp
> locs <- pred_grid(c(pc.comps@bbox[1,1]+gridcell/2, + pc.comps@bbox[1,2]-gridcell/2), c(pc.comps@bbox[2,1]+gridcell/2, + pc.comps@bbox[2,2]-gridcell/2), by=gridcell) # match the same grid as pc.comps;
and the mask map i.e. a polygon showing the borders of the area of interest:
> > # > >
meuse.grid$mask <- ifelse(!is.na(meuse.grid$dist), 1, NA) write.asciigrid(meuse.grid["mask"], "mask.asc", na.value=-1) raster to polygon conversion; rsaga.esri.to.sgrd(in.grids="mask.asc", out.sgrd="mask.sgrd", in.path=getwd()) rsaga.geoprocessor(lib="shapes_grid", module=6, param=list(GRID="mask.sgrd",
20 geoR implements the Box–Cox transformation (Diggle and Ribeiro Jr, 2007, p.61), which is somewhat more generic than simple log() transformation.
142
First steps (meuse)
+ > + # > >
SHAPES="mask.shp", CLASS_ALL=1)) mask <- readShapePoly("mask.shp", proj4string=CRS("+init=epsg:28992"), force_ring=T) coordinates of polygon defining the area of interest: mask.bor <- mask@polygons[[1]]@Polygons[[1]]@coords str(mask.bor) num [1:267, 1:2] 178880 178880 178760 178760 178720 ...
Ordinary kriging can be run by using the generic method for linear Gaussian models krige.conv21 :
> zinc.ok2 <- krige.conv(zinc.geo, locations=locs, + krige=krige.control(obj.m=zinc.vgm2), borders=mask.bor) krige.conv: krige.conv: krige.conv: krige.conv: krige.conv: results will be returned only for locations inside the borders model with constant mean performing the Box-Cox data transformation back-transforming the predicted mean and variance Kriging performed using global neighborhood
# Note: geoR will automatically back-transform the values! > str(zinc.ok2) List of 6 $ predict : num [1:3296] 789 773 756 740 727 ... $ krige.var : num [1:3296] 219877 197718 176588 159553 148751 ... $ beta.est : Named num 6.16 ..- attr(*, "names")= chr "beta" $ distribution: chr "normal" $ message : chr "krige.conv: Kriging performed using global neighbourhood" $ call : language krige.conv(geodata = zinc.geo, locations = locs, borders = mask.bor, krige = krige.control(obj.m = zinc.vgm2)) - attr(*, "sp.dim")= chr "2d" - attr(*, "prediction.locations")= symbol locs - attr(*, "parent.env")=<environment: R_GlobalEnv> - attr(*, "data.locations")= language zinc.geo$coords - attr(*, "borders")= symbol mask.bor - attr(*, "class")= chr "kriging"
To produce plots shown in Fig. 5.16, we use:
> > + > > > + > > + > + + >
par(mfrow=c(1,2)) image(zinc.ok2, loc=locs, col=gray(seq(1,0.1,l=30)), xlab="Coord X", ylab="Coord Y") title(main="Ordinary kriging predictions") contour(zinc.ok2, add=TRUE, nlev=8) image(zinc.ok2, loc=locs, value=sqrt(zinc.ok2$krige.var), col=gray(seq(1,0.1,l=30)), xlab="Coord X", ylab="Coord Y") title(main="Prediction error") krige.var.vals <- round(c(quantile(sqrt(zinc.ok2$krige.var),0.05), sd(zinc.geo$data), quantile(sqrt(zinc.ok2$krige.var), 0.99)), 0) legend.krige(x.leg=c(178500,178800), y.leg=c(332500,333500), values=sqrt(zinc.ok2$krige.var), vert=TRUE, col=gray(seq(1,0.1,l=30)), scale.vals=krige.var.vals) points(zinc.geo[[1]], pch="+", cex=.7)
To run regression-kriging (in original geodata object:
21
geoR “external trend kriging”) we first need to add values of covariates to the
Meaning “kriging conventional” i.e. linear kriging.
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Fig. 5.16: Zinc predicted using ordinary kriging in geoR. The map on the left is considered to be below critical accuracy level in the areas where the prediction error (right map) exceeds the global variance (the middle value in legend). Compare with Fig. 5.9.
