In ropensci/stplanr: Sustainable Transport Planning

Create cents dataset

cents <- rgdal::readOGR(dsn = "/home/robin/npct/pct-bigdata/cents.geojson", layer = "OGRGeoJSON")
# library(geojsonio) # load with the ropensci package geojsonio if rgdal fails
# cents <- geojsonio::geojson_read(x = "~/repos/pct/pct-data/national/cents.geojson")
crs <- sp::CRS("+init=epsg:4326")
crsuk <- sp::CRS("+init=epsg:27700")
cents <- sp::spTransform(x = cents, CRSobj = crsuk)
home <- geo_code("LS7 3HB")
home <- sp::SpatialPoints(matrix(home, ncol = 2), proj4string = crs)
home <- sp::spTransform(x = home, CRSobj = crsuk)
buf <- rgeos::gBuffer(home, width = 2000)
# Check it saved the points OK
cents <- cents[buf, ]
plot(buf)
points(cents)
cents <- sp::spTransform(x = cents, CRSobj = crs)
cents$geo_code <- as.character(cents$geo_code)
library(devtools)
# use_data(cents, overwrite = TRUE)
cents_sf <- sf::st_as_sf(cents)
devtools::use_data(cents_sf)

cents <- rgdal::readOGR(dsn = "/home/robin/npct/pct-bigdata/cents.geojson", layer = "OGRGeoJSON")
# library(geojsonio) # load with the ropensci package geojsonio if rgdal fails
# cents <- geojsonio::geojson_read(x = "~/repos/pct/pct-data/national/cents.geojson")
crs <- sp::CRS("+init=epsg:4326")
crsuk <- sp::CRS("+init=epsg:27700")
cents <- sp::spTransform(x = cents, CRSobj = crsuk)
home <- geo_code("LS7 3HB")
home <- sp::SpatialPoints(matrix(home, ncol = 2), proj4string = crs)
home <- sp::spTransform(x = home, CRSobj = crsuk)
buf <- rgeos::gBuffer(home, width = 2000)
# Check it saved the points OK
cents <- cents[buf, ]
plot(buf)
points(cents)
cents <- sp::spTransform(x = cents, CRSobj = crs)
cents$geo_code <- as.character(cents$geo_code)
library(devtools)
# use_data(cents, overwrite = TRUE)
cents_sf <- sf::st_as_sf(cents)
devtools::use_data(cents_sf)

# sample od data
remotes::install_github("ITSLeeds/pct")
library(pct)
od_data_all = pct::get_od()
sel_local = 
  od_data_all$geo_code1 %in% cents_sf$geo_code &
  od_data_all$geo_code2 %in% cents_sf$geo_code 
od_data_sample = od_data_all[sel_local, ]
od_data_lines = od2line(od_data_sample, cents_sf)
plot(od_data_lines)
od_data_routes_fast = route(l = od_data_lines, route_fun = cyclestreets::journey, plan = "fastest")
pryr::object_size(od_data_routes_fast) # 500+ kB
names(od_data_routes_fast)
od_data_routes = od_data_routes_fast[c(19:ncol(od_data_routes_fast))]
plot(od_data_routes)
pryr::object_size(od_data_routes) # 300+ kB
usethis::use_data(od_data_routes, overwrite = TRUE)
usethis::use_data(od_data_lines)
file.size("data/od_data_routes.rda") # 24 kB - it's fine
system('ls -hal data/od_data_routes.rda')

The code below shows how the od_lnd and c_lnd datasets were created.

library(dplyr)

# get nationwide OD data
od_all = pct::get_od()
nrow(od_all)
#> 2402201
od_all$Active = (od_all$bicycle + od_all$foot) /
    od_all$all * 100
centroids_all = pct::get_centroids_ew() %>% sf::st_transform(4326)
z_london = pct::get_pct_zones(region = "london") %>% 
  select(geo_code, all, foot)
nrow(centroids_all)
#> 7201
london = pct::pct_regions %>% filter(region_name == "london")
centroids_london = centroids_all[london, ]
od_london = od_all %>%
  filter(geo_code1 %in% centroids_london$msoa11cd) %>% 
  filter(geo_code2 %in% centroids_london$msoa11cd)
od_london = od_all[
  od_all$geo_code1 %in% centroids_london$msoa11cd &
  od_all$geo_code2 %in% centroids_london$msoa11cd , 
]

# aim: create a reproducible OD dataset
od_lnd = od_london %>% 
  select(-matches("rail|name|moto|car|tax|home|la_|Active")) %>% 
  filter(geo_code2 == "E02000001") %>% 
  top_n(4, wt = all)
c_lnd = centroids_london %>% 
  filter(msoa11cd %in% c(od$geo_code1, od$geo_code2))
z_lnd = z_london %>% 
  filter(geo_code %in% c_lnd$msoa11cd)
usethis::use_data(od_lnd)
usethis::use_data(c_lnd)
usethis::use_data(z_lnd)