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divided by the total number of cells that would have constituted the analysis window had it

been complete. This procedure thus normalizes the sum values so that they are comparable

across the entire raster.

Syntax

neighbourhoodstatistics(in, radius, sum, min, max, mean, sd, med, [edge]);

the input raster data source

radius the radius of the neighbourhood analysis window in coordinate system

units

sum

the new output raster data source representing the sum of values in the

neighbourhood

min

the new output raster data source representing the minimum of values

in the neighbourhood

max

the new output raster data source representing the maximum of values

in the neighbourhood

mean

the new output raster data source representing the mean of values in the

neighbourhood

the new output raster data source representing the standard deviation of

values in the neighbourhood

med

the new output raster data source representing the median of values in

the neighbourhood

[edge] (TRUE/FALSE) calculate statistics for edge cells, where the neighbor-

hood is only partially observed (default=TRUE)

Example

neighbourhoodstatistics(in="C:ndatandem", radius=2000,

mean="C:ndatandemmean.img", sd="C:ndatandemsd.img");

neighbourhoodstatistics(in="C:ndatanhabitat", radius=250, sum="C:ndatanhabsum.img",

edge=FALSE);

3.81 paste

Paste: Concatenates a list of values and/or variables to create a text string.

Description

This function is designed to be embedded within other command text. It creates new strings

(text) based on a variety of inputs you dene. Those inputs can be other strings (in quotes),

numbers (in quotes or not in quotes), index variables from a for loop, or variables that you

have dened.

The optional ’sep’ argument allows you to dene the separator to use between the com-

ponents. By default this is nothing, but if you are constructing a comma delimited list you

would chance this to sep="," for instance.

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This paste function is very similar to the paste function in R.

Syntax

paste(..., [sep]);

...

acomma delimited list of components from which the new string is con-

structed

[sep] denes the separator character to use between the components in the list

(default is no seperator); e.g. to construct a comma delimited list specify

sep=","

Example

wd <- "C:ndatan"

paste(wd, "image", i, ".img")

Assuming this paste function is embedded in a command occurring within a for(i in

1:3)f...g loop, and the ’wd’ variable has previously been dened, the following strings would

result in each of the three loops:

"C:ndatanimage1.img"

"C:ndatanimage2.img"

"C:ndatanimage3.img"

3.82 pointdistances

Distances Among Points: Calculates distances between points, either among points within

apoint data source, or among points between two point data sources.

Description

This tool calculates distances between points, either among points within a point datasource,

or among points between two point data sources. It also optionally identies the N nearest

neighbours, and has three output formats to choose from. This tool has been designed to

produce exible distance matrix outputs that would be used in subsequent analyses.

The simplest implementation of the tool is to specify an input point dataset, identify the

unique ID eld for that dataset, and specify the output le. The tool will calculate distances

between each of the points within this dataset and write the result using the default format.

There are three formats to choose from. The rst (1D) write a table that has three

columns: the unique ID of each of the two points, and the distance between them. This

format is the default because it can accommodate very large numbers of points, and it does

not report distances twice (i.e. if the tool calculates the distance between points A and B, it

will not also report the distance between points B and A). The second option (2D) writes an

NxN table (the full distance matrix), where N is the total number of points in the dataset.

This option is only recommended with small numbers of points because the output is unlikely

to be useful for large numbers of points (you would not be able to open it in Excel or import

it into Access for instance). The 2D format reports all values twice (e.g. A and B, and B and

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A, and all the cells on the diagonal are 0 (the distance between a point and itself). The nal

format (SUMMARY) generates a simple statistical summary (mean, minimum, maximum,

standard deviation, and optionally median) of the distances between a given point and all

the other points. The output table includes these summary statistic elds as columns, and

Nrows.

Nearest neighbours can be identied at the same time using the ’nearest’ and ’nout’

parameters. The nearest neighbour output is written to a dierent table, and includes the

ID’s of the ordered list of the n nearest neighbours, and the distance to each one.

The ’multiplier’ option provides you with a way of changing the units of the distance

value. By default the distance is calculated in coordinate system units, e.g. meters for UTM.

If you wanted these distances in kilometers then you would set the multiplier to be 0.001.

Tochange the delimiting character in the output table (if you are in a country that prefers

not to use the comma as the delimiting character), please refer to the ’delimiter’ command,

which must be issued before runningthis tool. However, you only have to issue that command

once per session as it is a global setting.

