• Introduction: the Propensity to Cycle Tool
• Packages for transport data
• Where next?

• 3 years in the making
• Origins go back further

Concept (PhD) -> Job at UoL (2009 - 2013)
 Discovery of R programming and shiny (2013)
  'Propensity to Cycle' bid by DfT via SDG (2014)
    Link-up with Cambridge University and colleagues (2015)
     Implementation on national OD dataset, 700k routes (2016)
      Completed LSOA phase (4 million lines!) (2017)

• 2018: (Global PCT?)

• Now publised in the Journal of Transport and Land Use (JTLU) (Lovelace et al. 2017)

Robin Lovelace (Lead Developer, University of Leeds)

• James Woodcock (Principal Investigator, Cambridge University)
• Anna Goodman (Lead Data Analyst, LSHTM)
• Rachel Aldred (Lead Policy and Practice, Westminster University)
• Ali Abbas (User Interface, University of Cambridge)
• Alvaro Ullrich (Data Management, University of Cambridge)
• Nikolai Berkoff (System Architecture, Independent Developer)
• Malcolm Morgan (GIS and infrastructure expert, UoL)

"The PCT is a brilliant example of using Big Data to better plan infrastructure investment. It will allow us to have more confidence that new schemes are built in places and along travel corridors where there is high latent demand."

• Shane Snow: Head of Seamless Travel Team, Sustainable and Acessible Travel Division

"The PCT shows the country’s great potential to get on their bikes, highlights the areas of highest possible growth and will be a useful innovation for local authorities to get the greatest bang for their buck from cycling investments  and realise cycling potential."

• Andrew Jones, Parliamentary Under Secretary of State for Transport

Included in Cycling and Walking Infrastructure Strategy (CWIS) and the Local Cycling and Walking Infrastructure Plan (LCWIP)

Shows on the map where there is high cycling potential, for 4 scenarios of change

• Government Target
• Gender Equality
• Go Dutch
• Ebikes

• Origin-destination data shows 'desire lines'
• How will these shift with cycling uptake

• Test version of pct.bike

stplanr lives here: https://github.com/ropensci/stplanr

Package can be installed from CRAN or GitHub (see the package's README for details), it can be loaded in with library():

install.packages("stplanr") # stable CRAN version
# devtools::install_github("ropensci/stplanr") # dev version

library(stplanr) # also loads spatial package

## Loading required package: sp

• Dev version requires rtools on Windows

• Why?

• Assisted with documentation: ropensci/stplanr/commits?author=karthik
• Set-up Continuous Integration (CI): https://travis-ci.org/ropensci/stplanr

• Provided peer review of the code: https://github.com/ropensci/onboarding/issues/10

• Plug Colin's R package courses

Use @importFrom whenever possible. Right now you have import(openxlsx) and import(sp) in your NAMESPACE file. Just import the functions you need. Same for other pkg deps.

Tests: Pleae write tests to cover at least all the major functions before we accept. Use testthat::skip_on_cran() for any tests that do web requests, so that CRAN tests don't fail in case a service is temporarily down

I think token's can be a bit easier for the user. Right now you have e.g.,

if (!Sys.getenv('CYCLESTREET') == "") {
    cckey <- Sys.getenv('CYCLESTREET')
}
if(is.null(cckey)){
    stop("You must have a CycleStreets.net api key saved as 'cckey'")
}

data("flow", package = "stplanr")
head(flow[c(1:3, 12)])

##        Area.of.residence Area.of.workplace All Bicycle
## 920573         E02002361         E02002361 109       2
## 920575         E02002361         E02002363  38       0
## 920578         E02002361         E02002367  10       0
## 920582         E02002361         E02002371  44       3
## 920587         E02002361         E02002377  34       0
## 920591         E02002361         E02002382   7       0

data("cents", package = "stplanr")
as.data.frame(cents[1:3,-c(3,4)])

##       geo_code  MSOA11NM coords.x1 coords.x2
## 1708 E02002384 Leeds 055 -1.546463  53.80952
## 1712 E02002382 Leeds 053 -1.511861  53.81161
## 1805 E02002393 Leeds 064 -1.524205  53.80410

flow_single_line = flow[4,] # select only the first line
desire_line_single = od2line(flow = flow_single_line, zones = cents)

• We selected a single 'od-pair' (flow[4,])
• The function od2line() matched the cents matching the lines and created a line (the hard bit)
• How? Check the source code!

od2line

## function(flow, zones, destinations = NULL,
##                     zone_code = names(zones)[1],
##                     origin_code = names(flow)[1],
##                     dest_code = names(flow)[2],
##                     zone_code_d = NA, silent = TRUE) {
##   UseMethod("od2line", object = zones)
## }
## <environment: namespace:stplanr>

o = cents[cents$geo_code %in% flow$Area.of.residence[4],]
d = cents[cents$geo_code %in% flow$Area.of.workplace[4],]
l_single = Lines(list(Line(rbind(o@coords, d@coords))), ID = 1)
l_sp = SpatialLines(LinesList = list(l_single))

plot(cents)
points(o, cex = 5)
points(d, cex = 5)
flow[4, 1:3]

##        Area.of.residence Area.of.workplace All
## 920582         E02002361         E02002371  44

plot(l_sp, add = TRUE)

l <- od2line(flow = flow, zones = cents)
# remove lines with no length
l <- l[!l$Area.of.residence == l$Area.of.workplace,]
plot(l, lwd = l$All / 10)

library(tmap)
tmap_mode("view")

## tmap mode set to interactive viewing

qtm(cents) + tm_shape(l) +
  tm_lines(col = "Bicycle", palette = "BuPu", lwd = 5)

stplanr has various functions for route allocation:

route_cyclestreet() # UK, cycling
route_graphhopper() # worldwide, any mode
route_transportapi_public() # UK, public transport
viaroute() # worldwide, any mode

routes_fast = line2route(l = l)

plot(l)
plot(routes_fast, add = T, col = "red")

• Add more services (e.g. OpenTripPlanner)
• Integrate interface
• Add support for OSM data download and analysis
• Via interface to osmdatar

• Work with practitioners to make it more useful for them (e.g. ITP)
• Link with industry (e.g. ITP)
• Make more international
• A global propensity to cycle tool?
• Better handling of GPS data

• Plans to create a toolkit for cycling infrastructure prioritisation - see cyip.bike
• Combines many datasets to identify 'low hanging fruit'

Credit: Malcolm Morgan

• Finish paper submitted to the RJournal
• Transition to sf (1000+ lines of code, 6 month mission underway)
• Integration with other tools e.g. osmdata and dodgr
• Improvements to the user interface
• Feature requests?

• Thanks for listening - get in touch via r.lovelace@leeds.ac.uk or @robinlovelace

• Download and test the dev version with:

devtools::install_github("ropensci/stplanr")

• Create issues on our issue tracker
• Check out the vignette