dodgr flows

1 Flow Aggregation

The first of the above functions aggregates ‘’flows’’ throughout a network from a set of origin (from) and destination (to) points. Flows commonly arise in origin-destination matrices used in transport studies, but may be any kind of generic flows on graphs. A flow matrix specifies the flow between each pair of origin and destination points, and the dodgr_flows_aggregate() function aggregates all of these flows throughout a network and assigns a resultant aggregate flow to each edge.

For a set of nf points of origin and nt points of destination, flows are defined by a simple nf-by-nt matrix of values, as in the following code:

graph <- weight_streetnet (hampi, wt_profile = "foot")
set.seed (1)
from <- sample (graph$from_id, size = 10)
to <- sample (graph$to_id, size = 10)
flows <- matrix (10 * runif (length (from) * length (to)),
    nrow = length (from)
)

This flows matrix is then submitted to dodgr_flows_aggregate(), which simply appends an additional column of flows to the submitted graph:

graph_f <- dodgr_flows_aggregate (graph, from = from, to = to, flows = flows)
head (graph_f)

##   geom_num edge_id    from_id from_lon from_lat      to_id   to_lon   to_lat
## 1        1       1  339318500 76.47491 15.34167  339318502 76.47612 15.34173
## 2        1       2  339318502 76.47612 15.34173  339318500 76.47491 15.34167
## 3        1       3  339318502 76.47612 15.34173 2398958028 76.47621 15.34174
## 4        1       4 2398958028 76.47621 15.34174  339318502 76.47612 15.34173
## 5        1       5 2398958028 76.47621 15.34174 1427116077 76.47628 15.34179
## 6        1       6 1427116077 76.47628 15.34179 2398958028 76.47621 15.34174
##            d d_weighted highway   way_id component      time time_weighted flow
## 1 130.000241 130.000241    path 28565950         1 93.600174     93.600174    0
## 2 130.000241 130.000241    path 28565950         1 93.600174     93.600174    0
## 3   8.890622   8.890622    path 28565950         1  6.401248      6.401248    0
## 4   8.890622   8.890622    path 28565950         1  6.401248      6.401248    0
## 5   9.307736   9.307736    path 28565950         1  6.701570      6.701570    0
## 6   9.307736   9.307736    path 28565950         1  6.701570      6.701570    0

Most flows are zero because they have only been calculated between very few points in the graph.

summary (graph_f$flow)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.3164  0.0000  5.1291

2 Flow Dispersal

The second function, dodgr_flows_disperse(), uses only a vector a origin (from) points, and aggregates flows as they disperse throughout the network according to a simple exponential model. In place of the matrix of flows required by dodgr_flows_aggregate(), dispersal requires an equivalent vector of densities dispersing from all origin (from) points. This is illustrated in the following code, using the same graph as the previous example.

dens <- rep (1, length (from)) # uniform densities
graph_f <- dodgr_flows_disperse (graph, from = from, dens = dens)
summary (graph_f$flow)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.000e+00 0.000e+00 7.548e-05 8.656e-03 1.013e-02 1.581e-01

3 Merging directed flows

Note that flows from both dodgr_flows_aggregate() and dodgr_flows_disperse() are directed, so the flow from ‘A’ to ‘B’ will not necessarily equal the flow from ‘B’ to ‘A’. It is often desirable to aggregate flows in an undirected manner, for example for visualisations where plotting pairs of directed flows between each edge if often not feasible for large graphs. Directed flows can be aggregated to equivalent undirected flows with the merge_directed_graph() function:

graph_undir <- merge_directed_graph (graph_f)

Resultant graphs produced by merge_directed_graph() only include those edges having non-zero flows, and so:

nrow (graph_f)

## [1] 6813

nrow (graph_undir) # the latter is much smaller

## [1] 3069

The resultant graph can readily be merged with the original graph to regain the original data on vertex coordinates through

graph <- graph [graph_undir$edge_id, ]
graph$flow <- graph_undir$flow

This graph may then be used to visualise flows with the dodgr_flowmap() function:

graph_f <- graph_f [graph_f$flow > 0, ]
dodgr_flowmap (graph_f, linescale = 5)

4. Flows from spatial interaction models

An additional function, dodgr_flows_si() enables flows to be aggregated according to exponential spatial interaction models. The function is called just as the dodgr_flows_aggregate() call demonstrated above, but without the flows matrix specifying strengths of flows between each pair of points.

graph_f <- dodgr_flows_si (graph, from = from, to = to)
graph_undir <- merge_directed_graph (graph_f)
graph <- graph [graph_undir$edge_id, ]
graph$flow <- graph_undir$flow
graph_f <- graph_f [graph_f$flow > 0, ]
dodgr_flowmap (graph_f, linescale = 5)

Flows in that graph are are notably lower than in the previous one, because that previous one aggregated flows between all pairs of points with no attenuation. Spatial interaction models attenuate both attraction based on how far apart two points are, as well as flows along paths between those points based on an exponential decay model. The documentation for that function describes the several ways this attenuation can be controlled, the easiest of which is via a single numeric value. Reducing the attenuation gives the following result:

graph <- weight_streetnet (hampi, wt_profile = "foot")
graph_f <- dodgr_flows_si (graph, from = from, to = to, k = 1e6)
graph_undir <- merge_directed_graph (graph_f)
graph <- graph [graph_undir$edge_id, ]
graph$flow <- graph_undir$flow
graph_f <- graph_f [graph_f$flow > 0, ]
dodgr_flowmap (graph_f, linescale = 5)