bikeR - a repository for studying risk to people on bicycles, using R
If you are looking to do reproducible road traffic safety research,
please check out the
package and accompanying paper
(Lovelace et al. 2019).
Welcome to bikeR, a store of code and example data underlying research into cycling and the associated risks.
The main branch of this work so far has been an analysis of STATS19, resulting in a paper published in the journal Transportation Research Part F: Traffic Psychology and Behaviour (Lovelace, Roberts, and Kellar 2016).
The reasons for putting this research online are:
To allow others to perform the analysis conducted by me for West Yorkshire for other parts of Great Britain and perhaps the world, without reinventing the wheel.
To help people learn R in general, an open source language for empowering oneself with the tools to extract meaning from data and make visualisations, like this one - see (Lovelace, Nowosad, and Meunchow 2019):
To be specific, the code used to produce this map in R can be found here: github.com/Robinlovelace/bikeR/blob/master/stat19/whereWards.R
- To encourage reproducibility, a conerstone of scientific research, in downloading road data (Padgham et al. 2017)
Lovelace, Robin, Malcolm Morgan, Layik Hama, and Mark Padgham. 2019. “Stats19: A Package for Working with Open Road Crash Data.” Journal of Open Source Software. https://doi.org/10.21105/joss.01181.
Lovelace, Robin, Jakub Nowosad, and Jannes Meunchow. 2019. Geocomputation with R. CRC Press. http://robinlovelace.net/geocompr.
Lovelace, Robin, Hannah Roberts, and Ian Kellar. 2016. “Who, Where, When: The Demographic and Geographic Distribution of Bicycle Crashes in West Yorkshire.” Transportation Research Part F: Traffic Psychology and Behaviour, Bicycling and bicycle safety, 41, Part B. https://doi.org/10.1016/j.trf.2015.02.010.
Padgham, Mark, Robin Lovelace, Maëlle Salmon, and Bob Rudis. 2017. “Osmdata.” The Journal of Open Source Software 2 (14). https://doi.org/10.21105/joss.00305.