> zinc.geo$covariate <- meuse.ov@data[,PCs.list]
which now allows us to incorporate the trend argument in the variogram model:
# trend model: > step.zinc$call$formula[c(1,3)]

PC1 + PC2 + PC3 + PC4 + PC6
> zinc.rvgm2 <- likfit(zinc.geo, lambda=0, trend=step.zinc$call$formula[c(1,3)], + messages=FALSE, ini=c(var(residuals(step.zinc)),500), cov.model="exponential") > zinc.rvgm2 likfit: estimated model parameters: beta0 beta1 beta2 beta3 beta4 beta5 " 5.6919" " -0.4028" " -0.1203" " -0.0176" " 0.0090" " -0.3199" tausq sigmasq phi " 0.0526" " 0.1894" "499.9983" Practical Range with cor=0.05 for asymptotic range: 1498 likfit: maximised log-likelihood = -975
Note that reports also the regression coefficients for the five predictors (beta0 is the intercept). In the case of this information will be hidden: will typically fit a regression model only to derive the residuals (regression coefficients can be printed by setting the debugging options). Neither nor report on the goodness of fit and similar regression diagnostics. Before we can make predictions, we also need to prepare the covariates at all locations. Unfortunately, is not compatible with grids, so we need to prepare the covariate values so they exactly match prediction locations:
geoR gstat
gstat
gstat
geoR
geoR
sp
# get values of covariates at new locations: > locs.sp <- locs > coordinates(locs.sp) <- ∼ Var1+Var2
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First steps (meuse)
> # > > >
PCs.gr <- overlay(pc.comps, locs.sp) fix NAs: for(i in PCs.list){ PCs.gr@data[,i] <- ifelse(is.na(PCs.gr@data[,i]), 0, PCs.gr@data[,i]) }
which allows us to run predictions using the same trend model as used in section 5.3.1:
# > + + # >
define the geostatistical model: KC <- krige.control(trend.d = step.zinc$call$formula[c(1,3)], trend.l = ∼ PCs.gr$PC1+PCs.gr$PC2+PCs.gr$PC3+PCs.gr$PC4+PCs.gr$PC6, obj.m = zinc.rvgm2) run predictions (external trend kriging): zinc.rk2 <- krige.conv(zinc.geo, locations=locs, krige=KC, borders=mask.bor) krige.conv: krige.conv: krige.conv: krige.conv: krige.conv: results will be returned only for prediction inside the borders model with mean defined by covariates provided by the user performing the Box-Cox data transformation back-transforming the predicted mean and variance Kriging performed using global neighbourhood
Fig. 5.17: Zinc predicted using external trend kriging in with Figs. 5.9 and 5.12.
geoR (left); simulations using the same model (right). Compare
The result is shown in Fig. 5.17. also allows generation of simulations using the same external trend model by setting the output.control parameter (the resulting map shown in Fig. 5.17; right):
geoR
> zinc.rk2 <- krige.conv(zinc.geo, locations=locs, krige=KC, borders=mask.bor, + output=output.control(n.predictive=1)) krige.conv: krige.conv: krige.conv: krige.conv: krige.conv: krige.conv: krige.conv: results will be returned only for prediction inside the borders model with mean defined by covariates provided by the user performing the Box-Cox data transformation sampling from the predictive distribution (conditional simulations) back-transforming the simulated values back-transforming the predicted mean and variance Kriging performed using global neighborhood
5.6 Visualization of generated maps
145
which shows a somewhat higher range of values than the simulation using a simple linear model (Fig. 5.12). In this case seems to do better in accounting for the skewed distribution of values than . However such simulations in are extremely computationally intensive, and are not recommended for large data sets. In fact, many default methods implemented in (Maximum Likelihood fitting for variograms, Bayesian methods and conditional simulations) are definitively not recommended with data sets with 1000 sampling points and/or over 100,000 new locations. Creators of seem to have selected a path of running only global neighborhood analysis on the point data. Although the author of this guide supports that decision (see also section 2.2), some solution needs to be found to process larger point data sets because computing time exponentially increases with the size of the data set. Finally, the results of predictions can be exported22 to some GIS format by copying the values to an frame:
geoR
geoR
gstat
geoR
geoR
sp
> > > + + > > >
mask.ov <- overlay(mask, locs.sp) mask.sel <- !is.na(mask.ov$MASK.SGRD) locs.geo <- data.frame(X=locs.sp@coords[mask.sel,1], Y=locs.sp@coords[mask.sel,2], zinc.rk2=zinc.rk2[[1]], zinc.rkvar2=zinc.rk2[[2]]) coordinates(locs.geo) <- ∼ X+Y gridded(locs.geo) <- TRUE write.asciigrid(locs.geo[1], "zinc_rk2.asc", na.value=-1)
