Package 'dodgr'

Description

Note that this routine presumes graphs to be dodgr_streetnet object, with geographical coordinates.

Usage

add_nodes_to_graph(graph, xy, dist_tol = 0.000001, intersections_only = FALSE)

Arguments

Details

This inserts new nodes by extending lines from each input point to the edge with the closest point of perpendicular intersection. That edge is then split at that point of intersection, creating two new edges (or four for directed edges). If intersections_only = FALSE (default), then additional edges are inserted from those intersection points to the input points. If intersections_only = TRUE, then nodes are added by splitting graph edges at points of nearest perpendicular intersection, without adding additional edges out to the actual input points.

In the former case, the properties of those new edges, such as distance and time weightings, are inherited from the edges which are intersected, and may need to be manually modified after calling this function.

Value

A modified version of graph, with additional edges formed by breaking previous edges at nearest perpendicular intersections with the points, xy.

See Also

Other match: match_points_to_graph(), match_points_to_verts(), match_pts_to_graph(), match_pts_to_verts()

Examples

graph <- weight_streetnet (hampi, wt_profile = "foot")
dim (graph)

verts <- dodgr_vertices (graph)
set.seed (2)
npts <- 10
xy <- data.frame (
    x = min (verts$x) + runif (npts) * diff (range (verts$x)),
    y = min (verts$y) + runif (npts) * diff (range (verts$y))
)

graph <- add_nodes_to_graph (graph, xy)
dim (graph) # more edges than original

Description

This function should generally not be needed, except if graph structure has been directly modified other than through dodgr functions; for example by modifying edge weights or distances. Graphs are cached based on the vector of edge IDs, so manual changes to any other attributes will not necessarily be translated into changes in dodgr output unless the cached versions are cleared using this function. See https://github.com/UrbanAnalyst/dodgr/wiki/Caching-of-streetnets-and-contracted-graphs for details of caching process.

Usage

clear_dodgr_cache()

Value

Nothing; the function silently clears any cached objects

See Also

Other cache: dodgr_cache_off(), dodgr_cache_on(), dodgr_load_streetnet(), dodgr_save_streetnet()

Description

Perform timing comparison between different kinds of heaps as well as with equivalent routines from the igraph package. To do this, a random sub-graph containing a defined number of vertices is first selected. Alternatively, this random sub-graph can be pre-generated with the dodgr_sample function and passed directly.

Usage

compare_heaps(graph, nverts = 100, replications = 2)

Arguments

Value

Result of bench::mark comparison.

See Also

Other misc: dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Examples

graph <- weight_streetnet (hampi)
## Not run: 
compare_heaps (graph, nverts = 1000, replications = 1)

## End(Not run)

Description

Distances on dual-weighted directed graphs using priority-queue shortest paths. Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.

The Main Function

• dodgr_dists(): Calculate pair-wise distances between specified pairs of points in a graph.

Functions to Obtain Graphs

• dodgr_streetnet(): Extract a street network in Simple Features (sf) form.

• weight_streetnet(): Convert an sf-formatted street network to a dodgr graph through applying specified weights to all edges.

Functions to Modify Graphs

• dodgr_components(): Number all graph edges according to their presence in distinct connected components.

• dodgr_contract_graph(): Contract a graph by removing redundant edges.

Miscellaneous Functions

• dodgr_sample(): Randomly sample a graph, returning a single connected component of a defined number of vertices.

• dodgr_vertices(): Extract all vertices of a graph.

• compare_heaps(): Compare the performance of different priority queue heap structures for a given type of graph.

Author(s)

Maintainer: Mark Padgham [email protected]

Authors:

• Andreas Petutschnig

• David Cooley

Other contributors:

• Robin Lovelace [contributor]

• Andrew Smith [contributor]

• Malcolm Morgan [contributor]

• Shane Saunders (Original author of included code for priority heaps) [copyright holder]

• Stanislaw Adaszewski (author of include concaveman-cpp code) [copyright holder]

See Also

Useful links:

• https://github.com/UrbanAnalyst/dodgr

• https://urbananalyst.github.io/dodgr/

• Report bugs at https://github.com/UrbanAnalyst/dodgr/issues

Description

This function is useful is speed is paramount, and if graph contraction is not needed. Caching can be switched back on with dodgr_cache_on.

Usage

dodgr_cache_off()

Value

Nothing; the function invisibly returns TRUE if successful.

See Also

Other cache: clear_dodgr_cache(), dodgr_cache_on(), dodgr_load_streetnet(), dodgr_save_streetnet()

Description

This will only have an effect after caching has been turned off with dodgr_cache_off.

Usage

dodgr_cache_on()

Value

Nothing; the function invisibly returns TRUE if successful.

See Also

Other cache: clear_dodgr_cache(), dodgr_cache_off(), dodgr_load_streetnet(), dodgr_save_streetnet()

Description

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

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>)⁠.

See Also

Other centrality: estimate_centrality_threshold(), estimate_centrality_time()

Examples

## Not run: 
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 ()

## End(Not run)

## Not run: 
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)

## End(Not run)

# 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.
## Not run: 
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"])

## End(Not run)

Description

Identify connected components of graph and add corresponding component column to data.frame.

Usage

dodgr_components(graph)

Arguments

Value

Equivalent graph with additional component column, sequentially numbered from 1 = largest component.

See Also

Other modification: dodgr_contract_graph(), dodgr_uncontract_graph()

Examples

graph <- weight_streetnet (hampi)
graph <- dodgr_components (graph)

Description

Removes redundant (straight-line) vertices from graph, leaving only junction vertices.

Usage

dodgr_contract_graph(graph, verts = NULL, nocache = FALSE)

Arguments

Value

A contracted version of the original graph, containing the same number of columns, but with each row representing an edge between two junction vertices (or between the submitted verts, which may or may not be junctions).

See Also

Other modification: dodgr_components(), dodgr_uncontract_graph()

Examples

graph <- weight_streetnet (hampi)
nrow (graph) # 5,973
graph <- dodgr_contract_graph (graph)
nrow (graph) # 662

Description

Graph may have duplicated edges, particularly when extracted as dodgr_streetnet objects. This function de-duplicates any repeated edges, reducing weighted distances and times to the minimal values from all duplicates.

Usage

dodgr_deduplicate_graph(graph)

Arguments

Value

A potentially modified version of graph, with any formerly duplicated edges reduces to single rows containing minimal weighted distances and times.

See Also

Other conversion: dodgr_to_igraph(), dodgr_to_sf(), dodgr_to_sfc(), dodgr_to_tidygraph(), igraph_to_dodgr()

Description

Alias for dodgr_dists

Usage

dodgr_distances(
  graph,
  from = NULL,
  to = NULL,
  shortest = TRUE,
  pairwise = FALSE,
  heap = "BHeap",
  parallel = TRUE,
  quiet = TRUE
)

Arguments

Value

square matrix of distances between nodes

Note

graph must minimally contain three columns of from, to, dist. If an additional column named weight or wt is present, shortest paths are calculated according to values specified in that column; otherwise according to dist values. Either way, final distances between from and to points are calculated by default according to values of dist. That is, paths between any pair of points will be calculated according to the minimal total sum of weight values (if present), while reported distances will be total sums of dist values.

For street networks produced with weight_streetnet, distances may also be calculated along the fastest routes with the shortest = FALSE option. Graphs must in this case have columns of time and time_weighted. Note that the fastest routes will only be approximate when derived from sf-format data generated with the osmdata function osmdata_sf(), and will be much more accurate when derived from sc-format data generated with osmdata_sc(). See weight_streetnet for details.

The from and to columns of graph may be either single columns of numeric or character values specifying the numbers or names of graph vertices, or combinations to two columns specifying geographical (longitude and latitude) coordinates. In the latter case, almost any sensible combination of names will be accepted (for example, ⁠fromx, fromy⁠, ⁠from_x, from_y⁠, or ⁠fr_lat, fr_lon⁠.)

from and to values can be either two-column matrices or equivalent of longitude and latitude coordinates, or else single columns precisely matching node numbers or names given in graph$from or graph$to. If to is NULL, pairwise distances are calculated from all from points to all other nodes in graph. If both from and to are NULL, pairwise distances are calculated between all nodes in graph.

Calculations in parallel (parallel = TRUE) ought very generally be advantageous. For small graphs, calculating distances in parallel is likely to offer relatively little gain in speed, but increases from parallel computation will generally markedly increase with increasing graph sizes. By default, parallel computation uses the maximal number of available cores or threads. This number can be reduced by specifying a value via ⁠RcppParallel::setThreadOptions (numThreads = <desired_number>)⁠. Parallel calculations are, however, not able to be interrupted (for example, by Ctrl-C), and can only be stopped by killing the R process.

See Also

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

Examples

# A simple graph
graph <- data.frame (
    from = c ("A", "B", "B", "B", "C", "C", "D", "D"),
    to = c ("B", "A", "C", "D", "B", "D", "C", "A"),
    d = c (1, 2, 1, 3, 2, 1, 2, 1)
)
dodgr_dists (graph)

# A larger example from the included [hampi()] data.
graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 100)
to <- sample (graph$to_id, size = 50)
d <- dodgr_dists (graph, from = from, to = to)
# d is a 100-by-50 matrix of distances between `from` and `to`

## Not run: 
# a more complex street network example, thanks to @chrijo; see
# https://github.com/UrbanAnalyst/dodgr/issues/47

xy <- rbind (
    c (7.005994, 51.45774), # limbeckerplatz 1 essen germany
    c (7.012874, 51.45041)
) # hauptbahnhof essen germany
xy <- data.frame (lon = xy [, 1], lat = xy [, 2])
essen <- dodgr_streetnet (pts = xy, expand = 0.2, quiet = FALSE)
graph <- weight_streetnet (essen, wt_profile = "foot")
d <- dodgr_dists (graph, from = xy, to = xy)
# First reason why this does not work is because the graph has multiple,
# disconnected components.
table (graph$component)
# reduce to largest connected component, which is always number 1
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)
# should work, but even then note that
table (essen$level)
# There are parts of the network on different building levels (because of
# shopping malls and the like). These may or may not be connected, so it may
# be necessary to filter out particular levels
index <- which (!(essen$level == "-1" | essen$level == "1")) # for example
library (sf) # needed for following sub-select operation
essen <- essen [index, ]
graph <- weight_streetnet (essen, wt_profile = "foot")
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)

## End(Not run)

Description

Calculate matrix of pair-wise distances between points.

Usage

dodgr_dists(
  graph,
  from = NULL,
  to = NULL,
  shortest = TRUE,
  pairwise = FALSE,
  heap = "BHeap",
  parallel = TRUE,
  quiet = TRUE
)

Arguments

Value

square matrix of distances between nodes

Note

graph must minimally contain three columns of from, to, dist. If an additional column named weight or wt is present, shortest paths are calculated according to values specified in that column; otherwise according to dist values. Either way, final distances between from and to points are calculated by default according to values of dist. That is, paths between any pair of points will be calculated according to the minimal total sum of weight values (if present), while reported distances will be total sums of dist values.

For street networks produced with weight_streetnet, distances may also be calculated along the fastest routes with the shortest = FALSE option. Graphs must in this case have columns of time and time_weighted. Note that the fastest routes will only be approximate when derived from sf-format data generated with the osmdata function osmdata_sf(), and will be much more accurate when derived from sc-format data generated with osmdata_sc(). See weight_streetnet for details.

The from and to columns of graph may be either single columns of numeric or character values specifying the numbers or names of graph vertices, or combinations to two columns specifying geographical (longitude and latitude) coordinates. In the latter case, almost any sensible combination of names will be accepted (for example, ⁠fromx, fromy⁠, ⁠from_x, from_y⁠, or ⁠fr_lat, fr_lon⁠.)

from and to values can be either two-column matrices or equivalent of longitude and latitude coordinates, or else single columns precisely matching node numbers or names given in graph$from or graph$to. If to is NULL, pairwise distances are calculated from all from points to all other nodes in graph. If both from and to are NULL, pairwise distances are calculated between all nodes in graph.

Calculations in parallel (parallel = TRUE) ought very generally be advantageous. For small graphs, calculating distances in parallel is likely to offer relatively little gain in speed, but increases from parallel computation will generally markedly increase with increasing graph sizes. By default, parallel computation uses the maximal number of available cores or threads. This number can be reduced by specifying a value via ⁠RcppParallel::setThreadOptions (numThreads = <desired_number>)⁠. Parallel calculations are, however, not able to be interrupted (for example, by Ctrl-C), and can only be stopped by killing the R process.

See Also

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

Examples

# A simple graph
graph <- data.frame (
    from = c ("A", "B", "B", "B", "C", "C", "D", "D"),
    to = c ("B", "A", "C", "D", "B", "D", "C", "A"),
    d = c (1, 2, 1, 3, 2, 1, 2, 1)
)
dodgr_dists (graph)

# A larger example from the included [hampi()] data.
graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 100)
to <- sample (graph$to_id, size = 50)
d <- dodgr_dists (graph, from = from, to = to)
# d is a 100-by-50 matrix of distances between `from` and `to`

## Not run: 
# a more complex street network example, thanks to @chrijo; see
# https://github.com/UrbanAnalyst/dodgr/issues/47

xy <- rbind (
    c (7.005994, 51.45774), # limbeckerplatz 1 essen germany
    c (7.012874, 51.45041)
) # hauptbahnhof essen germany
xy <- data.frame (lon = xy [, 1], lat = xy [, 2])
essen <- dodgr_streetnet (pts = xy, expand = 0.2, quiet = FALSE)
graph <- weight_streetnet (essen, wt_profile = "foot")
d <- dodgr_dists (graph, from = xy, to = xy)
# First reason why this does not work is because the graph has multiple,
# disconnected components.
table (graph$component)
# reduce to largest connected component, which is always number 1
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)
# should work, but even then note that
table (essen$level)
# There are parts of the network on different building levels (because of
# shopping malls and the like). These may or may not be connected, so it may
# be necessary to filter out particular levels
index <- which (!(essen$level == "-1" | essen$level == "1")) # for example
library (sf) # needed for following sub-select operation
essen <- essen [index, ]
graph <- weight_streetnet (essen, wt_profile = "foot")
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)

## End(Not run)

Description

Cumulative distances along different edge categories

Usage

dodgr_dists_categorical(
  graph,
  from = NULL,
  to = NULL,
  proportions_only = FALSE,
  pairwise = FALSE,
  dlimit = NULL,
  heap = "BHeap",
  quiet = TRUE
)

Arguments

Value

If to is specified, a list of distance matrices of equal dimensions (length(from), length(to)), the first of which ("distance") holds the final distances, while the rest are one matrix for each unique value of "edge_type", holding the distances traversed along those types of edges only. Otherwise, a single matrix of total distances along all ways from each point out to the specified value of dlimit, along with distances along each of the different kinds of ways specified in the "edge_type" column of the input graph.

Note

The "edge_type" column in the graph can contain any kind of discrete or categorical values, although integer values of 0 are not permissible. NA values are ignored. The function requires one full distance matrix to be stored for each category of "edge_type" (unless proportions_only = TRUE). It is wise to keep numbers of discrete types as low as possible, especially for large distance matrices.

Setting the proportions_only flag to TRUE may be advantageous for large jobs, because this avoids construction of the full matrices. This may speed up calculations, but perhaps more importantly it may make possible calculations which would otherwise require distance matrices too large to be directly stored.

Calculations are not able to be interrupted (for example, by Ctrl-C), and can only be stopped by killing the R process.

See Also

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

Examples

# Prepare a graph for categorical routing by including an "edge_type" column
graph <- weight_streetnet (hampi, wt_profile = "foot")
graph <- graph [graph$component == 1, ]
graph$edge_type <- graph$highway
# Define start and end points for categorical distances; using all vertices
# here.
length (unique (graph$edge_type)) # Number of categories
v <- dodgr_vertices (graph)
from <- to <- v$id [1:100]
d <- dodgr_dists_categorical (graph, from, to)
class (d)
length (d)
sapply (d, dim)
# 9 distance matrices, all of same dimensions, first of which is standard
# distance matrix
s <- summary (d) # print summary as proportions along each "edge_type"
# or directly calculate proportions only
dodgr_dists_categorical (graph, from, to,
    proportions_only = TRUE
)

# Pairwise distances return single matrix with number of rows equal to 'from'
# / 'to', and number of columns equal to number of edge types plus one for
# total distances.
d <- dodgr_dists_categorical (graph, from, to, pairwise = TRUE)
class (d)
dim (d)

# The 'dlimit' parameter can be used to calculate total distances along each
# category of edges from a set of points out to specified threshold:
dlimit <- 2000 # in metres
d <- dodgr_dists_categorical (graph, from, dlimit = dlimit)
dim (d) # length(from), length(unique(edge_type)) + 1

Description

Calculate vector of shortest distances from a series of 'from' points to nearest one of series of 'to' points.

Usage

dodgr_dists_nearest(
  graph,
  from = NULL,
  to = NULL,
  shortest = TRUE,
  heap = "BHeap",
  quiet = TRUE
)

Arguments

Value

Vector of distances, one element for each 'from' point giving the distance to the nearest 'to' point.

Note

graph must minimally contain three columns of from, to, dist. If an additional column named weight or wt is present, shortest paths are calculated according to values specified in that column; otherwise according to dist values. Either way, final distances between from and to points are calculated by default according to values of dist. That is, paths between any pair of points will be calculated according to the minimal total sum of weight values (if present), while reported distances will be total sums of dist values.

For street networks produced with weight_streetnet, distances may also be calculated along the fastest routes with the shortest = FALSE option. Graphs must in this case have columns of time and time_weighted. Note that the fastest routes will only be approximate when derived from sf-format data generated with the osmdata function osmdata_sf(), and will be much more accurate when derived from sc-format data generated with osmdata_sc(). See weight_streetnet for details.

The from and to columns of graph may be either single columns of numeric or character values specifying the numbers or names of graph vertices, or combinations to two columns specifying geographical (longitude and latitude) coordinates. In the latter case, almost any sensible combination of names will be accepted (for example, ⁠fromx, fromy⁠, ⁠from_x, from_y⁠, or ⁠fr_lat, fr_lon⁠.)

from and to values can be either two-column matrices or equivalent of longitude and latitude coordinates, or else single columns precisely matching node numbers or names given in graph$from or graph$to. If to is NULL, pairwise distances are calculated from all from points to all other nodes in graph. If both from and to are NULL, pairwise distances are calculated between all nodes in graph.

Calculations are always calculated in parallel, using multiple threads.

See Also

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

Examples

# A simple graph
graph <- data.frame (
    from = c ("A", "B", "B", "B", "C", "C", "D", "D"),
    to = c ("B", "A", "C", "D", "B", "D", "C", "A"),
    d = c (1, 2, 1, 3, 2, 1, 2, 1)
)
dodgr_dists (graph)

# A larger example from the included [hampi()] data.
graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 100)
to <- sample (graph$to_id, size = 50)
d <- dodgr_dists (graph, from = from, to = to)
# d is a 100-by-50 matrix of distances between `from` and `to`

## Not run: 
# a more complex street network example, thanks to @chrijo; see
# https://github.com/UrbanAnalyst/dodgr/issues/47

xy <- rbind (
    c (7.005994, 51.45774), # limbeckerplatz 1 essen germany
    c (7.012874, 51.45041)
) # hauptbahnhof essen germany
xy <- data.frame (lon = xy [, 1], lat = xy [, 2])
essen <- dodgr_streetnet (pts = xy, expand = 0.2, quiet = FALSE)
graph <- weight_streetnet (essen, wt_profile = "foot")
d <- dodgr_dists (graph, from = xy, to = xy)
# First reason why this does not work is because the graph has multiple,
# disconnected components.
table (graph$component)
# reduce to largest connected component, which is always number 1
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)
# should work, but even then note that
table (essen$level)
# There are parts of the network on different building levels (because of
# shopping malls and the like). These may or may not be connected, so it may
# be necessary to filter out particular levels
index <- which (!(essen$level == "-1" | essen$level == "1")) # for example
library (sf) # needed for following sub-select operation
essen <- essen [index, ]
graph <- weight_streetnet (essen, wt_profile = "foot")
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)

## End(Not run)

Description

Create a map of the output of dodgr_flows_aggregate or dodgr_flows_disperse

Usage

dodgr_flowmap(net, bbox = NULL, linescale = 1)

Arguments

Note

net should be first passed through merge_directed_graph prior to plotting, otherwise lines for different directions will be overlaid.

See Also

Other misc: compare_heaps(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

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)
## Not run: 
dodgr_flowmap (graph_undir)

## End(Not run)

Description

Aggregate flows throughout a network based on an input matrix of flows between all pairs of from and to points.

Usage

dodgr_flows_aggregate(
  graph,
  from,
  to,
  flows,
  pairwise = FALSE,
  contract = TRUE,
  heap = "BHeap",
  tol = 0.000000000001,
  norm_sums = TRUE,
  quiet = TRUE
)

Arguments

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.

Flows are 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>)⁠.

See Also

Other distances: dodgr_distances(), dodgr_dists(), dodgr_dists_categorical(), dodgr_dists_nearest(), dodgr_flows_disperse(), dodgr_flows_si(), 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

# The following code can be used to convert the resultant graph to an `sf`
# object suitable for plotting
## Not run: 
gsf <- dodgr_to_sf (graph_undir)

# example of plotting with the 'mapview' package
library (mapview)
flow <- gsf$flow / max (gsf$flow)
ncols <- 30
cols <- c ("lawngreen", "red")
colranmp <- colorRampPalette (cols) (ncols) [ceiling (ncols * flow)]
mapview (gsf, color = colranmp, lwd = 10 * flow)

## End(Not run)

# 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.
## Not run: 
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)
# then extract the start and end points of each of the original 'routes_fast'
# lines and use these for routing with `dodgr`
l <- lapply (routes_fast@lines, function (i) {
    c (
        sp::coordinates (i) [[1]] [1, ],
        tail (sp::coordinates (i) [[1]], 1)
    )
})
l <- do.call (rbind, l)
xy_start <- l [, 1:2]
xy_end <- l [, 3:4]
# Then just specify a generic OD matrix with uniform values of 1:
flows <- matrix (1, nrow = nrow (l), ncol = nrow (l))
# We need to specify both a `type` and `id` column for the
# \link{weight_streetnet} function.
r$type <- 1
r$id <- seq (nrow (r))
graph <- weight_streetnet (
    r,
    type_col = "type",
    id_col = "id",
    wt_profile = 1
)
f <- dodgr_flows_aggregate (
    graph,
    from = xy_start,
    to = xy_end,
    flows = flows
)
# Then merge directed flows and convert to \pkg{sf} for plotting as before:
f <- merge_directed_graph (f)
geoms <- dodgr_to_sfc (f)
gc <- dodgr_contract_graph (f)
gsf <- sf::st_sf (geoms)
gsf$flow <- gc$flow
# sf plot:
plot (gsf ["flow"])

## End(Not run)

Description

Disperse flows throughout a network based on a input vectors of origin points and associated densities

Usage

dodgr_flows_disperse(
  graph,
  from,
  dens,
  k = 500,
  contract = TRUE,
  heap = "BHeap",
  tol = 0.000000000001,
  quiet = TRUE
)

Arguments

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.

See Also

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

Examples

graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 10)
dens <- rep (1, length (from)) # Uniform densities
graph <- dodgr_flows_disperse (graph, from = from, dens = dens)
# 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)

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

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

Description

Calculate fundamental cycles on a FULL (that is, non-contracted) graph.

Usage

dodgr_full_cycles(graph, graph_max_size = 10000, expand = 0.05)

Arguments

Note

This function converts the graph to its contracted form, calculates the fundamental cycles on that version, and then expands these cycles back onto the original graph. This is far more computationally efficient than calculating fundamental cycles on a full (non-contracted) graph.

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Examples

## Not run: 
net <- weight_streetnet (hampi)
graph <- dodgr_contract_graph (net)
cyc1 <- dodgr_fundamental_cycles (graph)
cyc2 <- dodgr_full_cycles (net)

## End(Not run)
# cyc2 has same number of cycles, but each one is generally longer, through
# including all points intermediate to junctions; cyc1 has cycles composed of
# junction points only.

Description

Calculate fundamental cycles in a graph.

Usage

dodgr_fundamental_cycles(
  graph,
  vertices = NULL,
  graph_max_size = 10000,
  expand = 0.05
)

Arguments

Value

List of cycle paths, in terms of vertex IDs in graph and, for spatial graphs, the corresponding coordinates.

