overline

Overline is a function that takes overlapping linestrings and converts them into a route network (Morgan and Lovelace 2020) as illustrated in a minimal reproducible example below.

library(sf)
library(stplanr)
library(tidyverse)
library(tmap)
sl = routes_fast_sf[2:3, 0]
sl$n = 1:2
plot(sl)

rnet = overline(sl, attrib = "n")
plot(rnet)

sf::write_sf(sl, "minimal-example-input.geojson", delete_dsn = TRUE)
sf::write_sf(rnet, "minimal-example-output.geojson", delete_dsn = TRUE)

The function has been implemented in the overline() function in the R package stplanr. The function works fine for city sized datasets but for national datasets is slow, buggy and not feature complete, as it does not retain OSM IDs. This repo provides a place to discuss and develop example code to solve this problem.

In Python, the input and outputs can be visualised as follows:

import geopandas as gpd
input = gpd.read_file("input.geojson")
input.plot()

output = gpd.read_file("output.geojson")
output.plot()

Example with road names

The example below takes routes at the segment level and calculates average gradient for each segment. Road names are NOT currently implemented in overline() in R.

sl_desire_lines = stplanr::flowlines_sf[2:3, ]
qtm(sl_desire_lines) +
  qtm(sl)

route_segments_minimal = stplanr::route(
  l = sl_desire_lines,
  route_fun = cyclestreets::journey
  )

Most common output is sf

names(route_segments_minimal)

 [1] "Area.of.residence"                   
 [2] "Area.of.workplace"                   
 [3] "All"                                 
 [4] "Work.mainly.at.or.from.home"         
 [5] "Underground..metro..light.rail..tram"
 [6] "Train"                               
 [7] "Bus..minibus.or.coach"               
 [8] "Taxi"                                
 [9] "Motorcycle..scooter.or.moped"        
[10] "Driving.a.car.or.van"                
[11] "Passenger.in.a.car.or.van"           
[12] "Bicycle"                             
[13] "On.foot"                             
[14] "Other.method.of.travel.to.work"      
[15] "id"                                  
[16] "route_number"                        
[17] "name"                                
[18] "distances"                           
[19] "time"                                
[20] "busynance"                           
[21] "elevations"                          
[22] "start_longitude"                     
[23] "start_latitude"                      
[24] "finish_longitude"                    
[25] "finish_latitude"                     
[26] "crow_fly_distance"                   
[27] "event"                               
[28] "whence"                              
[29] "speed"                               
[30] "itinerary"                           
[31] "plan"                                
[32] "note"                                
[33] "length"                              
[34] "quietness"                           
[35] "west"                                
[36] "south"                               
[37] "east"                                
[38] "north"                               
[39] "leaving"                             
[40] "arriving"                            
[41] "grammesCO2saved"                     
[42] "calories"                            
[43] "edition"                             
[44] "gradient_segment"                    
[45] "elevation_change"                    
[46] "provisionName"                       
[47] "gradient_smooth"                     
[48] "geometry"                            

tm_shape(route_segments_minimal) +
  tm_lines("name")

rnet_from_cyclestreets = overline(
  route_segments_minimal,
  attrib = c("All", "gradient_smooth", "quietness"),
  fun = c(sum = sum, mean = mean)
  )
rnet_from_cyclestreets = rnet_from_cyclestreets %>% 
  transmute(All = All_sum, Gradient = gradient_smooth_mean, Quietness = quietness_mean)
plot(rnet_from_cyclestreets)

sf::write_sf(route_segments_minimal, "route_segments_minimal.geojson", delete_dsn = TRUE)
sf::write_sf(rnet_from_cyclestreets, "rnet_from_cyclestreets.geojson", delete_dsn = TRUE)

Large example

A large example plus benchmark is shown below:

# list.files()
cycle_routes_london = pct::get_pct_routes_fast("london")
sf::write_sf(cycle_routes_london, "cycle_routes_london.geojson")
zip("cycle_routes_london.zip", "cycle_routes_london.geojson")
system("gh release upload v0 cycle_routes_london.zip")

system.time({
  cycle_routes_london = geojsonsf::geojson_sf("cycle_routes_london.geojson")
  names(cycle_routes_london)
  rnet = overline(cycle_routes_london, attrib = "foot")
})
# sf::write_sf(rnet, "rnet_london.geojson")
# system("gh release upload v0 rnet_london.geojson")

The operation took around 2 minutes.

References

Morgan, Malcolm, and Robin Lovelace. 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 48 (6): 1684–96. https://doi.org/10.1177/2399808320942779.