Package 'stplanr'

Description

The stplanr package provides functions to access and analyse data for transportation research, including origin-destination analysis, route allocation and modelling travel patterns.

Author(s)

Robin Lovelace [email protected]

See Also

https://github.com/ropensci/stplanr

Description

This function was designed to find lines that are close to parallel and perpendicular to some pre-defined route. It can return results that are absolute (contain information on the direction of turn, i.e. + or - values for clockwise/anticlockwise), bidirectional (which mean values greater than +/- 90 are impossible).

Usage

angle_diff(l, angle, bidirectional = FALSE, absolute = TRUE)

Arguments

Details

Building on the convention used in in the bearing() function from the geosphere package and in many applications, North is definied as 0, East as 90 and West as -90.

See Also

Other lines: geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

lib_versions <- sf::sf_extSoftVersion()
lib_versions
# fails on some systems (with early versions of PROJ)
if (lib_versions[3] >= "6.3.1") {
  # Find all routes going North-South
  lines_sf <- od2line(od_data_sample, zones = zones_sf)
  angle_diff(lines_sf[2, ], angle = 0)
  angle_diff(lines_sf[2:3, ], angle = 0)
}

Description

Takes a bounding box as an input and outputs a bounding box of a different size, centred at the same point.

Usage

bbox_scale(bb, scale_factor)

Arguments

See Also

Other geo: bind_sf(), geo_bb(), geo_bb_matrix(), geo_buffer(), geo_length(), geo_projected(), geo_select_aeq(), quadrant()

Examples

bb <- matrix(c(-1.55, 53.80, -1.50, 53.83), nrow = 2)
bb1 <- bbox_scale(bb, scale_factor = 1.05)
bb2 <- bbox_scale(bb, scale_factor = c(2, 1.05))
bb3 <- bbox_scale(bb, 0.1)
plot(x = bb2[1, ], y = bb2[2, ])
points(bb1[1, ], bb1[2, ])
points(bb3[1, ], bb3[2, ])
points(bb[1, ], bb[2, ], col = "red")

Description

Rapid row-binding of sf objects

Usage

bind_sf(x)

Arguments

Value

An sf data frame

See Also

Other geo: bbox_scale(), geo_bb(), geo_bb_matrix(), geo_buffer(), geo_length(), geo_projected(), geo_select_aeq(), quadrant()

Description

These points represent population-weighted centroids of Medium Super Output Area (MSOA) zones within a 1 mile radius of of my home when I was writing this package.

Format

A spatial dataset with 8 rows and 5 columns

Details

• geo_code the official code of the zone

• MSOA11NM name zone name

• percent_fem the percent female

• avslope average gradient of the zone

Cents was generated from the data repository pct-data: https://github.com/npct/pct-data. This data was accessed from within the pct repo: https://github.com/npct/pct, using the following code:

See Also

Other data: destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

cents_sf

Description

This dataset represents trip destinations on a different geographic level than the origins stored in the object cents_sf.

Format

A spatial dataset with 87 features

See Also

Other data: cents_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

destinations_sf

Description

This dataset represents commuter flows (work travel) between origin and destination zones. The data is from the UK and is available as open data: https://wicid.ukdataservice.ac.uk/.

Format

A data frame with 49 rows and 15 columns

Details

The variables are as follows:

• Area.of.residence. id of origin zone

• Area.of.workplace id of destination zone

• All. Travel to work flows by all modes

• ⁠[,4:15]⁠. Flows for different modes

• id. unique id of flow

Although these variable names are unique to UK data, the data structure is generalisable and typical of flow data from any source. The key variables are the origin and destination ids, which link to the georeferenced spatial objects.

See Also

Other data: cents_sf, destinations_sf, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Other data: cents_sf, destinations_sf, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Description

Data frame of invented commuter flows with destinations in a different layer than the origins

Usage

data(flow_dests)

Format

A data frame with 49 rows and 15 columns

See Also

Other data: cents_sf, destinations_sf, flow, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

## Not run: 
# This is how the dataset was constructed
flow_dests <- flow
flow_dests$Area.of.workplace <- sample(x = destinations$WZ11CD, size = nrow(flow))
flow_dests <- dplyr::rename(flow_dests, WZ11CD = Area.of.workplace)
devtools::use_data(flow_dests)

## End(Not run)

Description

Flow data after conversion to a spatial format..

Format

A spatial lines dataset with 42 rows and 15 columns

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Description

Takes a geographic object or bounding box as an input and outputs a bounding box, represented as a bounding box, corner points or rectangular polygon.

Usage

geo_bb(
  shp,
  scale_factor = 1,
  distance = 0,
  output = c("polygon", "points", "bb")
)

Arguments

See Also

bb_scale

Other geo: bbox_scale(), bind_sf(), geo_bb_matrix(), geo_buffer(), geo_length(), geo_projected(), geo_select_aeq(), quadrant()

Examples

shp <- routes_fast_sf
shp_bb <- geo_bb(shp, distance = 100)
plot(shp_bb, col = "red", reset = FALSE)
plot(geo_bb(routes_fast_sf, scale_factor = 0.8), col = "green", add = TRUE)
plot(routes_fast_sf$geometry, add = TRUE)
geo_bb(shp, output = "point")

Description

Converts a range of spatial data formats into a matrix representing the bounding box

Usage

geo_bb_matrix(shp)

Arguments

See Also

Other geo: bbox_scale(), bind_sf(), geo_bb(), geo_buffer(), geo_length(), geo_projected(), geo_select_aeq(), quadrant()

Examples

geo_bb_matrix(routes_fast_sf)
geo_bb_matrix(cents_sf[1, ])
geo_bb_matrix(c(-2, 54))
geo_bb_matrix(sf::st_coordinates(cents_sf))

Description

This function solves the problem that buffers will not be circular when used on non-projected data.

Usage

geo_buffer(shp, dist = NULL, width = NULL, ...)

Arguments

Details

Requires recent version of PROJ (>= 6.3.0). Buffers on sf objects with geographic (lon/lat) coordinates can also be done with the s2 package.

See Also

Other geo: bbox_scale(), bind_sf(), geo_bb(), geo_bb_matrix(), geo_length(), geo_projected(), geo_select_aeq(), quadrant()

Examples

lib_versions <- sf::sf_extSoftVersion()
lib_versions
if (lib_versions[3] >= "6.3.1") {
  buff_sf <- geo_buffer(routes_fast_sf, dist = 50)
  plot(buff_sf$geometry)
  geo_buffer(routes_fast_sf$geometry, dist = 50)
}

Description

Generate a lat/long pair from data using Google's geolocation API.

Usage

geo_code(
  address,
  service = "nominatim",
  base_url = "https://maps.google.com/maps/api/geocode/json",
  return_all = FALSE,
  pat = NULL
)

Arguments

Examples

## Not run: 
geo_code(address = "Hereford")
geo_code("LS7 3HB")
geo_code("hereford", return_all = TRUE)
# needs api key in .Renviron
geo_code("hereford", service = "google", pat = Sys.getenv("GOOGLE"), return_all = TRUE)

## End(Not run)

Description

Takes a line (represented in sf or sp classes) and returns a numeric value representing distance in meters.

Usage

geo_length(shp)

Arguments

See Also

Other geo: bbox_scale(), bind_sf(), geo_bb(), geo_bb_matrix(), geo_buffer(), geo_projected(), geo_select_aeq(), quadrant()

Examples

lib_versions <- sf::sf_extSoftVersion()
lib_versions
if (lib_versions[3] >= "6.3.1") {
  geo_length(routes_fast_sf)
}

Description

This function performs operations on projected data.

Usage

geo_projected(shp, fun, crs, silent, ...)

Arguments

See Also

Other geo: bbox_scale(), bind_sf(), geo_bb(), geo_bb_matrix(), geo_buffer(), geo_length(), geo_select_aeq(), quadrant()

Examples

lib_versions <- sf::sf_extSoftVersion()
lib_versions
# fails on some systems (with early versions of PROJ)
if (lib_versions[3] >= "6.3.1") {
  shp <- routes_fast_sf[2:4, ]
  geo_projected(shp, sf::st_buffer, dist = 100)
}

Description

This function takes a spatial object with a geographic (WGS84) CRS and returns a custom projected CRS focussed on the centroid of the object. This function is especially useful for using units of metres in all directions for data collected anywhere in the world.

Usage

geo_select_aeq(shp)

Arguments

Details

The function is based on this stackexchange answer: https://gis.stackexchange.com/questions/121489

See Also

Other geo: bbox_scale(), bind_sf(), geo_bb(), geo_bb_matrix(), geo_buffer(), geo_length(), geo_projected(), quadrant()

Examples

shp <- zones_sf
geo_select_aeq(shp)

Description

Takes lines and removes the start and end point, to a distance determined by the user.

Usage

geo_toptail(l, toptail_dist, ...)

Arguments

Details

Note: see the function toptailgs() in stplanr v0.8.5 for an implementation that uses the geosphere package.

See Also

Other lines: angle_diff(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

lib_versions <- sf::sf_extSoftVersion()
lib_versions
# dont test due to issues with sp classes on some set-ups
if (lib_versions[3] >= "6.3.1") {
  l <- routes_fast_sf[2:4, ]
  l_top_tail <- geo_toptail(l, 300)
  l_top_tail
  plot(sf::st_geometry(l_top_tail))
  plot(sf::st_geometry(geo_toptail(l, 600)), lwd = 9, add = TRUE)
}

Description

Divides SpatialLinesDataFrame objects into separate Lines. Each new Lines object is the aggregate of a single number of aggregated lines.

Usage

gsection(sl, buff_dist = 0)

Arguments

See Also

Other rnet: islines(), overline(), rnet_breakup_vertices(), rnet_group()

Examples

lib_versions <- sf::sf_extSoftVersion()
lib_versions
# fails on some systems (with early versions of PROJ)
if (lib_versions[3] >= "6.3.1") {
  sl <- routes_fast_sf[2:4, ]
  rsec <- gsection(sl)
  length(rsec) # sections
  plot(rsec, col = seq(length(rsec)))
  rsec <- gsection(sl, buff_dist = 50)
  length(rsec) # 4 features: issue
  plot(rsec, col = seq(length(rsec)))
}

Description

OD matrices often contain 'intrazonal' flows, where the origin is the same point as the destination. This function can help identify such intrazonal OD pairs, using 2 criteria: the total number of vertices (2 or fewer) and whether the origin and destination are the same.

Usage

is_linepoint(l)

Arguments

Details

Returns a boolean vector. TRUE means that the associated line is in fact a point (has no distance). This can be useful for removing data that will not be plotted.

See Also

Other lines: angle_diff(), geo_toptail(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

islp <- is_linepoint(flowlines_sf)
nrow(flowlines_sf)
sum(islp)
# Remove invisible 'linepoints'
nrow(flowlines_sf[!islp, ])

Description

This is a function required in overline(). It identifies whether sets of lines overlap (beyond shared points) or not.

Usage

islines(g1, g2)

Arguments

See Also

Other rnet: gsection(), overline(), rnet_breakup_vertices(), rnet_group()

Examples

## Not run: 
# sf implementation
islines(routes_fast_sf[2, ], routes_fast_sf[3, ])
islines(routes_fast_sf[2, ], routes_fast_sf[22, ])

## End(Not run)

Description

This function returns the bearing (in degrees relative to north) of lines.

Usage

line_bearing(l, bidirectional = FALSE)

Arguments

Details

Returns a boolean vector. TRUE means that the associated line is in fact a point (has no distance). This can be useful for removing data that will not be plotted.

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

l <- flowlines_sf[1:5, ]
bearings_sf_1_9 <- line_bearing(l)
bearings_sf_1_9 # lines of 0 length have NaN bearing
b <- line_bearing(l, bidirectional = TRUE)
r <- routes_fast_sf[1:5, ]
b2 <- line_bearing(r, bidirectional = TRUE)
plot(b, b2)

Description

This function breaks up a LINESTRING geometries into smaller pieces.

Usage

line_breakup(l, z)

Arguments

Value

An sf object with LINESTRING geometry created after breaking up the input object.

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

library(sf)
z <- zones_sf$geometry
l <- routes_fast_sf$geometry[2]
l_split <- line_breakup(l, z)
l
l_split
sf::st_length(l)
sum(sf::st_length(l_split))
plot(z)
plot(l, add = TRUE, lwd = 9, col = "grey")
plot(l_split, add = TRUE, col = 1:length(l_split))

Description

Without losing vertices

Usage

line_cast(x)

Arguments

Description

Find the mid-point of lines

Usage

line_midpoint(l, tolerance = NULL)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

l <- routes_fast_sf[2:5, ]
plot(l$geometry, col = 2:5)
midpoints <- line_midpoint(l)
plot(midpoints, add = TRUE)
# compare with sf::st_point_on_surface:
midpoints2 <- sf::st_point_on_surface(l)
plot(midpoints2, add = TRUE, col = "red")

Description

This function keeps the attributes. Note: results differ when use_rsgeo is TRUE: the {rsgeo} implementation will be faster. Results may not always keep returned linestrings below the segment_length value. The {rsgeo} implementation does not always return the number of segments requested due to an upstream issue in the geo Rust crate.

Usage

line_segment(
  l,
  segment_length = NA,
  n_segments = NA,
  use_rsgeo = NULL,
  debug_mode = FALSE
)

Arguments

Details

Note: we recommend running these functions on projected data.

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

library(sf)
l <- routes_fast_sf[2:4, "ID"]
l_seg_multi <- line_segment(l, segment_length = 1000, use_rsgeo = FALSE)
l_seg_n <- line_segment(l, n_segments = 2)
l_seg_n <- line_segment(l, n_segments = c(1:3))
# Number of subsegments
table(l_seg_multi$ID)
plot(l_seg_multi["ID"])
plot(l_seg_multi$geometry, col = seq_along(l_seg_multi), lwd = 5)
round(st_length(l_seg_multi))
# rsgeo implementation (default if available):
if (rlang::is_installed("rsgeo")) {
  rsmulti = line_segment(l, segment_length = 1000, use_rsgeo = TRUE)
  plot(rsmulti["ID"])
}
# Check they have the same total length, to nearest mm:
# round(sum(st_length(l_seg_multi)), 3) == round(sum(st_length(rsmulti)), 3)
# With n_segments for 1 line (set use_rsgeo to TRUE to use rsgeo):
l_seg_multi_n <- line_segment(l[1, ], n_segments = 3, use_rsgeo = FALSE)
l_seg_multi_n <- line_segment(l$geometry[1], n_segments = 3, use_rsgeo = FALSE)
# With n_segments for all 3 lines:
l_seg_multi_n <- line_segment(l, n_segments = 2)
nrow(l_seg_multi_n) == nrow(l) * 2

Description

Segment a single line, using lwgeom or rsgeo

Usage

line_segment1(l, n_segments = NA, segment_length = NA)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

l <- routes_fast_sf[2, ]
l_seg2 <- line_segment1(l = l, n_segments = 2)
# Test with rsgeo (must be installed):
# l_seg2_rsgeo = line_segment1(l = l, n_segments = 2)
# waldo::compare(l_seg2, l_seg2_rsgeo)
l_seg3 <- line_segment1(l = l, n_segments = 3)
l_seg_100 <- line_segment1(l = l, segment_length = 100)
l_seg_1000 <- line_segment1(l = l, segment_length = 1000)
plot(sf::st_geometry(l_seg2), col = 1:2, lwd = 5)
plot(sf::st_geometry(l_seg3), col = 1:3, lwd = 5)
plot(sf::st_geometry(l_seg_100), col = seq(nrow(l_seg_100)), lwd = 5)
plot(sf::st_geometry(l_seg_1000), col = seq(nrow(l_seg_1000)), lwd = 5)

Description

Takes an origin (A) and destination (B), represented by the linestring l, and generates 3 extra geometries based on points p:

Usage

line_via(l, p)

Arguments

Details

1. From A to P1 (P1 being the nearest point to A)

2. From P1 to P2 (P2 being the nearest point to B)

3. From P2 to B

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

library(sf)
l <- flowlines_sf[2:4, ]
p <- destinations_sf
lv <- line_via(l, p)
lv
# library(mapview)
# mapview(lv) +
#    mapview(lv$leg_orig, col = "red")
plot(lv[3], lwd = 9, reset = FALSE)
plot(lv$leg_orig, col = "red", lwd = 5, add = TRUE)
plot(lv$leg_via, col = "black", add = TRUE)
plot(lv$leg_dest, col = "green", lwd = 5, add = TRUE)

Description

This function returns a data frame with fx and fy and tx and ty variables representing the beginning and end points of spatial line features respectively.

Usage

line2df(l)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

line2df(routes_fast_sf[5:6, ]) # beginning and end of routes

Description

The number of points will be double the number of lines with line2points. A closely related function, line2pointsn returns all the points that were line vertices. The points corresponding with a given line, i, will be (2*i):((2*i)+1). The last function, line2vertices, returns all the points that are vertices but not nodes. If the input l object is composed by only 1 LINESTRING with 2 POINTS, then it returns an empty sf object.

Usage

line2points(l, ids = rep(1:nrow(l)))

line2pointsn(l)

line2vertices(l)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

l <- routes_fast_sf[2, ]
lpoints <- line2points(l)
plot(l$geometry)
plot(lpoints, add = TRUE)
# test all vertices:
plot(l$geometry)
lpoints2 <- line2pointsn(l)
plot(lpoints2$geometry, add = TRUE)

# extract only internal vertices
l_internal_vertices <- line2vertices(l)
plot(sf::st_geometry(l), reset = FALSE)
plot(l_internal_vertices, add = TRUE)
# The boundary points are missing

Description

Convert 2 matrices to lines

Usage

mats2line(mat1, mat2, crs = NA)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), n_segments(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

m1 <- matrix(c(1, 2, 1, 2), ncol = 2)
m2 <- matrix(c(9, 9, 9, 1), ncol = 2)
l <- mats2line(m1, m2)
class(l)
l
lsf <- sf::st_sf(l, crs = 4326)
class(lsf)
plot(lsf)
# mapview::mapview(lsf)

Description

Vectorised function to calculate number of segments given a max segment length

Usage

n_segments(line_length, max_segment_length)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_vertices(), onewaygeo(), points2line(), toptail_buff()

Examples

n_segments(50, 10)
n_segments(50.1, 10)
n_segments(1, 10)
n_segments(1:9, 2)

Description

Returns a vector of the same length as the number of sf objects.

Usage

n_vertices(l)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), onewaygeo(), points2line(), toptail_buff()

Examples

l <- routes_fast_sf
n_vertices(l)
n_vertices(zones_sf)

Description

This function takes a data frame of OD data and returns a data frame reporting summary statistics for each unique zone of origin.

Usage

od_aggregate_from(flow, attrib = NULL, FUN = sum, ..., col = 1)

Arguments

Details

It has some default settings: the default summary statistic is sum() and the first column in the OD data is assumed to represent the zone of origin. By default, if attrib is not set, it summarises all numeric columns.

See Also

Other od: od2line(), od2odf(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

od_aggregate_from(flow)

Description

This function takes a data frame of OD data and returns a data frame reporting summary statistics for each unique zone of destination.

Usage

od_aggregate_to(flow, attrib = NULL, FUN = sum, ..., col = 2)

Arguments

Details

It has some default settings: it assumes the destination ID column is the 2nd and the default summary statistic is sum(). By default, if attrib is not set, it summarises all numeric columns.

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

od_aggregate_to(flow)

Description

This function takes a wide range of input data types (spatial lines, points or text strings) and returns a matrix of coordinates representing origin (fx, fy) and destination (tx, ty) points.

Usage

od_coords(from = NULL, to = NULL, l = NULL)

Arguments

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

od_coords(from = c(0, 52), to = c(1, 53)) # lon/lat coordinates
od_coords(cents_sf[1:3, ], cents_sf[2:4, ]) # sf points
# od_coords("Hereford", "Leeds") # geocode locations
od_coords(flowlines_sf[1:3, ])

Description

Convert origin-destination coordinates into desire lines

Usage

od_coords2line(odc, crs = 4326, remove_duplicates = TRUE)

Arguments

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

odf <- od_coords(l = flowlines_sf)
odlines <- od_coords2line(odf)
odlines <- od_coords2line(odf, crs = 4326)
plot(odlines)
x_coords <- 1:3
n <- 50
d <- data.frame(lapply(1:4, function(x) sample(x_coords, n, replace = TRUE)))
names(d) <- c("fx", "fy", "tx", "ty")
l <- od_coords2line(d)
plot(l)
nrow(l)
l_with_duplicates <- od_coords2line(d, remove_duplicates = FALSE)
plot(l_with_duplicates)
nrow(l_with_duplicates)

Description

Derived from od_data_sample showing movement between points represented in cents_sf

Format

A data frame (tibble) object

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

od_data_lines

Description

See data-raw/generate-data.Rmd for details on how this was created. The dataset shows routes between origins and destinations represented in od_data_lines

Format

A data frame (tibble) object

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

od_data_routes

Description

See data-raw/generate-data.Rmd for details on how this was created.

Format

A data frame (tibble) object

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, osm_net_example, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

od_data_sample

Description

Combine two ID values to create a single ID number

Usage

od_id_szudzik(x, y, ordermatters = FALSE)

od_id_max_min(x, y)

od_id_character(x, y)

Arguments

Details

In OD data it is common to have many 'oneway' flows from "A to B" and "B to A". It can be useful to group these an have a single ID that represents pairs of IDs with or without directionality, so they contain 'twoway' or bi-directional values.

⁠od_id*⁠ functions take two vectors of equal length and return a vector of IDs, which are unique for each combination but the same for twoway flows.

• the Szudzik pairing function, on two vectors of equal length. It returns a vector of ID numbers.

This function superseeds od_id_order as it is faster on large datasets

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

(d <- od_data_sample[2:9, 1:2])
(id <- od_id_character(d[[1]], d[[2]]))
duplicated(id)
od_id_szudzik(d[[1]], d[[2]])
od_id_max_min(d[[1]], d[[2]])

Description

Generate ordered ids of OD pairs so lowest is always first This function is slow on large datasets, see szudzik_pairing for faster alternative

Usage

od_id_order(x, id1 = names(x)[1], id2 = names(x)[2])

Arguments

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

x <- data.frame(id1 = c(1, 1, 2, 2, 3), id2 = c(1, 2, 3, 1, 4))
od_id_order(x) # 4th line switches id1 and id2 so stplanr.key is in order

Description

For example, sum total travel in both directions.

Usage

od_oneway(
  x,
  attrib = names(x[-c(1:2)])[vapply(x[-c(1:2)], is.numeric, TRUE)],
  id1 = names(x)[1],
  id2 = names(x)[2],
  stplanr.key = NULL
)

Arguments

Details

Flow data often contains movement in two directions: from point A to point B and then from B to A. This can be problematic for transport planning, because the magnitude of flow along a route can be masked by flows the other direction. If only the largest flow in either direction is captured in an analysis, for example, the true extent of travel will by heavily under-estimated for OD pairs which have similar amounts of travel in both directions. Flows in both direction are often represented by overlapping lines with identical geometries which can be confusing for users and are difficult to plot.

Value

oneway outputs a data frame (or sf data frame) with rows containing results for the user-selected attribute values that have been aggregated.

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

(od_min <- od_data_sample[c(1, 2, 9), 1:6])
(od_oneway <- od_oneway(od_min))
# (od_oneway_old = onewayid(od_min, attrib = 3:6)) # old implementation
nrow(od_oneway) < nrow(od_min) # result has fewer rows
sum(od_min$all) == sum(od_oneway$all) # but the same total flow
od_oneway(od_min, attrib = "all")
attrib <- which(vapply(flow, is.numeric, TRUE))
flow_oneway <- od_oneway(flow, attrib = attrib)
colSums(flow_oneway[attrib]) == colSums(flow[attrib]) # test if the colSums are equal
# Demonstrate the results from oneway and onewaygeo are identical
flow_oneway_sf <- od_oneway(flowlines_sf)
plot(flow_oneway_sf$geometry, lwd = flow_oneway_sf$All / mean(flow_oneway_sf$All))

Description

This function takes a data frame representing travel between origins (with origin codes in name_orig, typically the 1st column) and destinations (with destination codes in name_dest, typically the second column) and returns a matrix with cell values (from attrib, the third column by default) representing travel between origins and destinations.

Usage

od_to_odmatrix(flow, attrib = 3, name_orig = 1, name_dest = 2)

Arguments

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), odmatrix_to_od(), points2flow(), points2odf()

Examples

od_to_odmatrix(flow)
od_to_odmatrix(flow[1:9, ])
od_to_odmatrix(flow[1:9, ], attrib = "Bicycle")

Description

Origin-destination ('OD') flow data is often provided in the form of 1 line per flow with zone codes of origin and destination centroids. This can be tricky to plot and link-up with geographical data. This function makes the task easier.

Usage

od2line(
  flow,
  zones,
  destinations = NULL,
  zone_code = names(zones)[1],
  origin_code = names(flow)[1],
  dest_code = names(flow)[2],
  zone_code_d = NA,
  silent = FALSE
)

Arguments

Details

Origin-destination (OD) data is often provided in the form of 1 line per OD pair, with zone codes of the trip origin in the first column and the zone codes of the destination in the second column (see the vignette("stplanr-od")) for details. od2line() creates a spatial (linestring) object representing movement from the origin to the destination for each OD pair. It takes data frame containing origin and destination cones (flow) that match the first column in a a spatial (polygon or point) object (zones).

See Also

Other od: od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

od_data <- stplanr::flow[1:20, ]
l <- od2line(flow = od_data, zones = cents_sf)
plot(sf::st_geometry(cents_sf))
plot(l, lwd = l$All / mean(l$All), add = TRUE)

Description

Extract coordinates from OD data

Usage

od2odf(flow, zones)

Arguments

Details

Origin-destination (OD) data is often provided in the form of 1 line per OD pair, with zone codes of the trip origin in the first column and the zone codes of the destination in the second column (see the vignette("stplanr-od")) for details. od2odf() creates an 'origin-destination data frame', with columns containing origin and destination codes (flow) that match the first column in a a spatial (polygon or point sf) object (zones).

The function returns a data frame with coordinates for the origin and destination.

See Also

Other od: od2line(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow(), points2odf()

Examples

od2odf(flow[1:2, ], zones_sf)

Description

This function takes a matrix representing travel between origins (with origin codes in the rownames of the matrix) and destinations (with destination codes in the colnames of the matrix) and returns a data frame representing origin-destination pairs.

Usage

odmatrix_to_od(odmatrix)

Arguments

Details

The function returns a data frame with rows ordered by origin and then destination zone code values and with names orig, dest and flow.

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), points2flow(), points2odf()

Examples

odmatrix <- od_to_odmatrix(flow)
odmatrix_to_od(odmatrix)
flow[1:9, 1:3]
odmatrix_to_od(od_to_odmatrix(flow[1:9, 1:3]))

Description

Flow data often contains movement in two directions: from point A to point B and then from B to A. This can be problematic for transport planning, because the magnitude of flow along a route can be masked by flows the other direction. If only the largest flow in either direction is captured in an analysis, for example, the true extent of travel will by heavily under-estimated for OD pairs which have similar amounts of travel in both directions.

Usage

onewaygeo(x, attrib)

Arguments

Details

This function aggregates directional flows into non-directional flows, potentially halving the number of lines objects and reducing the number of overlapping lines to zero.

Value

onewaygeo outputs a SpatialLinesDataFrame with single lines and user-selected attribute values that have been aggregated. Only lines with a distance (i.e. not intra-zone flows) are included

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), points2line(), toptail_buff()

Description

Example of OpenStreetMap road network

Format

An sf object

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, read_table_builder(), route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Examples

osm_net_example

Description

This function takes a series of overlapping lines and converts them into a single route network.

This function is intended as a replacement for overline() and is significantly faster especially on large datasets. However, it also uses more memory.

Usage

overline(
  sl,
  attrib,
  ncores = 1,
  simplify = TRUE,
  regionalise = 1e+09,
  quiet = ifelse(nrow(sl) < 1000, TRUE, FALSE),
  fun = sum
)

overline2(
  sl,
  attrib,
  ncores = 1,
  simplify = TRUE,
  regionalise = 1e+07,
  quiet = ifelse(nrow(sl) < 1000, TRUE, FALSE),
  fun = sum
)

Arguments

Details

The function can be used to estimate the amount of transport 'flow' at the route segment level based on input datasets from routing services, for example linestring geometries created with the route() function.

The overline() function breaks each line into many straight segments and then looks for duplicated segments. Attributes are summed for all duplicated segments, and if simplify is TRUE the segments with identical attributes are recombined into linestrings.

The following arguments only apply to the sf implementation of overline():

• ncores, the number of cores to use in parallel processing

• simplify, should the final segments be converted back into longer lines? The default setting is TRUE. simplify = FALSE results in straight line segments consisting of only 2 vertices (the start and end point), resulting in a data frame with many more rows than the simplified results (see examples).

• regionalise the threshold number of rows above which regionalisation is used (see details).

For sf objects Regionalisation breaks the dataset into a 10 x 10 grid and then performed the simplification across each grid. This significantly reduces computation time for large datasets, but slightly increases the final file size. For smaller datasets it increases computation time slightly but reduces memory usage and so may also be useful.

A known limitation of this method is that overlapping segments of different lengths are not aggregated. This can occur when lines stop halfway down a road. Typically these errors are small, but some artefacts may remain within the resulting data.

For very large datasets nrow(x) > 1000000, memory usage can be significant. In these cases is is possible to overline subsets of the dataset, rbind the results together, and then overline again, to produce a final result.

Multicore support is only enabled for the regionalised simplification stage as it does not help with other stages.

Value

An sf object representing a route network

Author(s)

Barry Rowlingson

Malcolm Morgan

References

Morgan M and Lovelace R (2020). Travel flow aggregation: Nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level. Environment and Planning B: Urban Analytics and City Science. July 2020. doi:10.1177/2399808320942779.

Rowlingson, B (2015). Overlaying lines and aggregating their values for overlapping segments. Reproducible question from https://gis.stackexchange.com. See https://gis.stackexchange.com/questions/139681/.

See Also

Other rnet: gsection(), islines(), rnet_breakup_vertices(), rnet_group()

Other rnet: gsection(), islines(), rnet_breakup_vertices(), rnet_group()

Examples

sl <- routes_fast_sf[2:4, ]
sl$All <- flowlines_sf$All[2:4]
rnet <- overline(sl = sl, attrib = "All")
nrow(sl)
nrow(rnet)
plot(rnet)
rnet_mean <- overline(sl, c("All", "av_incline"), fun = list(mean = mean, sum = sum))
plot(rnet_mean, lwd = rnet_mean$All_sum / mean(rnet_mean$All_sum))
rnet_sf_raw <- overline(sl, attrib = "length", simplify = FALSE)
nrow(rnet_sf_raw)
summary(n_vertices(rnet_sf_raw))
plot(rnet_sf_raw)
rnet_sf_raw$n <- 1:nrow(rnet_sf_raw)
plot(rnet_sf_raw[10:25, ])

Description

This function takes overlapping LINESTRINGs stored in an sf object and returns a route network composed of non-overlapping geometries and aggregated values.

Usage

overline_intersection(sl, attrib, fun = sum)

Arguments

Examples

routes_fast_sf$value <- 1
sl <- routes_fast_sf[4:6, ]
attrib <- c("value", "length")
rnet <- overline_intersection(sl = sl, attrib)
plot(rnet, lwd = rnet$value)
# A larger example
sl <- routes_fast_sf[4:7, ]
rnet <- overline_intersection(sl = sl, attrib = c("value", "length"))
plot(rnet, lwd = rnet$value)
rnet_sf <- overline(routes_fast_sf[4:7, ], attrib = c("value", "length"))
plot(rnet_sf, lwd = rnet_sf$value)

# An even larger example (not shown, takes time to run)
# rnet = overline_intersection(routes_fast_sf, attrib = c("value", "length"))
# rnet_sf <- overline(routes_fast_sf, attrib = c("value", "length"), buff_dist = 10)
# plot(rnet$geometry, lwd = rnet$value * 2, col = "grey")
# plot(rnet_sf$geometry,  lwd = rnet_sf$value, add = TRUE)

Description

Takes a series of geographical points and converts them into a spatial (linestring) object representing the potential flows, or 'spatial interaction', between every combination of points.

Usage

points2flow(p)

Arguments

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2odf()

Examples

flow_sf <- points2flow(cents_sf[1:4, ])
plot(flow_sf)

Description

This function makes that makes the creation of sf objects with LINESTRING geometries easy.

Usage

points2line(p)

Arguments

See Also

Other lines: angle_diff(), geo_toptail(), is_linepoint(), line2df(), line2points(), line_bearing(), line_breakup(), line_midpoint(), line_segment(), line_segment1(), line_via(), mats2line(), n_segments(), n_vertices(), onewaygeo(), toptail_buff()

Examples

l_sf <- points2line(cents_sf)
plot(l_sf)

Description

Takes a series of geographical points and converts them into a data.frame representing the potential flows, or 'spatial interaction', between every combination of points.

Usage

points2odf(p)

Arguments

See Also

Other od: od2line(), od2odf(), od_aggregate_from(), od_aggregate_to(), od_coords(), od_coords2line(), od_id, od_id_order(), od_oneway(), od_to_odmatrix(), odmatrix_to_od(), points2flow()

Examples

points2odf(cents_sf)

Description

Returns a character vector of NE, SE, SW, NW corresponding to north-east, south-east quadrants respectively. If number_out is TRUE, returns numbers from 1:4, respectively.

Usage

quadrant(x, cent = NULL, number_out = FALSE)

Arguments

See Also

Other geo: bbox_scale(), bind_sf(), geo_bb(), geo_bb_matrix(), geo_buffer(), geo_length(), geo_projected(), geo_select_aeq()

Examples

x = zones_sf
(quads <- quadrant(x))
plot(x$geometry, col = factor(quads))

Description

Import and format Australian Bureau of Statistics (ABS) TableBuilder files

Usage

read_table_builder(dataset, filetype = "csv", sheet = 1, removeTotal = TRUE)

Arguments

Details

The Australian Bureau of Statistics (ABS) provides customised tables for census and other datasets in a format that is difficult to use in R because it contains rows with additional information. This function imports the original (unzipped) TableBuilder files in .csv or .xlsx format before creating an R dataframe with the data.

Note: we recommend using the readabs package for this purpose.

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, route_network_sf, route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Description

Add a node to route network

Usage

rnet_add_node(rnet, p)

Arguments

Examples

sample_routes <- routes_fast_sf[2:6, NULL]
sample_routes$value <- rep(1:3, length.out = 5)
rnet <- overline2(sample_routes, attrib = "value")
p <- sf::st_sfc(sf::st_point(c(-1.540, 53.826)), crs = sf::st_crs(rnet))
r_split <- route_split(rnet, p)
plot(rnet$geometry, lwd = rnet$value * 5, col = "grey")
plot(p, cex = 9, add = TRUE)
plot(r_split, col = 1:nrow(r_split), add = TRUE, lwd = r_split$value)

Description

Get points at the beginner and end of linestrings

Usage

rnet_boundary_points(rnet)

rnet_boundary_df(rnet)

rnet_boundary_unique(rnet)

rnet_boundary_points_lwgeom(rnet)

rnet_duplicated_vertices(rnet, n = 2)

Arguments

Examples

has_sfheaders <- requireNamespace("sfheaders", quietly = TRUE)
if(has_sfheaders) {
rnet <- rnet_roundabout
bp1 <- rnet_boundary_points(rnet)
bp2 <- line2points(rnet) # slower version with lwgeom
bp3 <- rnet_boundary_points_lwgeom(rnet) # slower version with lwgeom
bp4 <- rnet_boundary_unique(rnet)
nrow(bp1)
nrow(bp3)
identical(sort(sf::st_coordinates(bp1)), sort(sf::st_coordinates(bp2)))
identical(sort(sf::st_coordinates(bp3)), sort(sf::st_coordinates(bp4)))
plot(rnet$geometry)
plot(bp3, add = TRUE)
}

Description

This function breaks up a LINESTRING geometry into multiple LINESTRING(s). It is used mainly for preserving routability of an object that is created using Open Street Map data. See details, stplanr/issues/282, and stplanr/issues/416.

Usage

rnet_breakup_vertices(rnet, verbose = FALSE)

Arguments

Details

A LINESTRING geometry is broken-up when one of the two following conditions are met:

1. two or more LINESTRINGS share a POINT which is a boundary point for some LINESTRING(s), but not all of them (see the rnet_roundabout example);

2. two or more LINESTRINGS share a POINT which is not in the boundary of any LINESTRING (see the rnet_cycleway_intersection example).

The problem with the first example is that, according to algorithm behind SpatialLinesNetwork(), two LINESTRINGS are connected if and only if they share at least one point in their boundaries. The roads and the roundabout are clearly connected in the "real" world but the corresponding LINESTRING objects do not share two distinct boundary points. In fact, by Open Street Map standards, a roundabout is represented as a closed and circular LINESTRING, and this implies that the roundabout is not connected to the other roads according to SpatialLinesNetwork() definition. By the same reasoning, the roads in the second example are clearly connected in the "real" world, but they do not share any point in their boundaries. This function is used to solve this type of problem.

Value

An sf or sfc object with LINESTRING geometry created after breaking up the input object.

See Also

Other rnet: gsection(), islines(), overline(), rnet_group()

Examples

library(sf)
def_par <- par(no.readonly = TRUE)
par(mar = rep(0, 4))

# Check the geometry of the roundabout example. The dots represent the
# boundary points of the LINESTRINGS. The "isolated" red point in the
# top-left is the boundary point of the roundabout, and it is not shared
# with any other street.
plot(st_geometry(rnet_roundabout), lwd = 2, col = rainbow(nrow(rnet_roundabout)))
boundary_points <- st_geometry(line2points(rnet_roundabout))
points_cols <- rep(rainbow(nrow(rnet_roundabout)), each = 2)
plot(boundary_points, pch = 16, add = TRUE, col = points_cols, cex = 2)

# Clean the roundabout example.
rnet_roundabout_clean <- rnet_breakup_vertices(rnet_roundabout)
plot(st_geometry(rnet_roundabout_clean), lwd = 2, col = rainbow(nrow(rnet_roundabout_clean)))
boundary_points <- st_geometry(line2points(rnet_roundabout_clean))
points_cols <- rep(rainbow(nrow(rnet_roundabout_clean)), each = 2)
plot(boundary_points, pch = 16, add = TRUE, col = points_cols)
# The roundabout is now routable since it was divided into multiple pieces
# (one for each colour), which, according to SpatialLinesNetwork() function,
# are connected.

# Check the geometry of the overpasses example. This example is used to test
# that this function does not create any spurious intersection.
plot(st_geometry(rnet_overpass), lwd = 2, col = rainbow(nrow(rnet_overpass)))
boundary_points <- st_geometry(line2points(rnet_overpass))
points_cols <- rep(rainbow(nrow(rnet_overpass)), each = 2)
plot(boundary_points, pch = 16, add = TRUE, col = points_cols, cex = 2)
# At the moment the network is not routable since one of the underpasses is
# not connected to the other streets.

# Check interactively.
# mapview::mapview(rnet_overpass)

# Clean the network. It should not create any spurious intersection between
# roads located at different heights.
rnet_overpass_clean <- rnet_breakup_vertices(rnet_overpass)
plot(st_geometry(rnet_overpass_clean), lwd = 2, col = rainbow(nrow(rnet_overpass_clean)))
# Check interactively.
# mapview::mapview(rnet_overpass)

# Check the geometry of the cycleway_intersection example. The black dots
# represent the boundary points and we can see that the two roads are not
# connected according to SpatialLinesNetwork() function.
plot(
  rnet_cycleway_intersection$geometry,
  lwd = 2,
  col = rainbow(nrow(rnet_cycleway_intersection)),
  cex = 2
)
plot(st_geometry(line2points(rnet_cycleway_intersection)), pch = 16, add = TRUE)
# Check interactively
# mapview::mapview(rnet_overpass)

# Clean the rnet object and plot the result.
rnet_cycleway_intersection_clean <- rnet_breakup_vertices(rnet_cycleway_intersection)
plot(
  rnet_cycleway_intersection_clean$geometry,
  lwd = 2,
  col = rainbow(nrow(rnet_cycleway_intersection_clean)),
  cex = 2
)
plot(st_geometry(line2points(rnet_cycleway_intersection_clean)), pch = 16, add = TRUE)

par(def_par)

Description

This function takes an sf object representing a road network and returns only the parts of the network that are in the largest group.

Usage

rnet_connected(rnet)

Arguments

Value

An sf object representing the largest group in the network

Examples

rnet <- rnet_breakup_vertices(stplanr::osm_net_example)
rnet_largest_group <- rnet_connected(rnet)
plot(rnet$geometry)
plot(rnet_largest_group$geometry)

Description

See data-raw/rnet_cycleway_intersection for details on how this was created.

Format

A sf object

Examples

rnet_cycleway_intersection

Description

Extract nodes from route network

Usage

rnet_get_nodes(rnet, p = NULL)

Arguments

Examples

rnet_get_nodes(route_network_sf)

Description

This function assigns linestring features, many of which in an sf object can form route networks, into groups. By default, the function igraph::clusters() is used to determine group membership, but any ⁠igraph::cluster*()⁠ function can be used. See examples and the web page igraph.org/r/doc/communities.html for more information. From that web page, the following clustering functions are available:

Usage

rnet_group(rnet, ...)

## Default S3 method:
rnet_group(rnet, ...)

## S3 method for class 'sfc'
rnet_group(
  rnet,
  cluster_fun = igraph::clusters,
  d = NULL,
  as.undirected = TRUE,
  ...
)

## S3 method for class 'sf'
rnet_group(
  rnet,
  cluster_fun = igraph::clusters,
  d = NULL,
  as.undirected = TRUE,
  ...
)

Arguments

Details

⁠cluster_edge_betweenness, cluster_fast_greedy, cluster_label_prop,⁠ ⁠cluster_leading_eigen, cluster_louvain, cluster_optimal, cluster_spinglass, cluster_walktrap⁠

Value

If the input rnet is an sf/sfc object, it returns an integer vector reporting the groups of each network element. If the input is an sfNetwork object, it returns an sfNetwork object with an extra column called rnet_group representing the groups of each network element. In the latter case, the connectivity of the spatial object is derived from the sfNetwork object.

Note

These functions rely on the igraph package. If igraph is not installed, the function will return a message.

See Also

Other rnet: gsection(), islines(), overline(), rnet_breakup_vertices()

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
rnet <- rnet_breakup_vertices(stplanr::osm_net_example)
rnet$group <- rnet_group(rnet)
plot(rnet["group"])
# mapview::mapview(rnet["group"])
rnet$group_25m <- rnet_group(rnet, d = 25)
plot(rnet["group_25m"])
rnet$group_walktrap <- rnet_group(rnet, igraph::cluster_walktrap)
plot(rnet["group_walktrap"])
rnet$group_louvain <- rnet_group(rnet, igraph::cluster_louvain)
plot(rnet["group_louvain"])
rnet$group_fast_greedy <- rnet_group(rnet, igraph::cluster_fast_greedy)
plot(rnet["group_fast_greedy"])
}

Description

Join function that adds columns to a 'target' route network sf object from a 'source' route network that contains the base geometry, e.g. from OSM

Usage

rnet_join(
  rnet_x,
  rnet_y,
  dist = 5,
  length_y = TRUE,
  key_column = 1,
  subset_x = FALSE,
  dist_subset = NULL,
  segment_length = 0,
  endCapStyle = "FLAT",
  contains = TRUE,
  max_angle_diff = NULL,
  crs = geo_select_aeq(rnet_x),
  ...
)

Arguments

Details

The output is an sf object containing polygons representing buffers around the route network in rnet_x. The examples below demonstrate how to join attributes from a route network object created with the function overline() onto OSM geometries.

Note: The main purpose of this function is to join an ID from rnet_x onto rnet_y. Subsequent steps, e.g. with dplyr::inner_join() are needed to join the attributes back onto rnet_x. There are rarely 1-to-1 relationships between spatial network geometries so we take care when using this function.

See #505 for details and a link to an interactive example of inputs and outputs shown below.

Examples

library(sf)
library(dplyr)
plot(osm_net_example$geometry, lwd = 5, col = "grey", add = TRUE)
plot(route_network_small["flow"], add = TRUE)
rnetj <- rnet_join(osm_net_example, route_network_small, dist = 9)
rnetj2 <- rnet_join(osm_net_example, route_network_small, dist = 9, segment_length = 10)
# library(mapview)
# mapview(rnetj, zcol = "flow") +
#   mapview(rnetj2, zcol = "flow") +
#   mapview(route_network_small, zcol = "flow")
plot(sf::st_geometry(rnetj))
plot(rnetj["flow"], add = TRUE)
plot(rnetj2["flow"], add = TRUE)
plot(route_network_small["flow"], add = TRUE)
summary(rnetj2$length_y)
rnetj_summary <- rnetj2 %>%
  filter(!is.na(length_y)) %>%
  sf::st_drop_geometry() %>%
  group_by(osm_id) %>%
  summarise(
    flow = weighted.mean(flow, length_y, na.rm = TRUE),
  )
osm_joined_rnet <- dplyr::left_join(osm_net_example, rnetj_summary)
plot(sf::st_geometry(route_network_small))
plot(route_network_small["flow"], lwd = 3, add = TRUE)
plot(sf::st_geometry(osm_joined_rnet), add = TRUE)
# plot(osm_joined_rnet[c("flow")], lwd = 9, add = TRUE)
# Improve fit between geometries and performance by subsetting rnet_x
osm_subset <- rnet_subset(osm_net_example, route_network_small, dist = 5)
osm_joined_rnet <- dplyr::left_join(osm_subset, rnetj_summary)
plot(route_network_small["flow"])
# plot(osm_joined_rnet[c("flow")])
# mapview(joined_network) +
#   mapview(route_network_small)

Description

This is a small wrapper around rnet_join(). In most cases we recommend using rnet_join() directly, as it gives more control over the results

Usage

rnet_merge(
  rnet_x,
  rnet_y,
  dist = 5,
  funs = NULL,
  sum_flows = TRUE,
  crs = geo_select_aeq(rnet_x),
  ...
)

Arguments

Value

An sf object with the same geometry as rnet_x

Examples

# The source object:
rnet_y <- route_network_small["flow"]
# The target object
rnet_x <- rnet_subset(osm_net_example[1], rnet_y)
plot(rnet_x$geometry, lwd = 5)
plot(rnet_y$geometry, add = TRUE, col = "red", lwd = 2)
rnet_y$quietness <- rnorm(nrow(rnet_y))
funs <- list(flow = sum, quietness = mean)
rnet_merged <- rnet_merge(rnet_x[1], rnet_y[c("flow", "quietness")],
  dist = 9, segment_length = 20, funs = funs
)
plot(rnet_y$geometry, lwd = 5, col = "lightgrey")
plot(rnet_merged["flow"], add = TRUE, lwd = 2)

# # With a different CRS
rnet_xp <- sf::st_transform(rnet_x, "EPSG:27700")
rnet_yp <- sf::st_transform(rnet_y, "EPSG:27700")
rnet_merged <- rnet_merge(rnet_xp[1], rnet_yp[c("flow", "quietness")],
  dist = 9, segment_length = 20, funs = funs
)
plot(rnet_merged["flow"])
# rnet_merged2 = rnet_merge(rnet_x[1], rnet_y[c("flow", "quietness")],
#                          dist = 9, segment_length = 20, funs = funs,
#                          crs = "EPSG:27700")
# waldo::compare(rnet_merged, rnet_merged2)
# plot(rnet_merged$flow, rnet_merged2$flow)
# # Larger example
# system("gh release list")
# system("gh release upload v1.0.2 rnet_*")
# List the files released in v1.0.2:
# system("gh release download v1.0.2")
# rnet_x = sf::read_sf("rnet_x_ed.geojson")
# rnet_y = sf::read_sf("rnet_y_ed.geojson")
# rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 9, segment_length = 20, funs = funs)

Description

See data-raw/rnet_overpass.R for details on how this was created.

Format

A sf object

Examples

rnet_overpass

Description

See data-raw/rnet_roundabout.R for details on how this was created.

Format

A sf object

Examples

rnet_roundabout

Description

Subset one route network based on overlaps with another

Usage

rnet_subset(
  rnet_x,
  rnet_y,
  dist = 10,
  crop = TRUE,
  min_length = 20,
  rm_disconnected = TRUE
)

Arguments

Examples

rnet_x <- osm_net_example[1]
rnet_y <- route_network_small["flow"]
plot(rnet_x$geometry, lwd = 5)
plot(rnet_y$geometry, add = TRUE, col = "red", lwd = 3)
rnet_x_subset <- rnet_subset(rnet_x, rnet_y)
plot(rnet_x_subset, add = TRUE, col = "blue")

Description

Takes origins and destinations, finds the optimal routes between them and returns the result as a spatial (sf or sp) object. The definition of optimal depends on the routing function used

Usage

route(
  from = NULL,
  to = NULL,
  l = NULL,
  route_fun = cyclestreets::journey,
  wait = 0,
  n_print = 10,
  list_output = FALSE,
  cl = NULL,
  ...
)

Arguments

See Also

Other routes: route_dodgr(), route_osrm()

Other routes: route_dodgr(), route_osrm()

Examples

# Todo: add examples

Description

This function assumes that elevations and distances are in the same units.

Usage

route_average_gradient(elevations, distances)

Arguments

See Also

Other route_funs: route_rolling_average(), route_rolling_diff(), route_rolling_gradient(), route_sequential_dist(), route_slope_matrix(), route_slope_vector()

Examples

r1 <- od_data_routes[od_data_routes$route_number == 2, ]
elevations <- r1$elevations
distances <- r1$distances
route_average_gradient(elevations, distances) # an average of a 4% gradient

Description

See bikecitizens.net for an interactive version of the routing engine used by BikeCitizens.

Usage

route_bikecitizens(
  from = NULL,
  to = NULL,
  base_url = "https://map.bikecitizens.net/api/v1/locations/route.json",
  cccode = "gb-leeds",
  routing_profile = "balanced",
  bike_profile = "citybike",
  from_lat = 53.8265,
  from_lon = -1.576195,
  to_lat = 53.80025,
  to_lon = -1.51577
)

Arguments

Details

See the bikecitizens.R file in the data-raw directory of the package's development repository for details on usage and examples.

Description

Route on local data using the dodgr package

Usage

route_dodgr(from = NULL, to = NULL, l = NULL, net = NULL)

Arguments

See Also

Other routes: route(), route_osrm()

Examples

if (requireNamespace("dodgr")) {
  from <- c(-1.5327, 53.8006) # from <- geo_code("pedallers arms leeds")
  to <- c(-1.5279, 53.8044) # to <- geo_code("gzing")
  # next 4 lines were used to generate `stplanr::osm_net_example`
  # pts <- rbind(from, to)
  # colnames(pts) <- c("X", "Y")
  # net <- dodgr::dodgr_streetnet(pts = pts, expand = 0.1)
  # osm_net_example <- net[c("highway", "name", "lanes", "maxspeed")]
  r <- route_dodgr(from, to, net = osm_net_example)
  plot(osm_net_example$geometry)
  plot(r$geometry, add = TRUE, col = "red", lwd = 5)
}

Description

Find the shortest path using Google's services. See the mapsapi package for details.

Usage

route_google(from, to, mode = "walking", key = Sys.getenv("GOOGLE"), ...)

Arguments

Examples

## Not run: 
from <- "university of leeds"
to <- "pedallers arms leeds"
r <- route(from, to, route_fun = cyclestreets::journey)
plot(r)
# r_google <- route(from, to, route_fun = mapsapi::mp_directions) # fails
r_google1 <- route_google(from, to)
plot(r_google1)
r_google <- route(from, to, route_fun = route_google)

## End(Not run)

Description

This function was written as a drop-in replacement for sf::st_nearest_feature(), which only works with recent versions of GEOS.

Usage

route_nearest_point(r, p, id_out = FALSE)

Arguments

Examples

r <- routes_fast_sf[2:6, NULL]
p <- sf::st_sfc(sf::st_point(c(-1.540, 53.826)), crs = sf::st_crs(r))
route_nearest_point(r, p, id_out = TRUE)
r_nearest <- route_nearest_point(r, p)
plot(r$geometry)
plot(p, add = TRUE)
plot(r_nearest, lwd = 5, add = TRUE)

Description

The flow of commuters using different segments of the road network represented in the flowlines_sf() and routes_fast_sf() datasets

Format

A spatial lines dataset 80 rows and 1 column

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_small, routes_fast_sf, routes_slow_sf, zones_sf

Description

The flow between randomly selected vertices on the osm_net_example. See data-raw/route_network_small.R for details.

Format

A spatial lines dataset with one column: flow

See Also

Other data: cents_sf, destinations_sf, flow, flow_dests, flowlines_sf, od_data_lines, od_data_routes, od_data_sample, osm_net_example, read_table_builder(), route_network_sf, routes_fast_sf, routes_slow_sf, zones_sf

Description

This function is a simplified and (because it uses GeoJSON not binary polyline format) slower R interface to OSRM routing services compared with the excellent osrm::osrmRoute() function (which can be used via the route()) function.

Usage

route_osrm(
  from,
  to,
  osrm.server = "https://routing.openstreetmap.de/",
  osrm.profile = "foot"
)

Arguments

See Also

Other routes: route(), route_dodgr()

Examples

# Examples no longer working due to API being down
# l1 = od_data_lines[49, ]
# l1m = od_coords(l1)
# from = l1m[, 1:2]
# to = l1m[, 3:4]
# if(curl::has_internet()) {
# r_foot = route_osrm(from, to)
# r_bike = route_osrm(from, to, osrm.profile = "bike")
# r_car = route_osrm(from, to, osrm.profile = "car")
# plot(r_foot$geometry, lwd = 9, col = "grey")
# plot(r_bike, col = "blue", add = TRUE)
# plot(r_car, col = "red", add = TRUE)
# }

Description

This function calculates a simple rolling mean in base R. It is useful for calculating route characteristics such as mean distances of segments and changes in gradient.

Usage

route_rolling_average(x, n = 3)

Arguments

See Also

Other route_funs: route_average_gradient(), route_rolling_diff(), route_rolling_gradient(), route_sequential_dist(), route_slope_matrix(), route_slope_vector()

Examples

y <- od_data_routes$elevations[od_data_routes$route_number == 2]
y
route_rolling_average(y)
route_rolling_average(y, n = 1)
route_rolling_average(y, n = 2)
route_rolling_average(y, n = 3)

Description

This function calculates a simple rolling mean in base R. It is useful for calculating route characteristics such as mean distances of segments and changes in gradient.

Usage

route_rolling_diff(x, lag = 1, abs = TRUE)

Arguments

See Also

Other route_funs: route_average_gradient(), route_rolling_average(), route_rolling_gradient(), route_sequential_dist(), route_slope_matrix(), route_slope_vector()

Examples

r1 <- od_data_routes[od_data_routes$route_number == 2, ]
y <- r1$elevations
route_rolling_diff(y, lag = 1)
route_rolling_diff(y, lag = 2)
r1$elevations_diff_1 <- route_rolling_diff(y, lag = 1)
r1$elevations_diff_n <- route_rolling_diff(y, lag = 1, abs = FALSE)
d <- cumsum(r1$distances) - r1$distances / 2
diff_above_mean <- r1$elevations_diff_1 + mean(y)
diff_above_mean_n <- r1$elevations_diff_n + mean(y)
plot(c(0, cumsum(r1$distances)), c(y, y[length(y)]), ylim = c(80, 130))
lines(c(0, cumsum(r1$distances)), c(y, y[length(y)]))
points(d, diff_above_mean)
points(d, diff_above_mean_n, col = "blue")
abline(h = mean(y))

Description

Calculate rolling average gradient from elevation data at segment level

Usage

route_rolling_gradient(elevations, distances, lag = 1, n = 2, abs = TRUE)

Arguments

See Also

Other route_funs: route_average_gradient(), route_rolling_average(), route_rolling_diff(), route_sequential_dist(), route_slope_matrix(), route_slope_vector()

Examples

r1 <- od_data_routes[od_data_routes$route_number == 2, ]
y <- r1$elevations
distances <- r1$distances
route_rolling_gradient(y, distances)
route_rolling_gradient(y, distances, abs = FALSE)
route_rolling_gradient(y, distances, n = 3)
route_rolling_gradient(y, distances, n = 4)
r1$elevations_diff_1 <- route_rolling_diff(y, lag = 1)
r1$rolling_gradient <- route_rolling_gradient(y, distances, n = 2)
r1$rolling_gradient3 <- route_rolling_gradient(y, distances, n = 3)
r1$rolling_gradient4 <- route_rolling_gradient(y, distances, n = 4)
d <- cumsum(r1$distances) - r1$distances / 2
diff_above_mean <- r1$elevations_diff_1 + mean(y)
par(mfrow = c(2, 1))
plot(c(0, cumsum(r1$distances)), c(y, y[length(y)]), ylim = c(80, 130))
lines(c(0, cumsum(r1$distances)), c(y, y[length(y)]))
points(d, diff_above_mean)
abline(h = mean(y))
rg <- r1$rolling_gradient
rg[is.na(rg)] <- 0
plot(c(0, d), c(0, rg), ylim = c(0, 0.2))
points(c(0, d), c(0, r1$rolling_gradient3), col = "blue")
points(c(0, d), c(0, r1$rolling_gradient4), col = "grey")
par(mfrow = c(1, 1))