Note

Calculation of fundamental cycles is VERY computationally demanding, and this function should only be executed on CONTRACTED graphs (that is, graphs returned from dodgr_contract_graph), and even than may take a long time to execute. Results for full graphs can be obtained with the function dodgr_full_cycles. The computational complexity can also not be calculated in advance, and so the parameter graph_max_size will lead to graphs larger than that (measured in numbers of edges) being cut into smaller parts. (Note that that is only possible for spatial graphs, meaning that it is not at all possible to apply this function to large, non-spatial graphs.) Each of these smaller parts will be expanded by the specified amount (expand), and cycles found within. The final result is obtained by aggregating all of these cycles and removing any repeated ones arising due to overlap in the expanded portions. Finally, note that this procedure of cutting graphs into smaller, computationally manageable sub-graphs provides only an approximation and may not yield all fundamental cycles.

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Examples

net <- weight_streetnet (hampi)
graph <- dodgr_contract_graph (net)
verts <- dodgr_vertices (graph)
cyc <- dodgr_fundamental_cycles (graph, verts)

Description

Insert a new node or vertex into a network

Usage

dodgr_insert_vertex(graph, v1, v2, x = NULL, y = NULL)

Arguments

Value

A modified graph with specified edge between defined start and end vertices split into two edges either side of new vertex.

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Examples

graph <- weight_streetnet (hampi)
e1 <- sample (nrow (graph), 1)
v1 <- graph$from_id [e1]
v2 <- graph$to_id [e1]
# insert new vertex in the middle of that randomly-selected edge:
graph2 <- dodgr_insert_vertex (graph, v1, v2)
nrow (graph)
nrow (graph2) # new edges added to graph2

Description

Function is fully vectorized to calculate accept vectors of central points and vectors defining multiple isochrone thresholds.

Usage

dodgr_isochrones(
  graph,
  from = NULL,
  tlim = NULL,
  concavity = 0,
  length_threshold = 0,
  heap = "BHeap"
)

Arguments

Value

A single data.frame of isochrones as points sorted anticlockwise around each origin (from) point, with columns denoting the from points and tlim value(s). The isochrones are given as id values and associated coordinates of the series of points from each from point at the specified isochrone times.

Isochrones are 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>)⁠.

Note

Isodists are 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>)⁠.

See Also

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

Examples

## Not run: 
# Use osmdata package to extract 'SC'-format data:
library (osmdata)
dat <- opq ("hampi india") %>%
    add_osm_feature (key = "highway") %>%
    osmdata_sc ()
graph <- weight_streetnet (dat)
from <- sample (graph$.vx0, size = 100)
tlim <- c (5, 10, 20, 30, 60) * 60 # times in seconds
x <- dodgr_isochrones (graph, from = from, tlim)

## End(Not run)

Description

Function is fully vectorized to calculate accept vectors of central points and vectors defining multiple isodistances.

Usage

dodgr_isodists(
  graph,
  from = NULL,
  dlim = NULL,
  concavity = 0,
  length_threshold = 0,
  contract = TRUE,
  heap = "BHeap"
)

Arguments

Value

A single data.frame of isodistances as points sorted anticlockwise around each origin (from) point, with columns denoting the from points and dlim value(s). The isodistance contours are given as id values and associated coordinates of the series of points from each from point at the specified isodistances.

Note

Isodists are 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>)⁠.

See Also

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

Examples

graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 100)
dlim <- c (1, 2, 5, 10, 20) * 100
d <- dodgr_isodists (graph, from = from, dlim)

Description

Returns lists of all network vertices contained within the contours. Function is fully vectorized to calculate accept vectors of central points and vectors defining multiple isochrone thresholds. Provide one or more dlim values for isodistances, or one or more tlim values for isochrones.

Usage

dodgr_isoverts(graph, from = NULL, dlim = NULL, tlim = NULL, heap = "BHeap")

Arguments

Value

A single data.frame of vertex IDs, with columns denoting the from points and tlim value(s). The isochrones are given as id values and associated coordinates of the series of points from each from point at the specified isochrone times.

Isoverts are 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>)⁠.

See Also

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

Examples

## Not run: 
# Use osmdata package to extract 'SC'-format data:
library (osmdata)
dat <- opq ("hampi india") %>%
    add_osm_feature (key = "highway") %>%
    osmdata_sc ()
graph <- weight_streetnet (dat)
from <- sample (graph$.vx0, size = 100)
tlim <- c (5, 10, 20, 30, 60) * 60 # times in seconds
x <- dodgr_isoverts (graph, from = from, tlim)

## End(Not run)

Load a street network previously saved with dodgr_save_streetnet.

Description

This always returns the full, non-contracted graph. The contracted graph can be generated by passing the result to dodgr_contract_graph.

Usage

dodgr_load_streetnet(filename)

Arguments

See Also

Other cache: clear_dodgr_cache(), dodgr_cache_off(), dodgr_cache_on(), dodgr_save_streetnet()

Examples

net <- weight_streetnet (hampi)
f <- file.path (tempdir (), "streetnet.Rds")
dodgr_save_streetnet (net, f)
clear_dodgr_cache () # rm cached objects from tempdir
# at some later time, or in a new R session:
net <- dodgr_load_streetnet (f)

Description

Calculate lists of pair-wise shortest paths between points.

Usage

dodgr_paths(
  graph,
  from,
  to,
  vertices = TRUE,
  pairwise = FALSE,
  heap = "BHeap",
  quiet = TRUE
)

Arguments

Value

List of list of paths tracing all connections between nodes such that if x <- dodgr_paths (graph, from, to), then the path between from[i] and to[j] is x [[i]] [[j]]. Each individual path is then a vector of integers indexing into the rows of graph if vertices = FALSE, or into the rows of dodgr_vertices (graph) if vertices = TRUE.

Note

graph must minimally contain four columns of from, to, dist. If an additional column named weight or wt is present, shortest paths are calculated according to values specified in that column; otherwise according to dist values. Either way, final distances between from and to points are calculated according to values of dist. That is, paths between any pair of points will be calculated according to the minimal total sum of weight values (if present), while reported distances will be total sums of dist values.

The from and to columns of graph may be either single columns of numeric or character values specifying the numbers or names of graph vertices, or combinations to two columns specifying geographical (longitude and latitude) coordinates. In the latter case, almost any sensible combination of names will be accepted (for example, ⁠fromx, fromy⁠, ⁠from_x, from_y⁠, or ⁠fr_lat, fr_lon⁠.)

from and to values can be either two-column matrices of equivalent of longitude and latitude coordinates, or else single columns precisely matching node numbers or names given in graph$from or graph$to. If to is missing, pairwise distances are calculated between all points specified in from. If neither from nor to are specified, pairwise distances are calculated between all nodes in graph.

See Also

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

Examples

graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 100)
to <- sample (graph$to_id, size = 50)
dp <- dodgr_paths (graph, from = from, to = to)
# dp is a list with 100 items, and each of those 100 items has 30 items, each
# of which is a single path listing all vertiex IDs as taken from `graph`.

# it is also possible to calculate paths between pairwise start and end
# points
from <- sample (graph$from_id, size = 5)
to <- sample (graph$to_id, size = 5)
dp <- dodgr_paths (graph, from = from, to = to, pairwise = TRUE)
# dp is a list of 5 items, each of which just has a single path between each
# pairwise from and to point.

Description

Sample a random but connected sub-component of a graph

Usage

dodgr_sample(graph, nverts = 1000)

Arguments

Value

A connected sub-component of graph

Note

Graphs may occasionally have nverts + 1 vertices, rather than the requested nverts.

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Examples

graph <- weight_streetnet (hampi)
nrow (graph) # 5,742
graph <- dodgr_sample (graph, nverts = 200)
nrow (graph) # generally around 400 edges
nrow (dodgr_vertices (graph)) # 200

Description

The weight_streetnet function returns a single data.frame object, the processing of which also relies on a couple of cached lookup-tables to match edges in the data.frame to objects in the original input data. It automatically calculates and caches a contracted version of the same graph, to enable rapid conversion between contracted and uncontracted forms. This function saves all of these items in a single .Rds file, so that a the result of a weight_streetnet call can be rapidly loaded into a workspace in subsequent sessions, rather than re-calculating the entire weighted network.

Usage

dodgr_save_streetnet(net, filename = NULL)

Arguments

Note

This may take some time if dodgr_cache_off has been called. The contracted version of the graph is also saved, and so must be calculated if it has not previously been automatically cached.

