NAME

v.net.centrality - Computes degree, centrality, betweeness, closeness and eigenvector centrality measures in the network.

KEYWORDS

vector, network, centrality measures

SYNOPSIS

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

DESCRIPTION

v.net.centrality computes degree, closeness, betweenness and eigenvector centrality measures.

NOTES

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.

EXAMPLES

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

AUTHORS

Daniel Bundala, Google Summer of Code 2009, Student

Wolf Bergenheim, Mentor

Last changed: $Date: 2016-03-28 23:23:39 +0200 (Mon, 28 Mar 2016) $

SOURCE CODE

Available at: v.net.centrality source code (history)