Generate a grouped network by edge betweenness from a preprocessed coherent network

This function takes a preprocessed network and applies graph-based analysis to group the network edges based on their betweenness centrality. The function assumes that the input network has attributes relevant to cycling traffic dynamics, specifically 'all_fastest_bicycle_go_dutch' and 'weight'. It outputs a transformed network where edges are grouped and ranked according to their mean potential, facilitating further analysis or visualization of critical network pathways.

Usage

coherent_network_group(
  coherent_network,
  key_attribute = "all_fastest_bicycle_go_dutch",
  n_group = 12
)

Arguments

coherent_network

A preprocessed 'sf' object containing the network data, expected to have columns 'all_fastest_bicycle_go_dutch' and 'weight'.

key_attribute

The attribute used to keep.

n_group

The number of groups to divide the network into, based on edge betweenness centrality.

Value

An 'sf' object with edges grouped and ranked based on their mean potential.

Examples

# Assuming 'coherent_network' is obtained from a previous function corenet
# Generate the grouped network
# grouped_network = coherent_network_group(coherent_network)