Premise x neighbourhood co-occurrence network — morie_tps_criminal_network

Builds a co-occurrence network in lieu of the co-offender network from D'Orsogna & Perc (2015) Fig. 9 / Diviak et al. (2019). Public TPS data has no co-offender records, so we approximate by projecting (top-N premise types) x (HOOD_158 neighbourhoods) onto a premise-by-premise edge-weighted graph. Edge weight is the count of neighbourhoods in which both premise types appear.

Usage

morie_tps_criminal_network_graph(
  category = "Assault",
  sample_rows = 30000L,
  top_n_premises = 20L,
  save_fig = TRUE
)

Arguments

category: TPS category name.
sample_rows: Maximum rows to load.
top_n_premises: Number of premise nodes to keep.
save_fig: Whether to emit a circular layout PNG.

Value

A morie_rich_result with node count, edge count, strongest edge weight, and the adjacency payload.

References

Diviak T, Dijkstra JK, Snijders TAB (2019). Structure, multiplexity, and centrality in a corruption network. Trends in Organized Crime 22: 274-297.

Examples

if (FALSE) { # \dontrun{
  rr <- morie_tps_criminal_network_graph("Assault",
                                           top_n_premises = 10L,
                                           save_fig = FALSE)
  print(rr$summary_lines)
} # }