Network analysis of livestock movement data

movenet uses the Statnet suite of packages (e.g. networkDynamic and tsna) for (temporal) social network analysis. We are currently working on the functions in this section - e.g. creation of a pdf report with network measures of particular relevance to disease transmission.

library(movenet)

# Load example movenet-format movement and holding tibbles into the global environment:
movement_data <- head(example_movement_data, 100)
holding_data <- example_holding_data

Generating a temporal network representation

We can generate a temporal network (networkDynamic) representation of movement data and optional holding data, as follows:

# Create a networkDynamic from our data
network <- movedata2networkDynamic(movement_data, holding_data,
                                   incl_nonactive_holdings = FALSE)
network
#> NetworkDynamic properties:
#>   distinct change times: 85 
#>   maximal time range: 17897 until  18254 
#> 
#>  Dynamic (TEA) attributes:
#>   Edge TEAs:    movement_reference.active 
#>      qty_pigs.active 
#> 
#> Includes optional net.obs.period attribute:
#>  Network observation period info:
#>   Number of observation spells: 1 
#>   Maximal time range observed: 17897 until 18254 
#>   Temporal mode: continuous 
#>   Time unit: unknown 
#>   Suggested time increment: NA 
#> 
#>  Network attributes:
#>   vertices = 170 
#>   directed = TRUE 
#>   hyper = FALSE 
#>   loops = FALSE 
#>   multiple = FALSE 
#>   bipartite = FALSE 
#>   net.obs.period: (not shown)
#>   weight = qty_pigs 
#>   vertex.pid = true_id 
#>   total edges= 100 
#>     missing edges= 0 
#>     non-missing edges= 100 
#> 
#>  Vertex attribute names: 
#>     active coordinates herd_size holding_type true_id vertex.names 
#> 
#>  Edge attribute names: 
#>     active movement_reference.active qty_pigs.active

Calculate summary statistics for selected temporal network measures

This networkDynamic representation can then be used to calculate various network summary measures, such as finding the maximum reachability in the network:

max_reachability <- 
  parallel_summarise_temporal_node_properties(list(network), 
                                              n_threads = 1,
                                              node_property = "forward reachability",
                                              statistics = list(max = max),
                                              identify_nodes = TRUE)
unlist(max_reachability)
#>  summary_statistics.max node_pid_with_max_value 
#>                     "5"           "29/530/4214"

Create a temporal social network analysis report

The movenet package provides a function to create a network analysis report, with analyses of monthly network snapshots:

create_temporal_network_analysis_report(
  network,
  output_file = file.path(tempdir(),"network_report.html"),
  incl_reachability_analysis = FALSE,
  n_threads = 1,
  whole_months = TRUE,
  time_unit = "month")

This creates an html document that can be opened in a browser, and contains a full report as shown below: