Yesterday I had an opportunity to present the Propensity to Cycle Tool to a highly informed group: useRs. I was invited by Colin Gillespie of Jumping Rivers to present at UseR Northeast – see their website and associated links for further info.
What is a ‘UseR’? I hear you ask. It’s the name for aficionados of R, an open source statistical language that was used as the ‘back-end’ lying behind the tool – if you’re interested in getting R, see Installing R in Efficient R Programming, by Colin and myself (Gillespie and Lovelace 2016). The audience was very techy. I thus got to talk about the technical side of things without getting blank faces when mentioning ‘packages’ and ‘functions’ used to generate the spatial data underlying the PCT.
The talk went well, I got much interest in the package stplanr, a package which I developed to help process the data such that it could be represented on a map. See https://github.com/ropensci/stplanr for more information on the package and what it does.
I demonstrated the PCT’s network results, which suggested there is huge unmet demand for cycling along Coast Road, which has already received investment to improve facilities for cyclists, although there is still no continuous segregated route along it, according to local cyclists who attended the talk.
The results for Newcastle can be seen here: www.pct.bike/m/?r=north-east
The route network layer under the Go Dutch Scenario is illustrated below.
The slides themselves can be found here: http://rpubs.com/RobinLovelace/316614
Gillespie, C., Lovelace, R., 2016. Efficient R Programming: A Practical Guide to Smarter Programming. O’Reilly Media.