NAME¶
v.kernel - Generates a raster density map from vector point data
using a moving kernel or optionally generates a vector density map on a vector
network.
KEYWORDS¶
vector, kernel density
SYNOPSIS¶
v.kernel
v.kernel help
v.kernel [-
oqnmv]
input=
name
[
net=
name]
output=
name
stddeviation=
float [
dsize=
float]
[
segmax=
float] [
distmax=
float]
[
mult=
float] [
node=
string]
[
kernel=
string] [--
verbose] [--
quiet]
Flags:¶
- -o
-
Try to calculate an optimal standard deviation with 'stddeviation' taken as
maximum (experimental)
- -q
-
Only calculate optimal standard deviation and exit (no map is written)
- -n
-
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
- -m
-
In network mode, multiply the result by number of input points.
- -v
-
Verbose module output (retained for backwards compatibility)
- --verbose
-
Verbose module output
- --quiet
-
Quiet module output
Parameters:¶
- input=name
-
Input vector with training points
- net=name
-
Input network vector map
- output=name
-
Output raster/vector map
- stddeviation=float
-
Standard deviation in map units
- dsize=float
-
Discretization error in map units
Default: 0.
- segmax=float
-
Maximum length of segment on network
Default: 100.
- distmax=float
-
Maximum distance from point to network
Default: 100.
- mult=float
-
Multiply the density result by this number
Default: 1.
- node=string
-
Node method
Options: none,split
Default: none
none: No method applied at nodes with more than 2 arcs
split: Equal split (Okabe 2009) applied at nodes
- kernel=string
-
Kernel function
Options:
uniform,triangular,epanechnikov,quartic,triweight,gaussian,cosine
Default: gaussian
DESCRIPTION¶
v.kernel generates a raster density map from vector points data using a
moving kernel. Available kernel density functions are
uniform,
triangular, epanechnikov, quartic, triweight, gaussian, cosine, default
is
gaussian.
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
kernel option and can be enabled with
node=split.
NOTES¶
The
mult 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.
With the
-o flag (experimental) the command tries to calculate an optimal
standard deviation. The value of
stddeviation is taken as maximum
value. Standard deviation is calculated using ALL points, not just those in
the current region.
LIMITATIONS¶
The module only considers the presence of points, but not (yet) any attribute
values.
SEE ALSO¶
v.surf.rst
REFERENCES¶
Okabe, A., Satoh, T., Sugihara, K. (2009).
A kernel density estimation
method for networks, its computational method and a GIS-based tool.
International Journal of Geographical Information Science, Vol 23(1),
pp. 7-32.
DOI: 10.1080/13658810802475491
AUTHORS¶
Stefano Menegon, ITC-irst, Trento, Italy
Radim Blazek (additional kernel density functions and network part)
Last changed: $Date: 2011-11-08 12:29:50 +0100 (Tue, 08 Nov 2011) $
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