Slides: rpubs.com/RobinLovelace

The nature of transport data

  • The same as handling any other kind of data in R
  • But: dynamic, complex (e.g. GTFS) reliant on official feeds
  • That's why using a language that is powerful and flexible is good

Worked example: pct data in West Yorksire

  • We'll download and visualise some transport data
# u_pct = "https://github.com/npct/pct-data/raw/master/west-yorkshire/l.Rds"
# download.file(u_pct, "l.Rds")
library(stplanr)
## Loading required package: sp
l = readRDS("l.Rds")
plot(l)

Analysing where people walk

sel_walk = l$foot > 9
l_walk = l[sel_walk,]
plot(l)
plot(l_walk, add = T, col = "red")

library(dplyr) # for next slide...
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Doing it with sf (!)

l_walk1 = l %>% filter(All > 10) # fails
library(sf)
## Linking to GEOS 3.5.1, GDAL 2.1.3, proj.4 4.9.2, lwgeom 2.3.2 r15302
l_sf = st_as_sf(l)
plot(l_sf[6])

Subsetting with sf

is really fun!

l_walk2 = l_sf %>% 
  filter(foot > 9)
plot(l_sf[6])
plot(l_walk2, add = T)

Exercises

Use sf or sp depending on what you want

  • Find and plot all desire lines where cycling is more common than walking
  • Find all desire which are less than 2.5 km
  • Find all lines in which driving is more common than walking AND are
  • Find and plot all desire lines that are longer. Where are they?
  • Use the following code to download bus stops in the area

Solution

l_sf$distsf = as.numeric(st_length(l_sf))
summary(l_sf$distsf)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   589.7  5204.3  8343.3  8910.0 12238.3 20034.3
l_drive_short = l_sf %>% 
  filter(distsf < 1000 & car_driver > foot)
l_drive_short2 = l_sf %>% 
  filter(distsf < 1000) %>% 
  filter(car_driver > foot)
l_short = l_sf %>% 
  filter(distsf < 1000)
plot(l_drive_short[6])

devtools::install_github("osmdatar/osmdata")

Advanced challenge: download transport data with osmdata

  • You'll need to read-up on the osmdata vignette
library(osmdata)
## Data (c) OpenStreetMap contributors, ODbL 1.0. http://www.openstreetmap.org/copyright
q = opq(bbox = "Leeds") %>% 
  add_feature(key = "railway", value = "station")
stations = osmdata_sf(q = q)
plot(l_sf[6])
plot(stations$osm_points, add = T)