dodgr_flows_si: Aggregate flows throughout a network using a spatial...

In dodgr: Distances on Directed Graphs

Aggregate flows throughout a network using a spatial interaction model.

Description

Aggregate flows throughout a network using an exponential Spatial Interaction (SI) model between a specified set of origin and destination points, and associated vectors of densities.

Usage

dodgr_flows_si(
  graph,
  from,
  to,
  k = 500,
  dens_from = NULL,
  dens_to = NULL,
  contract = TRUE,
  norm_sums = TRUE,
  heap = "BHeap",
  tol = 0.000000000001,
  quiet = TRUE
)

Arguments

graph	data.frame or equivalent object representing the network graph (see Details)
from	Vector or matrix of points from which aggregate flows are to be calculated (see Details)
to	Vector or matrix of points to which aggregate flows are to be calculated (see Details)
k	Width of exponential spatial interaction function (exp (-d / k)), in units of 'd', specified in one of 3 forms: (i) a single value; (ii) a vector of independent values for each origin point (with same length as 'from' points); or (iii) an equivalent matrix with each column holding values for each 'from' point, so 'nrow(k)==length(from)'. See Note.
dens_from	Vector of densities at origin ('from') points
dens_to	Vector of densities at destination ('to') points
contract	If TRUE (default), calculate flows on contracted graph before mapping them back on to the original full graph (recommended as this will generally be much faster). FALSE should only be used if the graph has already been contracted.
norm_sums	Standardise sums from all origin points, so sum of flows throughout entire network equals sum of densities from all origins (see Note).
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).
tol	Relative tolerance below which flows towards to vertices are not considered. This will generally have no effect, but can provide speed gains when flow matrices represent spatial interaction models, in which case this parameter effectively reduces the radius from each from point over which flows are aggregated. To remove any such effect, set tol = 0.
quiet	If FALSE, display progress messages on screen.

Value

Modified version of graph with additional flow column added.

Note

Spatial Interaction models are often fitted through trialling a range of values of 'k'. The specification above allows fitting multiple values of 'k' to be done with a single call, in a way that is far more efficient than making multiple calls. A matrix of 'k' values may be entered, with each column holding a different vector of values, one for each 'from' point. For a matrix of 'k' values having 'n' columns, the return object will be a modified version in the input 'graph', with an additional 'n' columns, named 'flow1', 'flow2', ... up to 'n'. These columns must be subsequently matched by the user back on to the corresponding columns of the matrix of 'k' values.

The norm_sums parameter should be used whenever densities at origins and destinations are absolute values, and ensures that the sum of resultant flow values throughout the entire network equals the sum of densities at all origins. For example, with norm_sums = TRUE (the default), a flow from a single origin with density one to a single destination along two edges will allocate flows of one half to each of those edges, such that the sum of flows across the network will equal one, or the sum of densities from all origins. The norm_sums = TRUE option is appropriate where densities are relative values, and ensures that each edge maintains relative proportions. In the above example, flows along each of two edges would equal one, for a network sum of two, or greater than the sum of densities.

With norm_sums = TRUE, the sum of network flows (sum(output$flow)) should equal the sum of origin densities (sum(dens_from)). This may nevertheless not always be the case, because origin points may simply be too far from any destination (to) points for an exponential model to yield non-zero values anywhere in a network within machine tolerance. Such cases may result in sums of output flows being less than sums of input densities.

See Also

Other distances: dodgr_distances(), dodgr_dists(), dodgr_dists_categorical(), dodgr_dists_nearest(), dodgr_flows_aggregate(), dodgr_flows_disperse(), dodgr_isochrones(), dodgr_isodists(), dodgr_isoverts(), dodgr_paths(), dodgr_times()

Examples

graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 10)
to <- sample (graph$to_id, size = 5)
to <- to [!to %in% from]
flows <- matrix (10 * runif (length (from) * length (to)),
    nrow = length (from)
)
graph <- dodgr_flows_aggregate (graph, from = from, to = to, flows = flows)
# graph then has an additonal 'flows' column of aggregate flows along all
# edges. These flows are directed, and can be aggregated to equivalent
# undirected flows on an equivalent undirected graph with:
graph_undir <- merge_directed_graph (graph)
# This graph will only include those edges having non-zero flows, and so:
nrow (graph)
nrow (graph_undir) # the latter is much smaller

dodgr documentation built on Sept. 11, 2024, 7:52 p.m.