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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.

```

SEEALSO

```       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