In cyipt/cyipt: Cycling Infrastructure Prioritisation Toolkit

The scale of the challenge, from this...

options(htmltools.dir.version = FALSE)
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my_bib = ReadBib("/home/robin/h/allrefs.bib", check = FALSE)

# save pdf
webshot::webshot("slides/open-tools-lida.html", "/tmp/open-tools-lida.pdf")

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knitr::include_graphics("https://pbs.twimg.com/media/DOH94nXUIAAgcll.jpg")

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Source: Brent Toderian

Any ideas which city this is?

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Towards cycling being the natural choice

knitr::include_graphics("https://pbs.twimg.com/media/DJaWCo0U8AAzQGW.jpg:large")

Source: Brent Toderian

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Problem: evidence overload

knitr::include_graphics("https://larrycuban.files.wordpress.com/2015/02/data-overload-2.jpg")

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• Challenge: operationalising this data

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• Needs to be provided in 'actionable' format

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• Requires skills + infrastructure

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A case study of an actionable tool: www.pct.bike

knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/figures/pct-frontpage.png")

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Building the web application

• Started by writing various messy scripts

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But soon that became unsustainable

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Developing stplanr

• With support from colleagues in LIDA I created stplanr, which works like this (after you have installed R):

library(stplanr)
r = route_cyclestreet(
  from = "Chapeltown, Leeds",
  to = "University of Leeds"
  )
r

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Visualising the results

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• Thanks to existing open source tools for visualisation, we can plot this, e.g. with:

library(leaflet)
m = leaflet() %>% 
  addTiles() %>% 
  addPolylines(data = r) %>% 
  addMiniMap() %>% 
  addScaleBar()

]

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m

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Capabilities of stplanr

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• Convert origin-destination pairs into geographic lines

Example datasets in stplanr

Centroids (open access data):

head(cents_sf[1], 3)

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Origin-destination (OD) pairs

head(flow[c(1:3)], 2)

]

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Making tabular data spatial

Conversion to a spatial object with stplanr

l = od2line(flow, cents_sf)
sf:::plot.sf(l[3:5], lwd = l$All / mean(l$All))

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Interactive visualisation is key

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• Previous plot is not particularly revealing

• Another visualisation will help

library(tmap)
l$pwalk = l$On.foot / l$All
m2 = tm_shape(l) +
  tm_lines(
    "pwalk",
    lwd = "All",
    scale = 10,
    palette = "RdYlBu")

]

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• Make the data come alive

tmap_leaflet(m2)

]

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Ensuring impact

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The ‘Propensity to Cycle Tool’ shows that if residents of Greater Manchester were as likely to cycle as the Dutch we would increase commuter journeys ten-fold, leaving room on the road for people who had to drive. This level of cycling would lead to an estimated £1 billion per year saving to individuals and the local economy because of the resulting health benefits. (British Cycling’s chief executive, Julie Harrington, November 2017 see britishcycling.org.uk)

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~ 3 months later...

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City cycling czar Chris Boardman has unveiled plans for a thousand miles of interlinked bike and walking lanes across Greater Manchester. Dubbed ‘Beelines’, the game-changing 10-year, £1.5bn proposal includes 75 miles of segregated cycle lanes similar to those found in Holland and Denmark (Manchester Evening News)

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Deployment

• Live demo at http://www.pct.bike/
• Get policy makers interested

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knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/figures/front-page-leeds-pct-demo.png")

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Links + references

• Contact: [e-mail] or @robinlovelace

• If you want to code in R 4 transport, start here: https://geocompr.robinlovelace.net/spatial-class.html
• The PCT in action: http://www.pct.bike/
• A prototype of the CyIPT: http://cyipt.bike/
• Resource on R for transport: http://geocompr.robinlovelace.net/transport.html

Citation

Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J., 2017. The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of Transport and Land Use 10. https://doi.org/10.5198/jtlu.2016.862

cyipt/cyipt documentation built on Aug. 16, 2020, 10:24 p.m.