• The software (tools of the trade)
• Context (motivations)
• Building the National Propensity to Cycle Tool (NPCT)
• Sustainable transport planning with R
• Esperanzas y sueños

See the free, open source, online tutorial github.com/robinlovelace/Creating-maps-in-R.

Can be installed and loaded in 6 lines of code:

pkgs <- c("devtools", "shiny", "rgdal", "rgeos", "ggmap") # official packages
install.packages(pkgs) 
library(devtools) # enables installation of leaflet
gh_pkgs <- c("rstudio/leaflet", "robinlovelace/stplanr") 
install_github(gh_pkgs) # install packages on github
lapply(c(pkgs, "leaflet", "stplanr"), library, character.only = T) # load all

RStudio Desktop is highly recommended for Shiny development.

shiny is a framework for creating online interactive data visualisation 'apps'.

• A framework for making R output interactive
• An 'app' development framework
• A (small) growing community of developers centred around RStudio
• A way to structure online tools: separation of GUI and server via server.R and ui.R files.

• animate package
• Google Charts (googleVis::) and
• plotly
• D3
• Tableau

For maps

• Leaflet (supported by Shiny)
• Google Maps API

Build's on R's existing strengths

• Data visualisation (ggplot2)
• Increasingline widely understood language for querying data
• Amazing range of add-on packages

Flexibility

Not so good for

• Scalability
• Database interaction
• Low-level control

# type this to find out!
runExample()

## Valid examples are "01_hello", "02_text", "03_reactivity", "04_mpg", "05_sliders", "06_tabsets", "07_widgets", "08_html", "09_upload", "10_download", "11_timer"

An R interface to the Leaflet JavaScript library, compatible with Shiny.

cent <- geocode("Girona")
leaflet() %>% 
  addTiles() %>%
  addCircleMarkers(data = cent)

• House of Commons Criticising the DfT for using closed models
• 'Impact' and 'engagement' increasingly important for research funding
• Complex data cannot be adequately summarised in a single static graphic
• We've been contracted by the DfT (£100,000+) to create a 'National Propensity to Cycle Tool' (NPCT)!

See: cedar.iph.cam.ac.uk/research/modelling/npct-tool

• AURIN
• Leeds-Bradford cycle path interactive map
• DECC's 'Energy 2050' planning tool
• Walkability index site
• Interactive download of bicycle paths

• Interactive basemap
• Points, lines and polygons
• Cascading effects of different scenarios > - Tidy!

runApp("~/repos/pct-shiny/master/", launch.browser = T)

Key components:

|-- master
|   |-- master.R
|   |-- server.R
|   |-- ui.R
|   `-- pct-shiny-funs.R 
|-- manchester
|   |-- server.R (links to ../master.R)
|   `-- ui.R (links to ../ui.R)
`-- README.Rmd

runApp("~/repos/pct-shiny/norwich/", launch.browser = T)
runApp("~/repos/pct-shiny/cambridge/", launch.browser = T)
runApp("~/repos/pct-shiny/manchester/", launch.browser = T)

start_time <- Sys.time() # for timing the script

la <- "manchester" # Name of the local authority
dir.create(paste0("pct-data/", la))

# ... 200 + lines of code here!
# ...

end_time <- Sys.time()
end_time - start_time

kable(head(flow[1:3]))

library(stplanr)
flowlines <- gFlow2line(flow = flow, zones = cents)
plot(flowlines)

This code is not run - you need your own API key

example(gLines2CyclePath)
data(package = "stplanr", "flowlines")
?route_cyclestreet
plot(flowlines)
Sys.setenv(CYCLESTREET = 'eccbf612-214e-437d-8b73-06bdf9e68731')
routes_fast <- gLines2CyclePath(flowlines)
routes_slow <- gLines2CyclePath(flowlines, "quietest")
lines(routes_fast, col = "red")
lines(routes_slow, col = "green")

lanc_2_lds <- route_graphhopper(from = "University of Lancaster", to = "Leeds")

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=University+of+Lancaster&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Leeds&sensor=false

## [1] "The request sent was: https://graphhopper.com/api/1/route?point=54.0103942%2C-2.7877294&point=53.8007554%2C-1.5490774&vehicle=bike&locale=en-US&debug=true&points_encoded=false&key=eccbf612-214e-437d-8b73-06bdf9e6877e&elevation=true"

# nominatim::address_lookup("University of Lancaster")

# for online mapping
# leaflet() %>% addTiles() %>% addPolylines(data = lanc_2_lds)

plot(lanc_2_lds)

lanc_2_lds@data

##       time     dist  climb
## 1 461.5743 111422.9 2075.8

data("routes_fast","routes_slow")
flowlines <- spTransform(flowlines, CRS("+init=epsg:4326"))
plot(flowlines)
lines(routes_fast, col = "red")
lines(routes_slow, col = "green")

gLines2CyclePath(l, plan = "fastest")

This problem was solved by Barry Rowlingson on gis.stackexchange:

• Espero que el Departamento de Transporte utilise nuestra herramienta para prioritisar donde y como invertir en sistemas de transporte 'post-carbon'
• Espero que el esfuerzo este util para otros investigadores que quieren un sistema de transporte mas saludable
• Sueño que algun dia las decisiones estrategicas se haran usando datos, metodos y software libre
• Sueño que nuestra herremienta mejorara las decisiones estrategicas en muchos paises del mundo

• ¿Preguntas?