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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.
## 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 03:29:50 -0800 (Tue, 08 Nov 2011) $*

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