v.kernel (1grass)

vector, kernel density

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

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.

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.

The module only considers the presence of points, but not (yet) any attribute values.

v.surf.rst

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

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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