The median is not calculated by default because it can be a computationally expensive

calculation for large numbers of points. The median will be calculated if you specify ’for-

mat=SUMMARY’ and ’median=TRUE’ options.

This tool will attempt to ignore points with null geometries. These records can arise in a

number of ways, but are particularly common in the output of address geocoding algorithms

(the records that the algorithm failed to resolve). They are also common in GPS telemetry

databases. It is advisable to either repair or remove these records prior to running GME

commands. You will be warned if this command detects null geometries.

This tool should not be used with data in a geographic coordinate system. The distance

calculations here assume the coordinates are Cartesian (i.e. a projected coordinate system),

not spherical.

Note that this tool will not overwrite existing les, so you should ensure the output le

does not already exist.

Syntax

pointdistances(in, d, out, [in2], [ d2], [format], [nearest], [nout], [multiplier], [median],

[where]);

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the input point dataset (calculates distances between points within this

dataset - but see ’in2’)

the unique ID eld in the input dataset

out

the output delimited text le to create

[in2]

the second point layer (calculates distances between points between

datasets)

[ d2]

the unique ID eld in the second input dataset (MUST be specied if

’in2’ is specied)

[format]

the structure of the output table: 1D (default, one record per row), 2D

(an NxN matrix), SUMMARY (summary statistics for each point only)

(options: 1D, 2D, SUMMARY)

[nearest]

the number of nearest neighbours to identify for each input point (de-

fault=0); note that ’nout’ must also be specied with this option

[nout]

the output delimited text le to create for the nearest neighbour dataset

[multiplier] multiplies the distance by this value before writing to output (default=1)

[median]

(TRUE/FALSE) determines whether the median is also calculated if the

’summary’ format is specied (this can add considerably to processing

time for large datasets, default=FALSE)

[where]

the lter/selection statement that will be applied to the rst (’in’, not

’in2’) point feature class to identify a subset of points to process

Example

pointdistances(in="C:ndatanlocs.shp", d="RECID", out="C:ndatandistances.csv",

format="SUMMARY", multiplier=0.001);

pointdistances(in="C:ndatanpredators.shp", d="ANID", out="C:ndatandistances.csv",

in2="C:ndatanprey.shp", d2="PREYID");

pointdistances(in="C:ndatanrestations.shp", d="STID", out="C:ndatandistances.csv",

in2="C:ndatanhouses.shp", d2="PROPID", nearest=10,

nout="C:ndatannearestheigh.csv", median=TRUE);

3.83 r

Executes An R Command: Executes R commands.

Description

The r() command is dierent from most other GME commands in that it takes the text

within the parentheses and sends it directly to R, without any interpretation of the text. For

this reason you should not enclosethe R command text within quotes: write the R commands

here exactly as you would write them in R. This command does not recover any R objects,

but you can use the r.ls() command to view a list of objects in the current R session.

This command is designed to be used in conjunction with the following commands:

r.loaddata, r.writedatatoeld, r.writedatatoraster. One of the most important uses of this

114

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command will be to run R scripts that you have written and saved in text les.

You must call the r() command multiple times if you have several commands to execute

(rather than separate them using semi-colons within a single call to this command). Better

yet, write commands to a text le, and then call that as a script.

Note that if your code requires R libraries, then you must also make sure that the libraries

are installed, and that you include the R library() or require() commands to load them within

your code.

Syntax

r(code);

code R code to be evaluated (not in quotes)

Example

r(source(le="C:/scripts/ranalysis.txt"));

3.84 r.deldir

Delaunay Triangulation / Dirichlet Tesselation: Computes the Delaunay (Voronoi)

triangulation and Dirichlet tessalation of a set of points using the ’deldir’ library in R.

Description

This tool uses the ’deldir’ library in R to compute the Delaunay triangulation and Dirichlet

tessellation of a set of points. Given an input point data source, the user can produce one or

both of the two outputs (they are both calculated simultaneously so there is no signicant

processing cost to producing both) as line layers.

Dirichlet tessellation requires a rectangular boundary that denes the limits of the calcu-

lation. By default the deldir library will set this to be plus/minus 10% of the range of x and

ypoint coordinates, but this can be specied explicitly with the ’extent’ option.

This tool automatically lters any stacked (identically placed) points before running the

computation in R.

For citations and a full description of the deldir library, open R and type: library(deldir),

then ?deldir. If the deldir library has not been installed on your system, you must rst type:

install.pacakges("deldir").

