Title: Identify Naturally Continuous Lines in a Spatial Network
Version: 0.3.2
Description: Provides functionality to group lines that form naturally continuous lines in a spatial network. The algorithm implemented is based on the Continuity in Street Networks (COINS) method from Tripathy et al. (2021) <doi:10.1177/2399808320967680>, which identifies continuous "strokes" in the network as the line strings that maximize the angles between consecutive segments.
License: Apache License (≥ 2)
URL: https://cityriverspaces.github.io/rcoins/, https://doi.org/10.5281/zenodo.14501805, https://github.com/CityRiverSpaces/rcoins
BugReports: https://github.com/CityRiverSpaces/rcoins/issues
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: ggplot2, knitr, rmarkdown, sfnetworks, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Imports: dplyr, rlang, sf, sfheaders
VignetteBuilder: knitr
Depends: R (≥ 4.1.0)
NeedsCompilation: no
Packaged: 2025-05-27 11:46:37 UTC; fnattino
Author: Francesco Nattino ORCID iD [aut, cre, cph], Claudiu Forgaci ORCID iD [aut], Fakhereh Alidoost ORCID iD [aut], Thijs van Lankveld ORCID iD [ctb], Netherlands eScience Center [fnd]
Maintainer: Francesco Nattino <f.nattino@esciencecenter.nl>
Repository: CRAN
Date/Publication: 2025-05-29 18:20:02 UTC

Get example data

Description

This function retrieves example OpenStreetMap (OSM) data for the city of Bucharest, Romania, from a persistent URL on the 4TU.ResearchData data repository. The dataset includes the street network and the geometry of the Dâmbovița river.

Usage

get_example_data()

Value

A list of sf objects containing the OSM data.

Examples


get_example_data()

Identify naturally continuous lines in a spatial network

Description

Provides functionality to group lines that form naturally continuous lines in a spatial network. The algorithm implemented is based on the Continuity in Street Networks (COINS) method doi:10.1177/2399808320967680, which identifies continuous "strokes" in the network as the line strings that maximize the angles between consecutive segments.

Usage

stroke(
  edges,
  angle_threshold = 0,
  attributes = FALSE,
  flow_mode = FALSE,
  from_edge = NULL
)

Arguments

edges

An object of class sfc (or compatible), including the network edge geometries (should be of type LINESTRING).

angle_threshold

Consecutive line segments can be considered part of the same stroke if the internal angle they form is larger than angle_threshold (in degrees). It should fall in the range 0 <= angle_threshold < 180.

attributes

If TRUE, return a label for each edge, representing the groups each edge belongs to. Only possible for flow_mode = TRUE.

flow_mode

If TRUE, line segments that belong to the same edge are not split across strokes (even if they form internal angles smaller than angle_threshold).

from_edge

Only look for the continuous strokes that include the provided edges or line segments.

Value

An object of class sfc (if attributes = FALSE), a vector with the same length as edges otherwise.

Examples

library(sf)

# Setup a simple network

p1 <- st_point(c(0, 3))
p2 <- st_point(c(2, 1))
p3 <- st_point(c(3, 0))
p4 <- st_point(c(1, 4))
p5 <- st_point(c(3, 2))
p6 <- st_point(c(4, 1))
p7 <- st_point(c(4, 3))
p8 <- st_point(c(5, 3))

l1 <- st_linestring(c(p1, p2, p5))
l2 <- st_linestring(c(p2, p3))
l3 <- st_linestring(c(p4, p5))
l4 <- st_linestring(c(p5, p6))
l5 <- st_linestring(c(p5, p7))
l6 <- st_linestring(c(p7, p8))

network_edges <- st_sfc(l1, l2, l3, l4, l5, l6)

# Identify strokes in the full network with default settings
stroke(network_edges)

# Set a threshold to the angle between consecutive segments
stroke(network_edges, angle_threshold = 150)

# Identify strokes in flow mode (do not break initial edges)
stroke(network_edges, flow_mode = TRUE)

# Instead of returning stroke geometries, return stroke labels
stroke(network_edges, flow_mode = TRUE, attributes = TRUE)

# Identify strokes that continue one (or a subset) of edges
stroke(network_edges, from_edge = 2)
stroke(network_edges, from_edge = c(2, 3))