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Generating cohesive cycling network

corenet.Rd

This function processes the provided network data to identify and analyze significant routes based on a percentile threshold. It performs spatial operations, clusters data points, and calculates the largest network component without dangles, aiming to develop a cohesive cycling network infrastructure.

Usage

corenet(
  influence_network,
  cohesive_base_network,
  target_zone,
  key_attribute = "all_fastest_bicycle_go_dutch",
  crs = "EPSG:27700",
  npt_threshold = 1500,
  maxDistPts = 1500,
  minDistPts = 2,
  road_scores = list(`A Road` = 1, `B Road` = 1, `Minor Road` = 1000),
  n_removeDangles = 6,
  penalty_value = 1
)

Arguments

influence_network

The NPT network data, class 'sf'.

cohesive_base_network

Spatial object representing the cohesive base network obtained via function cohesive_network_prep, class 'sf'.

target_zone

Spatial object representing the study area or target zone, class 'sf'.

key_attribute

Attribute used to determine significant network routes, default is "all_fastest_bicycle_go_dutch".

crs

Coordinate reference system for transformation, default is "EPSG:27700".

npt_threshold

Threshold value for filtering the NPT network, default is 1500.

maxDistPts

Distance threshold used in path calculations, default is 1500 meters.

minDistPts

Minimum distance threshold used in path calculations, default is 2 meters.

road_scores

A list of road types and their corresponding scoring weights.

n_removeDangles

Number of iterations to remove dangles from the network, default is 6.

penalty_value

The penalty value for roads with low values, default is 1.

Value

A spatial object representing the largest cohesive component of the network, free of dangles.

Examples

library(sf)
library(dplyr)
library(dbscan)
#> 
#> Attaching package: ‘dbscan’
#> The following object is masked from ‘package:stats’:
#> 
#>     as.dendrogram
library(zonebuilder)

# Load demo data 
os_edinburgh_demo_3km = sf::st_set_crs(os_edinburgh_demo_3km, 27700)
NPT_demo_3km = sf::st_set_crs(NPT_demo_3km, 27700)
base_network = sf::st_transform(os_edinburgh_demo_3km, crs = 27700)
influence_network = sf::st_transform(NPT_demo_3km, crs = 27700)
target_zone = zonebuilder::zb_zone("Edinburgh", n_circles = 2) |>
               sf::st_transform(crs = "EPSG:27700")

# Execute the function
OS_NPT_demo = cohesive_network_prep( base_network = base_network, 
                                influence_network = influence_network, 
                                target_zone = target_zone, 
                                key_attribute = "road_function", 
                                crs = "EPSG:27700", 
                                attribute_values = c("A Road", "B Road", "Minor Road"))
#> Requested number of segments (45185) does not match the number of segments returned by rsgeo (44530).
#> Warning: st_centroid assumes attributes are constant over geometries
#> Joining with `by = join_by(road_function)`
#> [1] "Finished preparing the network data"
OS_NPT_demo$geometry = OS_NPT_demo$geom
coherent_network = corenet(influence_network = OS_NPT_demo, 
                  cohesive_base_network = OS_NPT_demo, 
                  target_zone = target_zone, 
                  key_attribute = "all_fastest_bicycle_go_dutch", 
                  crs = "EPSG:27700",npt_threshold = 1500,maxDistPts = 1500,minDistPts = 2,
                  road_scores = list("A Road" = 1, "B Road" = 1, "Minor Road" = 1000),n_removeDangles = 6,penalty_value = 1)
#> Requested number of segments (5620) does not match the number of segments returned by rsgeo (5505).
#> Warning: st_centroid assumes attributes are constant over geometries
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Warning: Overwriting column(s): from
#> Warning: Overwriting column(s): to
#> Checking if spatial network structure is valid...
#> Spatial network structure is valid