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v.net.centrality - Computes degree, centrality, betweeness, closeness and eigenvector centrality measures in the network.
vector, network, centrality measures
v.net.centrality v.net.centrality --help v.net.centrality [-ga] input=name [arc_layer=string] [node_layer=string] output=name [cats=range] [where=sql_query] [arc_column=name] [arc_backward_column=name] [node_column=string] [degree=name] [closeness=name] [betweenness=name] [eigenvector=name] [iterations=integer] [error=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui] Flags: -g Use geodesic calculation for longitude-latitude locations -a Add points on nodes --overwrite Allow output files to overwrite existing files --help Print usage summary --verbose Verbose module output --quiet Quiet module output --ui Force launching GUI dialog Parameters: input=name [required] Name of input vector map Or data source for direct OGR access arc_layer=string Arc layer Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name. Default: 1 node_layer=string Node layer Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name. Default: 2 output=name [required] Name for output vector map cats=range Category values Example: 1,3,7-9,13 where=sql_query WHERE conditions of SQL statement without ’where’ keyword Example: income < 1000 and population >= 10000 arc_column=name Arc forward/both direction(s) cost column (number) arc_backward_column=name Arc backward direction cost column (number) node_column=string Node cost column (number) degree=name Name of degree centrality column closeness=name Name of closeness centrality column betweenness=name Name of betweenness centrality column eigenvector=name Name of eigenvector centrality column iterations=integer Maximum number of iterations to compute eigenvector centrality Default: 1000 error=float Cumulative error tolerance for eigenvector centrality Default: 0.1
v.net.centrality computes degree, closeness, betweenness and eigenvector centrality measures.
The module computes various centrality measures for each node and stores them in the given columns of an attribute table, which is created and linked to the output map. For the description of these, please check the following wikipedia article. If the column name is not given for a measure then that measure is not computed. If -a flag is set then points are added on nodes without points. Also, the points for which the output is computed can be specified by cats, layer and where parameters. However, if any of these parameters is present then -a flag is ignored and no new points are added. Betweenness measure is not normalised. In order to get the normalised values (between 0 and 1), each number needs to be divided by N choose 2=N*(N-1)/2 where N is the number of nodes in the connected component. Computation of eigenvector measure terminates if the given number of iterations is reached or the cumulative squared error between the successive iterations is less than error.
Compute closeness and betweenness centrality measures for each node and produce a map containing not only points already present in the input map but a map with point on every node. v.net.centrality input=roads output=roads_cent closeness=closeness \ betweenness=betweenness -a
Daniel Bundala, Google Summer of Code 2009, Student Wolf Bergenheim, Mentor
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