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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.
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.
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 03:29:50 -0800 (Tue, 08 Nov 2011) $ Full index © 2003-2013 GRASS Development Team