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Syntax

r.deldir(in, dir, del, [extent], [where]);

the input point data source

dir

the output line data source for the Dirichlet (Voronoi) tessellation (op-

tional if ’del’ is specied)

del

the output line data source for the Delaunay triangulation (optional if

’dir’ is specied)

[extent] the reference layer to clip to (a vector or raster layer), or the coordinates

of the rectangle to clip to (min x, max x, min y, max y)

[where] the selection statement that will be applied to the feature data source to

identify a subset of features to process (see full Help documentation for

further details)

Example

r.deldir(in="C:ndatanrestations.shp", dir="C:ndatandirtess.shp");

r.deldir(in="C:ndatanrestations.shp", del="C:ndatandeltriang.shp");

r.deldir(in="C:ndatansamples.shp", dir="C:ndatandirtess.shp",

del="C:ndatandeltriang.shp", extent=c(346000,450600,4956300,5204300));

r.deldir(in="C:ndatantrees.shp", dir="C:ndatandirtess.shp", del="C:ndatandeltriang.shp",

extent="C:ndatanstudyarea.shp");

3.85 r.eval

Evaluate R Expression: This function evaluates an expression in R, and retrieves the

result.

Description

This function is designed to be embedded within command text. It provides a mechanism for

running R code to obtain strings, numbers or arrays that are used as parameters for GME

command arguments.

Important note. The r.eval syntax is dierent than all other GME commands because

you must not enclose the R code in quotes. All other GME commands and functions require

that you enclose all text in quotes, but the code contained within the r.eval(...) function is

sent directly to R. The reason for this is that you may need to use quotes in your R code,

which is perfectly acceptable.

Important note. R commands must be separated by semi-colons.

If the R code is single line and simple, then GME will automatically retrieve the resulting

value. However, if your R code is complex, and/or multiline, then you must explicitly set

avariable called ’gme’ within that R code, which GME will then retrieve. If your code is

complex, I recommend you save it as an R script le, and then use the r.eval function to call

that script.

116

Note that if your code requires R libraries, then you must also make sure that the libraries

are installed, and that you include the R library() command to load them within your code.

Syntax

r.eval(code);

code the R code to evaluate (not in quotes) and the result returned

Example

r.eval(seq(0, 1, 0.02))

r.eval(exp(-0.01*200))

r.eval(a <- rnorm(10, 100, 12.234); gme <- log(a))

3.86 r.graphsettings

Graph Settings: Changes the default graph parameters that are used to generate graphics

in R.

Description

This command allows you to change the default graph parameters that are used to generate

graphics in R. Some of the parameters only aect the next graphics to be produced, while

the other parameters aect all subsequent graphics.

The parameters ’xlab’, ’ylab’ and ’main’ correspond to the x axis title, the y axis title

and the main title respectively, and only aect the next graph to be produced, after which

they are reset. You must therefore set these for each graph if you wish to customize the

labels or title. Unlike R, you should not use quotation marks to set these parameters (see

the example below). Note that for the labels, the code will automatically wrap long labels (

>50 characters) onto multiple lines.

The ’width’ and ’height’ parameters refer to the dimensions in pixels of the graphic that

is produced. The default is 500 x 500 pixels. Small dimensions ( < 300 pixels) may result in

absurd graphics.

The ’pointsize’ parameters refers to the size of text. Note that the size of the main title

text is always automatically scaled to be larger than the text of the axis labels. Point sizes

in the range of 9-16 are likely to produce reasonable graphics.

The ’llcol’ parameters sets the default ll colour (e.g. for bars in a histogram), and

’linecol’ sets the default line colour for graph types that display lines. Colours are specied

using colour names. See the R Colours appendix for samples of colours and their names.

The ’letype’ parameters controls the output le type. The default is Ping (png), which

is a widely used format that most other software you encounter will be able to use. You may

change the default to JPEG format (jpg) or Windows Bitmap format (bmp).

This command is driven by R. Type ’citation’ to see the suggested citation for R.

