Given the rapidly increasing volume and availability of origin/destination travel data, techniques to visualise these datasets effectively are in constant evolution. This talk is in four parts, all focusing around a common aim of visualising cycling flow data, in particular data from bikeshare systems. First, I introduce some older straight-line flow visualisations of travel data that I created as part of my own earlier research. I then look at some visualisations where the street network is used to group together flows to improve visibility and realism. I then move to the “new” by looking at two recent projects which I believe will offer great potential for visualisation of cycling flows - firstly the ROpenSci bikedata project which promises an automated approach to both of the earlier methods, and finally Kepler, which potentially solves both recurring presentation issues relating both to data volume and flow visibility, but comes with its own challenges. 

Bio: Oliver O'Brien (@oobr) is a data scientist and web developer at the Consumer Data Research Centre, based at University College London, managing datasets from consumer-focused commercial organisations such as high street retailers and utility companies, for interested parties, as well as conducting his own demographic research and mapping. His “research obsession” for many years has been bikeshare systems, and he has always been keen to exploit and visualise the numerous generally open datasets to understand how the systems are used, and how they impact their users' mobility decisions and the wider cities they operate in.