Mapping Bicycle Crash Risk Patterns on the Local Scale

safety2016-screenshotEarlier this year we published a very detailed spatial (and temporal) analysis of bicycle crash data from Salzburg (Austria) in Transport Geography internet. In this paper we demonstrated the additional benefit of an explicit spatial perspective on crash reports. However, one of the major objections was, that meaningful conclusions from such an analysis can only be drawn when an exposure variable is introduced. This objection stems from the well established methodology of risk calculation in bicycle safety analysis (the quality of commonly used exposure variables is a whole different story as I’ve exemplified in an earlier post internet).

Because of the lack of sound exposure variables on the local scale – this is the scale I’m especially interested in – most bicycle risk analyses are done on a highly aggregated level. Last year we were, at least partly, successful in overcoming this shortcoming. With an agent-based simulation model (Wallentin & Loidl 2015 internet) we estimated the traffic flow for every road segment in an urban road network. This model allowed us to take the final step now: bicycle risk estimation on the local scale.

Theoretically we are able to calculate incident rates (commonly used synonymously with “risk”) for each and every road segment. However, thanks God, bicycle crashes are relatively rare; and officially reported ones are even rarer. Consequently the statistical robustness of calculated incident rates is weak, leading to analysis results that are potentially biased by random effects. