Calculate betweenness centrality for a 'dodgr' network.

Centrality can be calculated in either vertex- or edge-based form.

Usage

dodgr_centrality(
  graph,
  contract = TRUE,
  edges = TRUE,
  column = "d_weighted",
  vert_wts = NULL,
  dist_threshold = NULL,
  heap = "BHeap",
  check_graph = TRUE
)

Arguments

graph: 'data.frame' or equivalent object representing the network graph (see Details)
contract: If 'TRUE', centrality is calculated on contracted graph before mapping back on to the original full graph. Note that for street networks, in particular those obtained from the osmdata package, vertex placement is effectively arbitrary except at junctions; centrality for such graphs should only be calculated between the latter points, and thus 'contract' should always be 'TRUE'.
edges: If 'TRUE', centrality is calculated for graph edges, returning the input 'graph' with an additional 'centrality' column; otherwise centrality is calculated for vertices, returning the equivalent of 'dodgr_vertices(graph)', with an additional vertex-based 'centrality' column.
column: Column of graph defining the edge properties used to calculate centrality (see Note).
vert_wts: Optional vector of length equal to number of vertices (nrow(dodgr_vertices(graph))), to enable centrality to be calculated in weighted form, such that centrality measured from each vertex will be weighted by the specified amount.
dist_threshold: If not 'NULL', only calculate centrality for each point out to specified threshold. Setting values for this will result in approximate estimates for centrality, yet with considerable gains in computational efficiency. For sufficiently large values, approximations will be accurate to within some constant multiplier. Appropriate values can be established via the estimate_centrality_threshold function.
heap: Type of heap to use in priority queue. Options include Fibonacci Heap (default; 'FHeap'), Binary Heap ('BHeap'), Trinomial Heap ('TriHeap'), Extended Trinomial Heap ('TriHeapExt', and 2-3 Heap ('Heap23').
check_graph: If TRUE, graph is first checked for duplicate edges, which can cause incorrect centrality calculations. If duplicate edges are detected in an interactive session, a prompt will ask whether you want to proceed or rectify edges first. This value may be set to FALSE to skip this check and the interactive prompt.

Value

Modified version of graph with additional 'centrality' column added.

Note

The column parameter is by default d_weighted, meaning centrality is calculated by routing according to weighted distances. Other possible values for this parameter are

d for unweighted distances
time for unweighted time-based routing
time_weighted for weighted time-based routing

Centrality is calculated by default using parallel computation with the maximal number of available cores or threads. This number can be reduced by specifying a value via RcppParallel::setThreadOptions (numThreads = <desired_number>).

Examples

if (FALSE) { # \dontrun{
graph_full <- weight_streetnet (hampi)
graph <- dodgr_contract_graph (graph_full)
graph <- dodgr_centrality (graph)
# 'graph' is then the contracted graph with an additional 'centrality' column
# Same calculation via 'igraph':
igr <- dodgr_to_igraph (graph)
library (igraph)
cent <- edge_betweenness (igr)
identical (cent, graph$centrality) # TRUE
# Values of centrality between all junctions in the contracted graph can then
# be mapped back onto the original full network by "uncontracting":
graph_full <- dodgr_uncontract_graph (graph)
# For visualisation, it is generally necessary to merge the directed edges to
# form an equivalent undirected graph. Conversion to 'sf' format via
# 'dodgr_to_sf()' is also useful for many visualisation routines.
graph_sf <- merge_directed_graph (graph_full) %>%
    dodgr_to_sf ()
} # }

if (FALSE) { # \dontrun{
library (mapview)
centrality <- graph_sf$centrality / max (graph_sf$centrality)
ncols <- 30
cols <- c ("lawngreen", "red")
cols <- colorRampPalette (cols) (ncols) [ceiling (ncols * centrality)]
mapview (graph_sf, color = cols, lwd = 10 * centrality)
} # }

# An example of flow aggregation across a generic (non-OSM) highway,
# represented as the 'routes_fast' object of the \pkg{stplanr} package,
# which is a SpatialLinesDataFrame containing commuter densities along
# components of a street network.
if (FALSE) { # \dontrun{
library (stplanr)
# merge all of the 'routes_fast' lines into a single network
r <- overline (routes_fast, attrib = "length", buff_dist = 1)
r <- sf::st_as_sf (r)
# Convert to a 'dodgr' network, for which we need to specify both a 'type'
# and 'id' column.
r$type <- 1
r$id <- seq (nrow (r))
graph_full <- weight_streetnet (
    r,
    type_col = "type",
    id_col = "id",
    wt_profile = 1
)
# convert to contracted form, retaining junction vertices only, and append
# 'centrality' column
graph <- dodgr_contract_graph (graph_full) %>%
    dodgr_centrality ()
#' expand back to full graph; merge directed flows; and convert result to
# 'sf'-format for plotting
graph_sf <- dodgr_uncontract_graph (graph) %>%
    merge_directed_graph () %>%
    dodgr_to_sf ()
plot (graph_sf ["centrality"])
} # }