5.6
Visualization of generated maps
5.6.1 Visualization of uncertainty
The following paragraphs explain how to visualize results of geostatistical mapping to explore uncertainty in maps. We will focus on the technique called whitening, which is a simple but efficient technique to visualize mapping error (Hengl and Toomanian, 2006). It is based on the Hue-Saturation-Intensity (HSI) color model (Fig. 5.18a) and calculations with colors using the color mixture (CM) concept. The HSI is a psychologically appealing color model — hue is used to visualize values or taxonomic space and whiteness (paleness) is used to visualize the uncertainty (Dooley and Lavin, 2007). For this purpose, a 2D legend was designed to accompany the visualizations. Unlike standard legends for continuous variables, this legend has two axis (Fig. 5.18b): (1) vertical axis (hues) is used to visualize the predicted values and (2) horizontal axis (whiteness) is used to visualize the prediction error. Fig. 5.19 shows an example of visualization using whitening for the meuse data set. Visualization of uncertainty in maps using whitening can be achieved using one of the two software programs: and . In , you can use the VIS_error script that can be obtained from the author’s homepage. To visualize the uncertainty for your own case study using this technique, you should follow these steps (Hengl and Toomanian, 2006):
ILWIS
R
ILWIS
(1.) Download the ILWIS script (VIS_error23 ) for visualization of prediction error and unzip it to the default directory (C:\Program Files\ILWIS\Scripts\). (2.) Derive the predictions and prediction variance for some target variable. Import both maps to . The prediction variance needs to be then converted to normalized prediction variance by using Eq.(1.4.4), so you will also need to determine the global variance of your target variable. (3.) Start and run the script from the left menu (operations list) or from the main menu → Operations → Scripts → VIS_error. Use the help button to find more information about the algorithm. (4.) To prepare final layouts, you will need to use the legend2D.tif legend file24 . A more interesting option is to visualize maps using whitening in additional package:
22 23 24 25
ILWIS
ILWIS
R25 . You will need to load the following
Note that the results of prediction in geoR is simply a list of values without any spatial reference.
http://spatial-analyst.net/scripts/ http://spatial-analyst.net/scripts/legend2D.tif; This legend is a Hue-whitening legend: in the vertical direction only http://spatial-analyst.net/scripts/whitening.R
Hue values change, while in the horizontal direction amount of white color is linearly increased from 0.5 up to 1.0.
146
First steps (meuse)
> library(colorspace)
The example with the meuse data set:
# ordinary kriging: > m <- vgm(.59, "Sph", 874, .04) > vismaps <- krige(log(zinc) ∼ 1, meuse, meuse.grid, model=m)
As a result of ordinary kriging, we have produced two maps: predictions and the prediction variance. Now, we can visualize both maps together using whitening. We start by setting up threshold levels (lower and upper limits), and stretching the values within that range:
> > > > > > # > # # > > > # # > > # > > > > > > > >
z1 <- min(log(meuse$zinc), na.rm=TRUE) z2 <- max(log(meuse$zinc), na.rm=TRUE) e1 <- 0.4 e2 <- 0.7 vismaps$er <- sqrt(vismaps$e)/sqrt(var(log(meuse$zinc))) vismaps$tmpz <- (vismaps$z-z1)/(z2-z1) Mask the values outside the 0-1 range: vismaps$tmpzc <- ifelse(vismaps$tmpz<=0, 0, ifelse(vismaps$tmpz>1, 1, vismaps$tmpz))
The Hue-Saturation-Value (HSV) bands we can generate using:
The hues should lie between between 0 and 360, and the saturations and values should lie between 0 and 1. vismaps$tmpf1 <- -90-vismaps$tmpzc*300 vismaps$tmpf2 <- ifelse(vismaps$tmpf1<=-360, vismaps$tmpf1+360, vismaps$tmpf1) vismaps$H <- ifelse(vismaps$tmpf2>=0, vismaps$tmpf2, (vismaps$tmpf2+360)) Strech the error values (e) to the inspection range: Mask the values out of the 0-1 range: vismaps$tmpe <- (vismaps$er-e1)/(e2-e1) vismaps$tmpec <- ifelse(vismaps$tmpe<=0, 0, ifelse(vismaps$tmpe>1, 1, vismaps$tmpe)) Derive the saturation and intensity images: vismaps$S <- 1-vismaps$tmpec vismaps$V <- 0.5*(1+vismaps$tmpec)
The HSV values can be converted to RGB bands using:
