.TH v.kernel 1grass "" "GRASS 6.4.4" "Grass User's Manual"
.SH NAME
\fI\fBv.kernel\fR\fR  - Generates a raster density map from vector point data using a moving kernel or optionally generates a vector density map on a vector network.
.SH KEYWORDS
vector, kernel density
.SH SYNOPSIS
\fBv.kernel\fR
.br
\fBv.kernel help\fR
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\fBv.kernel\fR [\-\fBoqnmv\fR] \fBinput\fR=\fIname\fR  [\fBnet\fR=\fIname\fR]  \fBoutput\fR=\fIname\fR \fBstddeviation\fR=\fIfloat\fR  [\fBdsize\fR=\fIfloat\fR]   [\fBsegmax\fR=\fIfloat\fR]   [\fBdistmax\fR=\fIfloat\fR]   [\fBmult\fR=\fIfloat\fR]   [\fBnode\fR=\fIstring\fR]   [\fBkernel\fR=\fIstring\fR]   [\-\-\fBverbose\fR]  [\-\-\fBquiet\fR] 
.SS Flags:
.IP "\fB\-o\fR" 4m
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Try to calculate an optimal standard deviation with 'stddeviation' taken as maximum (experimental)
.IP "\fB\-q\fR" 4m
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Only calculate optimal standard deviation and exit (no map is written)
.IP "\fB\-n\fR" 4m
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In network mode, normalize values by sum of density multiplied by length of each segment. Integral over the output map then gives 1.0 * mult
.IP "\fB\-m\fR" 4m
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In network mode, multiply the result by number of input points.
.IP "\fB\-v\fR" 4m
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Verbose module output (retained for backwards compatibility)
.IP "\fB\-\-verbose\fR" 4m
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Verbose module output
.IP "\fB\-\-quiet\fR" 4m
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Quiet module output
.PP
.SS Parameters:
.IP "\fBinput\fR=\fIname\fR" 4m
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Input vector with training points
.IP "\fBnet\fR=\fIname\fR" 4m
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Input network vector map
.IP "\fBoutput\fR=\fIname\fR" 4m
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Output raster/vector map
.IP "\fBstddeviation\fR=\fIfloat\fR" 4m
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Standard deviation in map units
.IP "\fBdsize\fR=\fIfloat\fR" 4m
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Discretization error in map units
.br
Default: \fI0.\fR
.IP "\fBsegmax\fR=\fIfloat\fR" 4m
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Maximum length of segment on network
.br
Default: \fI100.\fR
.IP "\fBdistmax\fR=\fIfloat\fR" 4m
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Maximum distance from point to network
.br
Default: \fI100.\fR
.IP "\fBmult\fR=\fIfloat\fR" 4m
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Multiply the density result by this number
.br
Default: \fI1.\fR
.IP "\fBnode\fR=\fIstring\fR" 4m
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Node method
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Options: \fInone,split\fR
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Default: \fInone\fR
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\fBnone\fR: No method applied at nodes with more than 2 arcs
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\fBsplit\fR: Equal split (Okabe 2009) applied at nodes
.IP "\fBkernel\fR=\fIstring\fR" 4m
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Kernel function
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Options: \fIuniform,triangular,epanechnikov,quartic,triweight,gaussian,cosine\fR
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Default: \fIgaussian\fR
.PP
.SH DESCRIPTION
\fIv.kernel\fR generates a raster density map from vector points data using
a moving kernel. Available kernel density functions are \fIuniform, 
triangular, epanechnikov, quartic, triweight, gaussian, cosine\fR, 
default is \fIgaussian\fR.
.PP
The module can also generate a vector density map on a vector network. 
Conventional kernel functions produce biased estimates by overestimating 
the densities around network nodes, whereas the equal split method of 
Okabe et al. (2009) produces unbiased density estimates. The equal split 
method uses the kernel function selected with the \fIkernel\fR option 
and can be enabled with \fInode=split\fR.
.SH NOTES
The \fImult\fR option is needed to overcome the limitation that
the resulting density in case of a vector map output is stored as category
(Integer). The density result stored as category may be multiplied by this number.
.PP
With the \fI-o\fR flag (experimental) the command tries to calculate an 
optimal standard deviation. The value of \fIstddeviation\fR is taken 
as maximum value. Standard deviation is calculated using ALL points, 
not just those in the current region.
.SH LIMITATIONS
The module only considers the presence of points, but not 
(yet) any attribute values.
.SH SEE ALSO
v.surf.rst
.SH REFERENCES
Okabe, A., Satoh, T., Sugihara, K. (2009). \fIA kernel density estimation 
method for networks, its computational method and a GIS-based tool\fR.
\fBInternational Journal of Geographical Information Science\fR, Vol 23(1), 
pp. 7-32.
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DOI: 10.1080/13658810802475491
.SH AUTHORS
Stefano Menegon, ITC-irst, Trento, Italy
.br
Radim Blazek (additional kernel density functions and network part)
.PP
\fILast changed: $Date: 2011-11-08 12:29:50 +0100 (Tue, 08 Nov 2011) $\fR
.PP
Full index
.PP
© 2003-2014 GRASS Development Team