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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.kernelv.kernelhelpv.kernel[-oqnmv]input=name[net=name]output=namestddeviation=float[dsize=float] [segmax=float] [distmax=float] [mult=float] [node=string] [kernel=string] [--verbose] [--quiet]Flags:-oTry to calculate an optimal standard deviation with 'stddeviation' taken as maximum (experimental)-qOnly calculate optimal standard deviation and exit (no map is written)-nIn network mode, normalize values by sum of density multiplied by length of each segment. Integral over the output map then gives 1.0 * mult-mIn network mode, multiply the result by number of input points.-vVerbose module output (retained for backwards compatibility)--verboseVerbose module output--quietQuiet module outputParameters:input=nameInput vector with training pointsnet=nameInput network vector mapoutput=nameOutput raster/vector mapstddeviation=floatStandard deviation in map unitsdsize=floatDiscretization error in map units Default:0.segmax=floatMaximum length of segment on network Default:100.distmax=floatMaximum distance from point to network Default:100.mult=floatMultiply the density result by this number Default:1.node=stringNode method Options:none,splitDefault:nonenone: No method applied at nodes with more than 2 arcssplit: Equal split (Okabe 2009) applied at nodeskernel=stringKernel function Options:uniform,triangular,epanechnikov,quartic,triweight,gaussian,cosineDefault:gaussian

**DESCRIPTION**

v.kernelgenerates a raster density map from vector points data using a moving kernel. Available kernel density functions areuniform,triangular,epanechnikov,quartic,triweight,gaussian,cosine, default isgaussian. 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 thekerneloption and can be enabled withnode=split.

**NOTES**

Themultoption 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-oflag (experimental) the command tries to calculate an optimal standard deviation. The value ofstddeviationis 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).Akerneldensityestimationmethodfornetworks,itscomputationalmethodandaGIS-basedtool.InternationalJournalofGeographicalInformationScience, 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)Lastchanged:$Date:2011-11-0803:29:50-0800(Tue,08Nov2011)$Full index © 2003-2013 GRASS Development Team