RGBimg <- as(HSV(vismaps$H, vismaps$S, vismaps$V), "RGB") summary(RGBimg@coords) vismaps$red <- as.integer(ifelse(is.na(vismaps@data[1]), 255, RGBimg@coords[,1]*255)) vismaps$green <- as.integer(ifelse(is.na(vismaps@data[1]), 255, RGBimg@coords[,2]*255)) vismaps$blue <- as.integer(ifelse(is.na(vismaps@data[1]), 255, RGBimg@coords[,3]*255)) summary(vismaps[c("red", "green", "blue")]) Object of class SpatialGridDataFrame Coordinates: min max x 178440 181560 y 329600 333760 Is projected: NA proj4string : [NA] Number of points: 2 Grid attributes: cellcentre.offset cellsize cells.dim x 178460 40 78 y 329620 40 104 Data attributes: red green blue Min. : 0.0 Min. : 0.0 Min. : 0.0 1st Qu.:153.0 1st Qu.:183.0 1st Qu.:194.0 Median :255.0 Median :255.0 Median :255.0 Mean :206.2 Mean :220.5 Mean :219.2 3rd Qu.:255.0 3rd Qu.:255.0 3rd Qu.:255.0 Max. :255.0 Max. :255.0 Max. :255.0
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Fig. 5.18: Design of the special 2D legend used to visualize the prediction variance using whitening: (a) the HSI color model, (b) the 2D legend and (c) the common types of Hues. After Hengl et al. (2004a).
178440 333760
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70%
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ordinary kriging
40% 7.51
universal kriging
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329640 178440
329640 181560
Fig. 5.19: Mapping uncertainty for zinc visualized using whitening: ordinary kriging (left) and universal kriging (right). Predicted values in log-scale. See cover of this book for a color version of this figure.
148 which is now a spatial object with three RGB bands. To display a true RGB image in method26 :
First steps (meuse)
R, use the SGDF2PCT
> > > >
RGBimg <- SGDF2PCT(vismaps[c("red", "green", "blue")], ncolors=256, adjust.bands=FALSE) vismaps$idx <- RGBimg$idx image(vismaps, "idx", col=RGBimg$ct) plot(meuse, pch="+", add=TRUE)
In the last step (optional), we can set the right georeference and export the map to e.g. GeoTIFF format:
> proj4string(vismaps) <- CRS("+init=epsg:28992") # Export as geoTIFF / or any other format: > writeGDAL(vismaps[c("red", "green", "blue")], "vismap.tif", drivername="GTiff", + type="Byte", options="INTERLEAVE=PIXEL")
A comparison of uncertainty for maps produced using ordinary kriging and universal kriging in can be seen in Fig. 5.19. In this case, the universal kriging map is distinctly more precise. You can manually change the lower and upper values for both prediction and error maps depending on your mapping requirements. By default, thresholds of 0.4 and 0.8 (max 1.0) are used for the normalized prediction error values. This assumes that a satisfactory prediction is when the model explains more than 85% of the total variation (normalized error = 40%; see p.23). Otherwise, if the value of the normalized error get above 80%, the model accounts for less than 50% of variability at calibration points and the prediction is probably unsatisfactory. To prepare the 2D legend shown in Fig. 5.19 (100×100 pixels), we use:
gstat
> # > > + > # > # > > > > > > > # > + > > +
legend.2D <- expand.grid(x=seq(.01,1,.01),y=seq(.01,1,.01)) Hues legend.2D$tmpf1 <- -90-legend.2D$y*300 legend.2D$tmpf2 <- ifelse(legend.2D$tmpf1<=-360, legend.2D$tmpf1+360, legend.2D$tmpf1) legend.2D$H <- ifelse(legend.2D$tmpf2>=0, legend.2D$tmpf2, (legend.2D$tmpf2+360)) Saturation: legend.2D$S <- 1-legend.2D$x Intensity: legend.2D$V <- 0.5+legend.2D$x/2 gridded(legend.2D) <- ∼ x+y legend.2D <- as(legend.2D, "SpatialGridDataFrame") legendimg <- as(HSV(legend.2D$H, legend.2D$S, legend.2D$V), "RGB") legend.2D$red <- as.integer(legendimg@coords[,1]*255) legend.2D$green <- as.integer(legendimg@coords[,2]*255) legend.2D$blue <- as.integer(legendimg@coords[,3]*255) Write as a RGB image: legend.2Dimg <- SGDF2PCT(legend.2D[c("red", "green", "blue")], ncolors=256, adjust.bands=FALSE) legend.2D$idx <- legend.2Dimg$idx writeGDAL(legend.2D[c("red", "green", "blue")], "legend2D.tif", drivername="GTiff", type="Byte", options="INTERLEAVE=PIXEL")
Another sophisticated option to visualize the results of (spatio-temporal) geostatistical mapping is the 27 stand-alone visualization software called (Pebesma et al., 2007). Aquila facilitates interactive exploration of the spatio-temporal Cumulative Distribution Functions (CDFs) and allows decision makers to explore uncertainty associated with attaching different threshold or its spatial distribution in the area of interest. It is actually rather simple to use — one only needs to prepare a sample (e.g. 12 slices) of quantile estimates, which are then locally interpolated to produce CDFs.
