Who, where, when: the demographic and geographic distribution of bicycle crashes in West Yorkshire. (August 2016) - Lovelace, Robin; Roberts, Hannah; Kellar, Ian
Highlights: Analysis of cyclist casualties in area with ambitious cycling plans and investment. Risk of serious injury and death estimated at high geographical resolution. Evidence for spatial and temporal variability in social distribution of risk. West Yorkshire seems especially dangerous for young cyclists. Opportunities to use safety interventions to increase cycling uptake. Abstract: Factors associated with cycle safety, including international differences in injury and mortality rates, protective equipment and bicycle training, have been subject to increasing academic interest. Environmental variables associated with cycle safety have also been scrutinised, but few studies have focussed on geographical factors at the local level. This paper addresses this research gap by analysing a geo-referenced dataset of road traffic incidents, taken from the UK's STATS19 dataset (2005–2012). We investigate incidents involving cyclists within West Yorkshire. This is an interesting case study area as it has an historically low cycling rate but very ambitions cycling plans following investment from the Department of Transport. West Yorkshire is found to be an unusually risky area for cyclists, with an estimated 53 deaths and 1372 serious injuries per billion kilometres cycled, based on census commuting statistics. This is roughly double the national average. This riskiness varies spatially and temporally, broadly in line with expectations from the previous literature. An unexpected result was that cycling seems to be disproportionately risky for young people in West Yorkshire compared with young people nationally. The case study raises the issue of potential negative health impacts of promoting cycling amongst vulnerable groups in dangerous areas. We conclude by highlighting opportunities for increasing cycling uptake via measures designed primarily to improve safety. The analysis underlying this research is reproducible, based on code stored atgithub.com/Robinlovelace/bikeR .