(Note: a good source of open OD data has been made available from the .uk website). From this we can get a good indication of where people cycle at the desire line level. Now, imagine that V1 represents the total number of people travelling between the origin and destination and that V2 represents the number who regularly cycle. However, for multiple variables (e.g. modes of transport) and to prevent giant and unwieldy sparse matrices, the ‘long’ form represented above is much more common. This dataset can also be represent as an ‘od matrix’, where rows represent the origins and columns destinations. There is also movement represented between Zone 1 and 3. What this example OD table means is that 100 units of ‘V1’ and 3 units of V2 travel between zone 1 and zone 2. A more detailed overview of the package’s aims and capabilities is contained in a longer vignette, which has since been published in the R Journal (Lovelace and Ellison 2018). The package was reviewed through the rOpenSci package review process and the package is now hosted on their site. Stplanr has since grown to include a wide range of functions for transport planning.
#TRANSCAD FUNCTIONS HOW TO#
Stplanr was initially developed to answer a practical question: how to convert official data on travel behaviour into geographic objects that can be plotted on a map and analysed using methods from geographical information systems (GIS)? Specifically, how can origin-destination (OD) data, such as the open datasets provided by the UK Data Services WICID portal (see .uk/), be used to estimate cycling potential down to the street levels at city and national levels? The project was initially developed to support the Propensity to Cycle Tool (PCT), which has now been deployed as a national web application hosted at and written-up as an academic paper (Lovelace et al. The purpose of this vignette is to get you up-to-speed with the basics and provide useful links for doing transport research with R.
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