See Also

Other cache: clear_dodgr_cache(), dodgr_cache_off(), dodgr_cache_on(), dodgr_load_streetnet()

Examples

net <- weight_streetnet (hampi)
f <- file.path (tempdir (), "streetnet.Rds")
dodgr_save_streetnet (net, f)
clear_dodgr_cache () # rm cached objects from tempdir
# at some later time, or in a new R session:
net <- dodgr_load_streetnet (f)

Description

Convert sf LINESTRING objects to POLYGON objects representing all fundamental cycles within the LINESTRING objects.

Usage

dodgr_sflines_to_poly(sflines, graph_max_size = 10000, expand = 0.05)

Arguments

Value

An sf::sfc collection of POLYGON objects.

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Description

Use the osmdata package to extract the street network for a given location. For routing between a given set of points (passed as pts), the bbox argument may be omitted, in which case a bounding box will be constructed by expanding the range of pts by the relative amount of expand.

Usage

dodgr_streetnet(bbox, pts = NULL, expand = 0.05, quiet = TRUE)

Arguments

Value

A Simple Features (sf) object with coordinates of all lines in the street network.

Note

Calls to this function may return "General overpass server error" with a note that "Query timed out." The overpass served used to access the data has a sophisticated queueing system which prioritises requests that are likely to require little time. These timeout errors can thus generally not be circumvented by changing "timeout" options on the HTTP requests, and should rather be interpreted to indicate that a request is too large, and may need to be refined, or somehow broken up into smaller queries.

See Also

Other extraction: dodgr_streetnet_sc(), weight_railway(), weight_streetnet()

Examples

## Not run: 
streetnet <- dodgr_streetnet ("hampi india", expand = 0)
# convert to form needed for `dodgr` functions:
graph <- weight_streetnet (streetnet)
nrow (graph) # around 5,900 edges
# Alternative ways of extracting street networks by using a small selection
# of graph vertices to define bounding box:
verts <- dodgr_vertices (graph)
verts <- verts [sample (nrow (verts), size = 200), ]
streetnet <- dodgr_streetnet (pts = verts, expand = 0)
graph <- weight_streetnet (streetnet)
nrow (graph)
# This will generally have many more rows because most street networks
# include streets that extend considerably beyond the specified bounding box.

# bbox can also be a polygon:
bb <- osmdata::getbb ("gent belgium") # rectangular bbox
nrow (dodgr_streetnet (bbox = bb)) # around 30,000
bb <- osmdata::getbb ("gent belgium", format_out = "polygon")
nrow (dodgr_streetnet (bbox = bb)) # around 17,000
# The latter has fewer rows because only edges within polygon are returned

# Example with access restrictions
bbox <- c (-122.2935, 47.62663, -122.28, 47.63289)
x <- dodgr_streetnet_sc (bbox)
net <- weight_streetnet (x, keep_cols = "access", turn_penalty = TRUE)
# has many streets with "access" = "private"; these can be removed like this:
net2 <- net [which (!net$access != "private"), ]
# or modified in some other way such as strongly penalizing use of those
# streets:
index <- which (net$access == "private")
net$time_weighted [index] <- net$time_weighted [index] * 100

## End(Not run)

Description

Use the osmdata package to extract the street network for a given location and return it in SC-format. For routing between a given set of points (passed as pts), the bbox argument may be omitted, in which case a bounding box will be constructed by expanding the range of pts by the relative amount of expand.

Usage

dodgr_streetnet_sc(bbox, pts = NULL, expand = 0.05, quiet = TRUE)

Arguments

Value

A Simple Features (sf) object with coordinates of all lines in the street network.

Note

Calls to this function may return "General overpass server error" with a note that "Query timed out." The overpass served used to access the data has a sophisticated queueing system which prioritises requests that are likely to require little time. These timeout errors can thus generally not be circumvented by changing "timeout" options on the HTTP requests, and should rather be interpreted to indicate that a request is too large, and may need to be refined, or somehow broken up into smaller queries.

See Also

Other extraction: dodgr_streetnet(), weight_railway(), weight_streetnet()

Examples

## Not run: 
streetnet <- dodgr_streetnet ("hampi india", expand = 0)
# convert to form needed for `dodgr` functions:
graph <- weight_streetnet (streetnet)
nrow (graph) # around 5,900 edges
# Alternative ways of extracting street networks by using a small selection
# of graph vertices to define bounding box:
verts <- dodgr_vertices (graph)
verts <- verts [sample (nrow (verts), size = 200), ]
streetnet <- dodgr_streetnet (pts = verts, expand = 0)
graph <- weight_streetnet (streetnet)
nrow (graph)
# This will generally have many more rows because most street networks
# include streets that extend considerably beyond the specified bounding box.

# bbox can also be a polygon:
bb <- osmdata::getbb ("gent belgium") # rectangular bbox
nrow (dodgr_streetnet (bbox = bb)) # around 30,000
bb <- osmdata::getbb ("gent belgium", format_out = "polygon")
nrow (dodgr_streetnet (bbox = bb)) # around 17,000
# The latter has fewer rows because only edges within polygon are returned

# Example with access restrictions
bbox <- c (-122.2935, 47.62663, -122.28, 47.63289)
x <- dodgr_streetnet_sc (bbox)
net <- weight_streetnet (x, keep_cols = "access", turn_penalty = TRUE)
# has many streets with "access" = "private"; these can be removed like this:
net2 <- net [which (!net$access != "private"), ]
# or modified in some other way such as strongly penalizing use of those
# streets:
index <- which (net$access == "private")
net$time_weighted [index] <- net$time_weighted [index] * 100

## End(Not run)

Description

Calculate matrix of pair-wise travel times between points.

Usage

dodgr_times(graph, from = NULL, to = NULL, shortest = FALSE, heap = "BHeap")

Arguments

Value

square matrix of distances between nodes

Note

graph must minimally contain three columns of from, to, dist. If an additional column named weight or wt is present, shortest paths are calculated according to values specified in that column; otherwise according to dist values. Either way, final distances between from and to points are calculated by default according to values of dist. That is, paths between any pair of points will be calculated according to the minimal total sum of weight values (if present), while reported distances will be total sums of dist values.

For street networks produced with weight_streetnet, distances may also be calculated along the fastest routes with the shortest = FALSE option. Graphs must in this case have columns of time and time_weighted. Note that the fastest routes will only be approximate when derived from sf-format data generated with the osmdata function osmdata_sf(), and will be much more accurate when derived from sc-format data generated with osmdata_sc(). See weight_streetnet for details.

The from and to columns of graph may be either single columns of numeric or character values specifying the numbers or names of graph vertices, or combinations to two columns specifying geographical (longitude and latitude) coordinates. In the latter case, almost any sensible combination of names will be accepted (for example, ⁠fromx, fromy⁠, ⁠from_x, from_y⁠, or ⁠fr_lat, fr_lon⁠.)

from and to values can be either two-column matrices or equivalent of longitude and latitude coordinates, or else single columns precisely matching node numbers or names given in graph$from or graph$to. If to is NULL, pairwise distances are calculated from all from points to all other nodes in graph. If both from and to are NULL, pairwise distances are calculated between all nodes in graph.

Calculations in parallel (parallel = TRUE) ought very generally be advantageous. For small graphs, calculating distances in parallel is likely to offer relatively little gain in speed, but increases from parallel computation will generally markedly increase with increasing graph sizes. By default, parallel computation uses the maximal number of available cores or threads. This number can be reduced by specifying a value via ⁠RcppParallel::setThreadOptions (numThreads = <desired_number>)⁠. Parallel calculations are, however, not able to be interrupted (for example, by Ctrl-C), and can only be stopped by killing the R process.