Aquila
5.6.2 To export maps we have produced to nate system (the native system for
Export of maps to
Google Earth
Google Earth, we first need to reproject the maps to the WGS84 coordiGoogle Earth). We can first reproject the map of sample points, using the
26 Note that the results might differ slightly between ILWIS and R, which is mainly due to somewhat different HSI-RGB conversion algorithms. For example, the SGDF2PCT method is limited to 256 colors only! 27
http://pcraster.geo.uu.nl/projects/aguila/
5.6 Visualization of generated maps
149
spTransform method of the sp package, and then export them using writeOGR: > meuse.ll <- spTransform(meuse, CRS("+proj=longlat +datum=WGS84")) > writeOGR(meuse.ll, "meuse.kml", "meuse", driver="KML")
You can examine these in by opening the KML file meuse.kml which you just wrote. Next, we want to export the predictions of zinc, which means that we first need to reproject the interpolated values onto geographic coordinates. The most efficient way to achieve this is by using the proj4 module28 :
Google Earth
SAGA
> rsaga.geoprocessor(lib="pj_proj4", 2, param=list(SOURCE_PROJ=paste('"', + proj4string(meuse.grid), '"', sep=""), TARGET_PROJ="\"+proj=longlat + +datum=WGS84\"", SOURCE="zinc_rk.sgrd", TARGET="zinc_rk_ll.sgrd", + TARGET_TYPE=0, INTERPOLATION=1)) SAGA CMD 2.0.4 library path: library name: module name : author :
C:/Progra∼1/saga_vc/modules pj_proj4 Proj.4 (Command Line Arguments, Grid) O. Conrad (c) 2004-8
Load grid: zinc_rk.sgrd... ready Parameters Inverse: no Source Projection Parameters: +init=epsg:28992 +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.237,50.0087,465.658,-0.406857, 0.350733,-1.87035,4.0812 +units=m +no_defs Target Projection Parameters: +proj=longlat +datum=WGS84 Grid system: 40; 77x 104y; 178500x 329620y Source: zinc_rk.sgrd Target: [not set] Shapes: [not set] X Coordinates: [not set] Y Coordinates: [not set] Create X/Y Grids: no Target: user defined Interpolation: Bilinear Interpolation Source: +init=epsg:28992 +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.237,50.0087,465.658,-0.406857,0.350733, -1.87035,4.0812 +units=m +no_defs Target: +proj=longlat +datum=WGS84 ready Save grid: zinc_rk_ll.sgrd...
Once we have created this gridded result, we can plot the maps and export the plots to . First we need to set up metadata in the form of a SpatialGrid object for defining the size and placing of a PNG image overlay in ; this is the job of the GE_SpatialGrid method of the package:
Google Earth
Google Earth maptools
# read back into R: > rsaga.sgrd.to.esri(in.sgrds="zinc_rk_ll.sgrd", out.grids="zinc_rk_ll.asc", + out.path=getwd()) > zinc_rk.ll <- readGDAL("zinc_rk_ll.asc")
28 SAGA will automatically estimate both the grid cell size and the bounding box in geographical coordinates. Compare with section 10.6.3.