117

Syntax

r.graphsettings([xlab], [ylab], [main], [width], [height], [pointsize], [llcol], [linecol], [letype]);

[xlab]

the x axis label for the next plot (overrides the default label)

[ylab]

the y axis label for the next plot (overrides the default label)

[main]

the main title for the next plot (overrides the default title, which is

usually no title)

[width]

the width of the output plot in pixels (default=700)

[height]

the height of the output plot in pixels (default=700)

[pointsize] the font size for text on R graphics (default=12)

[llcol]

the ll colour for plots like histograms (default=lightgreen) - see the full

help documentation for a list of R colours

[linecol]

the line colour for plots (default=lightgreen) - see the full help documen-

tation for a list of R colours

[letype]

the output le type for R graphics (default=png; options: png, jpg, bmp)

Example

r.graphsettings(xlab="X Coordinate (UTM)", ylab="Y Coordinate (UTM)",

main="Animal locations", width=900, height=900);

r.graphsettings(width=400, height=400, letype="jpg", pointsize=9, llcol="blue");

3.87 r.hist

Histogram: Creates a histogram graph using R.

Description

This command creates a histogram graph using R (the ’hist’ function in R) and displays it

in the output window. The R commands used to create the histogram are also displayed.

Type ’? hist’ at the R command prompt for further details.

This command is driven by R. Type ’citation’ to see the suggested citation for R.

Syntax

r.hist(in, eld, [breaks], [freq], [where]);

the input feature data source

eld

the name of eld containing the data to plot

[breaks] the number of bins or cells in the histogram (default=determined auto-

matically)

[freq]

(TRUE/FALSE) if TRUE the histogram represents frequencies/counts,

ifFALSE the histogramrepresents probability densities (default=TRUE)

[where] the selection statement that will be applied to the feature data source to

identify a subset of features to process (see full Help documentation for

further details)

118

Example

r.hist(in="C:ndatanparcels.shp", eld="Value08");

r.hist(in="C:ndatancounty.shp", eld="Popn", breaks=30, freq=FALSE);

3.88 r.loaddata

Load Data Into R: Loads data from a vector attribute table into the current R session.

Description

This command will read data from a vector data attribute table and load it into the current

Rsession (every instance of GME has a hidden R session - you can use the r.ls() command to

view the objects in that session). The ’elds’ parameter is used to dene which elds will be

read, and the optional ’where’ parameter can be used to dene a subset of rows. There are

two important issues to be aware of when using this tool. First, the order in which records

from an attribute table are returned is not always the same, so if this command is called

multiple times on the same dataset there is a risk the order of values in the resulting R

vectors will be misaligned! Thus, it is important to specify all of the elds you want to load

into R in a single call to this command (the ’elds’ parameter can be a vector of multiple

eld names). If you do this you can be sure that the order of values in each vector will be

correct.

The second issue to be aware of is the need for an integer unique ID eld in the attribute

table that you can use to relate the R vectors (or new R vectors) back to the attribute table.

If you perform an analysis that generates a new R vector as output, for instance, and you

wish to write those values back to the attribute table then this unique ID eld is essential

for assigning the correct value from an R vector back to the attribute table. So if you need

to maintain a relational link between the R data and the original attribute table then be

sure to specify an integer unique ID eld in the list of elds that will be loaded. See the

r.writedatatotable command for an example of how this can be used.

The objects in R will have the same name as the elds from which they were written.

Note that R is case sensitive.

Syntax

r.loaddata(in, elds, [where]);

the input feature data source

elds

the names of eld or elds containing the data to load

[where] the selection statement that will be applied to the feature data source to

identify a subset of features to process (see full Help documentation for

further details)

Example

r.loaddata(in="C:ndatanparcels.shp", eld="Value08");

119

r.loaddata(in="C:ndatancounty.shp", elds=c("FID", "Popn","Area","Name"),

where="STATE=’WI’");

3.89 r.ls

List R Variables / Objects: Lists the R objects currently present in the GME R session.

Description

This command will display a list of the objects that are currently present (in memory) in

the GME R session. Although you cannot see this R session, you can interact with it using

several of the GME commands that begin with ’r.’. This command is useful for checking the

current state of the R session.

Syntax

r.ls);

Example

r.ls();

3.90 r.plotxy

Scatterplot: Creates a scatterplot graph using R.

Description

This command creates a scatterplot graph using R (the ’plot’ function in R) and displays it

in the output window. The R commands used to create the scatterplot are also displayed.

Type ’? plot’ at the R command prompt for further details.

This command is driven by R. Type ’citation’ to see the suggested citation for R.

Syntax

r.plotxy(in, xeld, yeld, [where]);

the input feature data source

xeld

the name of eld containing the x axis data to plot

yeld

the name of eld containing the y axis data to plot

[where] the selection statement that will be applied to the feature data source to

identify a subset of features to process (see full Help documentation for

further details)

120

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