See Also

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

Examples

# A simple graph
graph <- data.frame (
    from = c ("A", "B", "B", "B", "C", "C", "D", "D"),
    to = c ("B", "A", "C", "D", "B", "D", "C", "A"),
    d = c (1, 2, 1, 3, 2, 1, 2, 1)
)
dodgr_dists (graph)

# A larger example from the included [hampi()] data.
graph <- weight_streetnet (hampi)
from <- sample (graph$from_id, size = 100)
to <- sample (graph$to_id, size = 50)
d <- dodgr_dists (graph, from = from, to = to)
# d is a 100-by-50 matrix of distances between `from` and `to`

## Not run: 
# a more complex street network example, thanks to @chrijo; see
# https://github.com/UrbanAnalyst/dodgr/issues/47

xy <- rbind (
    c (7.005994, 51.45774), # limbeckerplatz 1 essen germany
    c (7.012874, 51.45041)
) # hauptbahnhof essen germany
xy <- data.frame (lon = xy [, 1], lat = xy [, 2])
essen <- dodgr_streetnet (pts = xy, expand = 0.2, quiet = FALSE)
graph <- weight_streetnet (essen, wt_profile = "foot")
d <- dodgr_dists (graph, from = xy, to = xy)
# First reason why this does not work is because the graph has multiple,
# disconnected components.
table (graph$component)
# reduce to largest connected component, which is always number 1
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)
# should work, but even then note that
table (essen$level)
# There are parts of the network on different building levels (because of
# shopping malls and the like). These may or may not be connected, so it may
# be necessary to filter out particular levels
index <- which (!(essen$level == "-1" | essen$level == "1")) # for example
library (sf) # needed for following sub-select operation
essen <- essen [index, ]
graph <- weight_streetnet (essen, wt_profile = "foot")
graph <- graph [which (graph$component == 1), ]
d <- dodgr_dists (graph, from = xy, to = xy)

## End(Not run)

Description

Convert a dodgr graph to an igraph.

Usage

dodgr_to_igraph(graph, weight_column = "d")

Arguments

Value

The igraph equivalent of the input. Note that this will not be a dual-weighted graph.

See Also

igraph_to_dodgr

Other conversion: dodgr_deduplicate_graph(), dodgr_to_sf(), dodgr_to_sfc(), dodgr_to_tidygraph(), igraph_to_dodgr()

Examples

graph <- weight_streetnet (hampi)
graphi <- dodgr_to_igraph (graph)

Description

Works by aggregating edges into LINESTRING objects representing longest sequences between all junction nodes. The resultant objects will generally contain more LINESTRING objects than the original sf object, because the former will be bisected at every junction point.

Usage

dodgr_to_sf(graph)

Arguments

Value

Equivalent object of class sf.

Note

Requires the sf package to be installed.

See Also

Other conversion: dodgr_deduplicate_graph(), dodgr_to_igraph(), dodgr_to_sfc(), dodgr_to_tidygraph(), igraph_to_dodgr()

Examples

hw <- weight_streetnet (hampi)
nrow (hw) # 5,729 edges
xy <- dodgr_to_sf (hw)
dim (xy) # 764 edges; 14 attributes

Description

Convert a dodgr graph into a list composed of two objects: dat, a data.frame; and geometry, an sfc object from the (sf) package. Works by aggregating edges into LINESTRING objects representing longest sequences between all junction nodes. The resultant objects will generally contain more LINESTRING objects than the original sf object, because the former will be bisected at every junction point.

Usage

dodgr_to_sfc(graph)

Arguments

Value

A list containing (1) A data.frame of data associated with the sf geometries; and (ii) A Simple Features Collection (sfc) list of LINESTRING objects.

Note

The output of this function corresponds to the edges obtained from dodgr_contract_graph. This function does not require the sf package to be installed; the corresponding function that creates a full sf object - dodgr_to_sf does requires sf to be installed.

See Also

Other conversion: dodgr_deduplicate_graph(), dodgr_to_igraph(), dodgr_to_sf(), dodgr_to_tidygraph(), igraph_to_dodgr()

Examples

hw <- weight_streetnet (hampi)
nrow (hw)
xy <- dodgr_to_sfc (hw)
dim (hw) # 5.845 edges
length (xy$geometry) # more linestrings aggregated from those edges
nrow (hampi) # than the 191 linestrings in original sf object
dim (xy$dat) # same number of rows as there are geometries
# The dodgr_to_sf function then just implements this final conversion:
# sf::st_sf (xy$dat, geometry = xy$geometry, crs = 4326)

Description

Convert a dodgr graph to an tidygraph.

Usage

dodgr_to_tidygraph(graph)

Arguments

Value

The tidygraph equivalent of the input

See Also

Other conversion: dodgr_deduplicate_graph(), dodgr_to_igraph(), dodgr_to_sf(), dodgr_to_sfc(), igraph_to_dodgr()

Examples

graph <- weight_streetnet (hampi)
grapht <- dodgr_to_tidygraph (graph)

Description

Revert a contracted graph created with dodgr_contract_graph back to a full, uncontracted version. This function is mostly used for the side effect of mapping any new columns inserted on to the contracted graph back on to the original graph, as demonstrated in the example.

Usage

dodgr_uncontract_graph(graph)

Arguments

Details

Note that this function will generally not recover original graphs submitted to dodgr_contract_graph. Specifically, the sequence dodgr_contract_graph(graph) |> dodgr_uncontract_graph() will generally produce a graph with fewer edges than the original. This is because graphs may have multiple paths between a given pair of points. Contraction will reduce these to the single path with the shortest weighted distance (or time), and uncontraction will restore only that single edge with shortest weighted distance, and not any original edges which may have had longer weighted distances.

Value

A single data.frame representing the equivalent original, uncontracted graph.

See Also

Other modification: dodgr_components(), dodgr_contract_graph()

Examples

graph0 <- weight_streetnet (hampi)
nrow (graph0) # 6,813
graph1 <- dodgr_contract_graph (graph0)
nrow (graph1) # 760
graph2 <- dodgr_uncontract_graph (graph1)
nrow (graph2) # 6,813

# Insert new data on to the contracted graph and uncontract it:
graph1$new_col <- runif (nrow (graph1))
graph3 <- dodgr_uncontract_graph (graph1)
# graph3 is then the uncontracted graph which includes "new_col" as well
dim (graph0)
dim (graph3)

Description

Extract vertices of graph, including spatial coordinates if included.

Usage

dodgr_vertices(graph)

Arguments

Value

A data.frame of vertices with unique numbers (n).

Note

Values of n are 0-indexed

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), merge_directed_graph(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

Examples

graph <- weight_streetnet (hampi)
v <- dodgr_vertices (graph)

Estimate a value for the 'dist_threshold' parameter of the dodgr_centrality function.

Description

Providing distance thresholds to this function generally provides considerably speed gains, and results in approximations of centrality. This function enables the determination of values of 'dist_threshold' corresponding to specific degrees of accuracy.

Usage

estimate_centrality_threshold(graph, tolerance = 0.001)

Arguments

Value

A single value for 'dist_threshold' giving the required tolerance.

Note

This function may take some time to execute. While running, it displays ongoing information on screen of estimated values of 'dist_threshold' and associated errors. Thresholds are progressively increased until the error is reduced below the specified tolerance.

See Also

Other centrality: dodgr_centrality(), estimate_centrality_time()

Description

The 'dodgr' centrality functions are designed to be applied to potentially very large graphs, and may take considerable time to execute. This helper function estimates how long a centrality function may take for a given graph and given value of 'dist_threshold' estimated via the estimate_centrality_threshold function.

Usage

estimate_centrality_time(
  graph,
  contract = TRUE,
  edges = TRUE,
  dist_threshold = NULL,
  heap = "BHeap"
)

Arguments

Value

An estimated calculation time for calculating centrality for the given value of 'dist_threshold'

Note

This function may take some time to execute. While running, it displays ongoing information on screen of estimated values of 'dist_threshold' and associated errors. Thresholds are progressively increased until the error is reduced below the specified tolerance.

See Also

Other centrality: dodgr_centrality(), estimate_centrality_threshold()

Description

A sample street network from the township of Hampi, Karnataka, India.

Format

A Simple Features sf data.frame containing the street network of Hampi.

Note

Can be re-created with the following command, which also removes extraneous columns to reduce size:

See Also

Other data: os_roads_bristol, weighting_profiles

Examples

## Not run: 
hampi <- dodgr_streetnet ("hampi india")
cols <- c ("osm_id", "highway", "oneway", "geometry")
hampi <- hampi [, which (names (hampi) %in% cols)]

## End(Not run)
# this 'sf data.frame' can be converted to a 'dodgr' network with
net <- weight_streetnet (hampi, wt_profile = "foot")

Description

Convert a igraph network to an equivalent dodgr representation.

Usage

igraph_to_dodgr(graph)

Arguments

Value

The dodgr equivalent of the input.