150
First steps (meuse)
zinc_rk_ll.asc has GDAL driver AAIGrid and has 105 rows and 122 columns > proj4string(zinc_rk.ll) <- CRS("+proj=longlat +datum=WGS84") > zinc_rk.kml <- GE_SpatialGrid(zinc_rk.ll)
Fig. 5.20: RK predictions of zinc for Meuse area — as visualized in Google
Earth.
where zinc_rk.kml is the name of object, which carries only a definition of the ground overlay frame and not the data to be exported. Next we create a PNG (Portable Network Graphics) file (the format recognized as an overlay by ) using the png graphics device:
R
Google Earth
> png(file="zinc_rk.png", width=zinc_rk.kml$width, height=zinc_rk.kml$height, bg="transparent") > par(mar=c(0,0,0,0), xaxs="i", yaxs="i") > image(as.image.SpatialGridDataFrame(zinc_rk.ll[1]), + col=grey(rev(seq(0,0.95,1/length(at.zinc)))), + xlim=zinc_rk.kml$xlim, ylim=zinc_rk.kml$ylim)
which will plot the map over the whole area of the plotting space, so that border coordinates exactly match the borders of the plot. We then create the overlay itself, from the PNG file, with the kmlOverlay method, specifying the SpatialGrid object that orients the map in :
Google Earth
> kmlOverlay(zinc_rk.kml, kmlfile="zinc_rk.kml", imagefile="zinc_rk.png", name="zinc") [1] "<?xml version='1.0' encoding='UTF-8'?>" [2] "<kml xmlns='http://earth.google.com/kml/2.0'>" [3] "<GroundOverlay>"
5.6 Visualization of generated maps
151
[4] [5] [6] + [7]
"<name>zinc (RK)</name>" "<Icon><href>zinc_rk.png</href><viewBoundScale>0.75</viewBoundScale></Icon>" "<LatLonBox><north>50.992973</north><south>50.955488</south> <east>5.76502299201062</east><west>5.721466</west></LatLonBox>" "</GroundOverlay></kml>"
> dev.off()
When you open the resulting KML in , you will see a display shown in Fig. 5.20. This allows you to orient yourself and make an interpretation of the produced maps. Open the final map in and visually explore how many areas next to the populated areas show high concentrations of zinc.
Google Earth
Google Earth
Self-study exercises: (1.) How many pixels in the meuse.grid are available for spatial prediction? (HINT: Number of pixels that do not have missing values for any of the variables.) (2.) What is the correlation coefficient between maps dist and ahn? (HINT: use the cor method.) Is the default Pearson’s parametric correlation appropriate? (HINT: Make a scatterplot of the two maps using the plot method. Compute also a non-parametric Spearman correlation.) (3.) How much of the variation in Zinc does the RK model explains, and which are the most significant predictors? (HINT: Look at the R-square and the Pr(>|t|).) (4.) Up to which distance from the points are the predictions improved by the ordinary kriging model (rather than just by using an average of all the observations)? (HINT: look at the original variance, and then find at which distance does the semivariance exceeds the original variance.) (5.) Up to which distance is zinc spatially auto-correlated based on this model? Provide your answer. (6.) Is there significant difference in the accuracy of the predictions between OK and RK? (7.) Up to which distance from the points are the predictions improved by the model? (HINT: At which distance does the regression-kriging prediction variance exceed the global variance?) (8.) Generate geostatistical simulations of zinc by using only ordinary kriging model and compare your results with Fig 5.12. Which simulation produces higher variability of values (HINT: derive standard deviation and range) — with RK or OK model? (9.) Randomly split the meuse points in two data sets. Then repeat OK and RK using the same procedure explained in section 5.3.1 and see if the difference in accuracy at validation points is still the same? (10.) If you had more funds available to locate additional 50 points, and then sample soil again, where would you put them? (HINT: use the sampling optimization algorithm implemented in the package.)
R code to support
intamapInteractive
Further reading: Bivand, R., Pebesma, E., Rubio, V 2008. Applied Spatial Data Analysis with R. Use ., Heidelberg.
R Series. Springer,
Ribeiro Jr, P J., Christensen, O. F. and Diggle, P J., 2003. . . and : Software for Model-Based Geostatistics. In: Hornik, K. and Leisch, F. and Zeileis, A. (eds) Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Technical University Vienna, pp. 517– 524.
geoR
geoRglm
152
First steps (meuse) Kutner, M. H., Nachtsheim, C. J., Neter, J., Li, W. (Eds.), 2004. Applied Linear Statistical Models, 5th Edition. McGraw-Hill. Pebesma, E. J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30(7), 683–691.
http://www.gstat.org — The gstat package project.
http://leg.ufpr.br/geoR/ — The geoR package project.

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