See Also

dodgr_to_igraph

Other conversion: dodgr_deduplicate_graph(), dodgr_to_igraph(), dodgr_to_sf(), dodgr_to_sfc(), dodgr_to_tidygraph()

Examples

graph <- weight_streetnet (hampi)
graphi <- dodgr_to_igraph (graph)
graph2 <- igraph_to_dodgr (graphi)
identical (graph2, graph) # FALSE

Alias for match_pts_to_graph

Description

Match spatial points to the edges of a spatial graph, through finding the edge with the closest perpendicular intersection. NOTE: Intersections are calculated geometrically, and presume planar geometry. It is up to users of projected geometrical data, such as those within a dodgr_streetnet object, to ensure that either: (i) Data span an sufficiently small area that errors from presuming planar geometry may be ignored; or (ii) Data are re-projected to an equivalent planar geometry prior to calling this routine.

Usage

match_points_to_graph(graph, xy, connected = FALSE)

Arguments

Value

For distances = FALSE (default), a vector index matching the xy coordinates to nearest edges. For bi-directional edges, only one match is returned, and it is up to the user to identify and suitably process matching edge pairs. For 'distances = TRUE', a 'data.frame' of four columns:

• "index" The index of closest edges in "graph", as described above.

• "d_signed" The perpendicular distance from ech point to the nearest edge, with negative distances denoting points to the left of edges, and positive distances denoting points to the right. Distances of zero denote points lying precisely on the line of an edge (potentially including cases where nearest point of bisection lies beyond the actual edge).

• "x" The x-coordinate of the point of intersection.

• "y" The y-coordinate of the point of intersection.

See Also

Other match: add_nodes_to_graph(), match_points_to_verts(), match_pts_to_graph(), match_pts_to_verts()

Examples

graph <- weight_streetnet (hampi, wt_profile = "foot")
# Then generate some random points to match to graph
verts <- dodgr_vertices (graph)
npts <- 10
xy <- data.frame (
    x = min (verts$x) + runif (npts) * diff (range (verts$x)),
    y = min (verts$y) + runif (npts) * diff (range (verts$y))
)
edges <- match_pts_to_graph (graph, xy)
graph [edges, ] # The edges of the graph closest to `xy`

Alias for match_pts_to_verts

Description

The match_pts_to_graph function matches points to the nearest edge based on geometric intersections; this function only matches to the nearest vertex based on point-to-point distances.

Usage

match_points_to_verts(verts, xy, connected = FALSE)

Arguments

Value

A vector index into verts

See Also

Other match: add_nodes_to_graph(), match_points_to_graph(), match_pts_to_graph(), match_pts_to_verts()

Examples

net <- weight_streetnet (hampi, wt_profile = "foot")
verts <- dodgr_vertices (net)
# Then generate some random points to match to graph
npts <- 10
xy <- data.frame (
    x = min (verts$x) + runif (npts) * diff (range (verts$x)),
    y = min (verts$y) + runif (npts) * diff (range (verts$y))
)
pts <- match_pts_to_verts (verts, xy)
pts # an index into verts
pts <- verts$id [pts]
pts # names of those vertices

Description

Match spatial points to the edges of a spatial graph, through finding the edge with the closest perpendicular intersection. NOTE: Intersections are calculated geometrically, and presume planar geometry. It is up to users of projected geometrical data, such as those within a dodgr_streetnet object, to ensure that either: (i) Data span an sufficiently small area that errors from presuming planar geometry may be ignored; or (ii) Data are re-projected to an equivalent planar geometry prior to calling this routine.

Usage

match_pts_to_graph(graph, xy, connected = FALSE, distances = FALSE)

Arguments

Value

For distances = FALSE (default), a vector index matching the xy coordinates to nearest edges. For bi-directional edges, only one match is returned, and it is up to the user to identify and suitably process matching edge pairs. For 'distances = TRUE', a 'data.frame' of four columns:

• "index" The index of closest edges in "graph", as described above.

• "d_signed" The perpendicular distance from ech point to the nearest edge, with negative distances denoting points to the left of edges, and positive distances denoting points to the right. Distances of zero denote points lying precisely on the line of an edge (potentially including cases where nearest point of bisection lies beyond the actual edge).

• "x" The x-coordinate of the point of intersection.

• "y" The y-coordinate of the point of intersection.

See Also

Other match: add_nodes_to_graph(), match_points_to_graph(), match_points_to_verts(), match_pts_to_verts()

Examples

graph <- weight_streetnet (hampi, wt_profile = "foot")
# Then generate some random points to match to graph
verts <- dodgr_vertices (graph)
npts <- 10
xy <- data.frame (
    x = min (verts$x) + runif (npts) * diff (range (verts$x)),
    y = min (verts$y) + runif (npts) * diff (range (verts$y))
)
edges <- match_pts_to_graph (graph, xy)
graph [edges, ] # The edges of the graph closest to `xy`

Description

The match_pts_to_graph function matches points to the nearest edge based on geometric intersections; this function only matches to the nearest vertex based on point-to-point distances.

Usage

match_pts_to_verts(verts, xy, connected = FALSE)

Arguments

Value

A vector index into verts

See Also

Other match: add_nodes_to_graph(), match_points_to_graph(), match_points_to_verts(), match_pts_to_graph()

Examples

net <- weight_streetnet (hampi, wt_profile = "foot")
verts <- dodgr_vertices (net)
# Then generate some random points to match to graph
npts <- 10
xy <- data.frame (
    x = min (verts$x) + runif (npts) * diff (range (verts$x)),
    y = min (verts$y) + runif (npts) * diff (range (verts$y))
)
pts <- match_pts_to_verts (verts, xy)
pts # an index into verts
pts <- verts$id [pts]
pts # names of those vertices

Description

Merge directed edges into equivalent undirected values by aggregating across directions. This function is primarily intended to aid visualisation of directed graphs, particularly visualising the results of the dodgr_flows_aggregate and dodgr_flows_disperse functions, which return columns of aggregated flows directed along each edge of a graph.

Usage

merge_directed_graph(graph, col_names = c("flow"))

Arguments

Value

An equivalent graph in which all directed edges have been reduced to single, undirected edges, and all values of the specified column(s) have been aggregated across directions to undirected values.

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), summary.dodgr_dists_categorical(), write_dodgr_wt_profile()

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

Description

A sample street network for Bristol, U.K., from the Ordnance Survey.

Format

A Simple Features sf data.frame representing motorways in Bristol, UK.

Note

Input data downloaded from https://osdatahub.os.uk/downloads/open, To download the data from that page click on the tick box next to 'OS Open Roads', scroll to the bottom, click 'Continue' and complete the form on the subsequent page. This dataset is open access and can be used under these licensing conditions, and must be cited as follows: Contains OS data © Crown copyright and database right (2017)

See Also

Other data: hampi, weighting_profiles

Examples

## Not run: 
library (sf)
library (dplyr)
# data must be unzipped here
# os_roads <- sf::read_sf("~/data/ST_RoadLink.shp")
# u <- paste0 (
#     "https://opendata.arcgis.com/datasets/",
#     "686603e943f948acaa13fb5d2b0f1275_4.kml"
# )
# lads <- sf::read_sf(u)
# mapview::mapview(lads)
# bristol_pol <- dplyr::filter(lads, grepl("Bristol", lad16nm))
# os_roads <- st_transform(os_roads, st_crs(lads)
# os_roads_bristol <- os_roads[bristol_pol, ] %>%
#   dplyr::filter(class == "Motorway" &
#                 roadNumber != "M32") %>%
#   st_zm(drop = TRUE)
# mapview::mapview(os_roads_bristol)

## End(Not run)
# Converting this 'sf data.frame' to a 'dodgr' network requires manual
# specification of weighting profile:
colnm <- "formOfWay" # name of column used to determine weights
wts <- data.frame (
    name = "custom",
    way = unique (os_roads_bristol [[colnm]]),
    value = c (0.1, 0.2, 0.8, 1)
)
net <- weight_streetnet (
    os_roads_bristol,
    wt_profile = wts,
    type_col = colnm, id_col = "identifier"
)
# 'id_col' tells the function which column to use to attribute IDs of ways

Transform a result from dodgr_dists_categorical to summary statistics

Description

Transform a result from dodgr_dists_categorical to summary statistics

Usage

## S3 method for class 'dodgr_dists_categorical'
summary(object, ...)

Arguments

Value

The summary statistics (invisibly)

See Also

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), write_dodgr_wt_profile()

Description

Weight (or re-weight) an sf-formatted OSM street network for routing along railways.

Usage

weight_railway(
  x,
  type_col = "railway",
  id_col = "osm_id",
  keep_cols = c("maxspeed"),
  excluded = c("abandoned", "disused", "proposed", "razed")
)

Arguments

Value

A data.frame of edges representing the rail network, along with a column of graph component numbers.

Note

Default railway weighting is by distance. Other weighting schemes, such as by maximum speed, can be implemented simply by modifying the d_weighted column returned by this function accordingly.

See Also

Other extraction: dodgr_streetnet(), dodgr_streetnet_sc(), weight_streetnet()

Examples

## Not run: 
# sample railway extraction with the 'osmdata' package
library (osmdata)
dat <- opq ("shinjuku") %>%
    add_osm_feature (key = "railway") %>%
    osmdata_sf (quiet = FALSE)
graph <- weight_railway (dat$osm_lines)

## End(Not run)

Description

Weight (or re-weight) an sf or SC (silicate)-formatted OSM street network according to a named profile, selected from (foot, horse, wheelchair, bicycle, moped, motorcycle, motorcar, goods, hgv, psv), or a cusstomized version dervied from those.

Usage

weight_streetnet(
  x,
  wt_profile = "bicycle",
  wt_profile_file = NULL,
  turn_penalty = FALSE,
  type_col = "highway",
  id_col = "osm_id",
  keep_cols = NULL,
  left_side = FALSE
)

## Default S3 method:
weight_streetnet(
  x,
  wt_profile = "bicycle",
  wt_profile_file = NULL,
  turn_penalty = FALSE,
  type_col = "highway",
  id_col = "osm_id",
  keep_cols = NULL,
  left_side = FALSE
)

## S3 method for class 'sf'
weight_streetnet(
  x,
  wt_profile = "bicycle",
  wt_profile_file = NULL,
  turn_penalty = FALSE,
  type_col = "highway",
  id_col = "osm_id",
  keep_cols = NULL,
  left_side = FALSE
)

## S3 method for class 'sc'
weight_streetnet(
  x,
  wt_profile = "bicycle",
  wt_profile_file = NULL,
  turn_penalty = FALSE,
  type_col = "highway",
  id_col = "osm_id",
  keep_cols = NULL,
  left_side = FALSE
)

## S3 method for class 'SC'
weight_streetnet(
  x,
  wt_profile = "bicycle",
  wt_profile_file = NULL,
  turn_penalty = FALSE,
  type_col = "highway",
  id_col = "osm_id",
  keep_cols = NULL,
  left_side = FALSE
)

Arguments

Value

A data.frame of edges representing the street network, with distances in metres and times in seconds, along with a column of graph component numbers. Times for sf-formatted street networks are only approximate, and do not take into account traffic lights, turn angles, or elevation changes. Times for sc-formatted street networks take into account all of these factors, with elevation changes automatically taken into account for networks generated with the osmdata function osm_elevation().

Note

Names for the wt_profile parameter are taken from weighting_profiles, which is a list including a data.frame also called weighting_profiles of weights for different modes of transport. Values for wt_profile are taken from current modes included there, which are "bicycle", "foot", "goods", "hgv", "horse", "moped", "motorcar", "motorcycle", "psv", and "wheelchair". Railway routing can be implemented with the separate function weight_railway. Alternatively, the entire weighting_profile structures can be written to a local .json-formatted file with write_dodgr_wt_profile, the values edited as desired, and the name of this file passed as the wt_profile_file parameter.

Realistic routing include factors such as access restrictions, turn penalties, and effects of incline, can only be implemented when the objects passed to weight_streetnet are of sc ("silicate") format, generated with dodgr_streetnet_sc. Restrictions applied to ways (in Open Streetmap Terminology) may be controlled by ensuring specific columns are retained in the dodgr network with the keep_cols argument. For example, restrictions on access are generally specified by specifying a value for the key of "access". Include "access" in keep_cols will ensure these values are retained in the dodgr version, from which ways with specified values can easily be removed or modified, as demonstrated in the examples.

The additional Open Street Map (OSM) keys which can be used to specify restrictions are which are automatically extracted with dodgr_streetnet_sc, and so may be added to the keep_cols argument, include:

• "access"

• "bicycle"

• "foot"

• "highway"

• "motorcar"

• "motor_vehicle"

• "restriction"

• "toll"

• "vehicle"

Restrictions and time-penalties on turns can be implemented from such objects by setting turn_penalty = TRUE. Setting turn_penalty = TRUE will honour turn restrictions specified in Open Street Map (unless the "penalties" table of weighting_profiles has restrictions = FALSE for a specified wt_profile). Resultant graphs are fundamentally different from the default for distance-based routing. These graphs may be used directly in the dodgr_dists function. Use in any other functions requires additional information obtained in a file in the temporary directory of the current R session with a name starting with "dodgr_junctions_", and including the value of attr(graph, "hash"). If graphs with turn penalties are to be used in subsequent R sessions, this "dodgr_junctions_" file will need to be moved to a more permanent storage location, and then replaced in the temporary directory of any subsequent R sessions.

The resultant graph includes only those edges for which the given weighting profile specifies finite edge weights. Any edges of types not present in a given weighting profile are automatically removed from the weighted streetnet.

If the resultant graph is to be contracted via dodgr_contract_graph, and if the columns of the graph have been, or will be, modified, then automatic caching must be switched off with dodgr_cache_off. If not, the dodgr_contract_graph function will return the automatically cached version, which is the contracted version of the full graph prior to any modification of columns.

See Also

write_dodgr_wt_profile, dodgr_times

Other extraction: dodgr_streetnet(), dodgr_streetnet_sc(), weight_railway()

Other extraction: dodgr_streetnet(), dodgr_streetnet_sc(), weight_railway()

Other extraction: dodgr_streetnet(), dodgr_streetnet_sc(), weight_railway()

Other extraction: dodgr_streetnet(), dodgr_streetnet_sc(), weight_railway()

Other extraction: dodgr_streetnet(), dodgr_streetnet_sc(), weight_railway()

Examples

# hampi is included with package as an 'osmdata' sf-formatted street network
net <- weight_streetnet (hampi)
class (net) # data.frame
dim (net) # 6096  11; 6096 streets
# os_roads_bristol is also included as an sf data.frame, but in a different
# format requiring identification of columns and specification of custom
# weighting scheme.
colnm <- "formOfWay"
wts <- data.frame (
    name = "custom",
    way = unique (os_roads_bristol [[colnm]]),
    value = c (0.1, 0.2, 0.8, 1)
)
net <- weight_streetnet (
    os_roads_bristol,
    wt_profile = wts,
    type_col = colnm, id_col = "identifier"
)
dim (net) # 406 11; 406 streets

# An example for a generic (non-OSM) highway, represented as the
# `routes_fast` object of the \pkg{stplanr} package, which is a
# SpatialLinesDataFrame.
## Not run: 
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, crs = 4326)
# We need to specify both a `type` and `id` column for the
# \link{weight_streetnet} function.
r$type <- 1
r$id <- seq (nrow (r))
graph <- weight_streetnet (
    r,
    type_col = "type",
    id_col = "id",
    wt_profile = 1
)

## End(Not run)

Description

Collection of weighting profiles used to adjust the routing process to different means of transport. Modified from data taken from the Routino project, with additional tables for average speeds, dependence of speed on type of surface, and waiting times in seconds at traffic lights. The latter table (called "penalties") includes waiting times at traffic lights (in seconds), additional time penalties for turning across oncoming traffic ("turn"), and a binary flag indicating whether turn restrictions should be obeyed or not.

Format

List of data.frame objects with profile names, means of transport and weights.

References

https://www.routino.org/xml/routino-profiles.xml

See Also

Other data: hampi, os_roads_bristol

Description

Write the dodgr street network weighting profiles to a local .json-formatted file for manual editing and subsequent re-reading.

Usage

write_dodgr_wt_profile(file = NULL)

Arguments

Value

TRUE if writing successful.

See Also

weight_streetnet

Other misc: compare_heaps(), dodgr_flowmap(), dodgr_full_cycles(), dodgr_fundamental_cycles(), dodgr_insert_vertex(), dodgr_sample(), dodgr_sflines_to_poly(), dodgr_vertices(), merge_directed_graph(), summary.dodgr_dists_categorical()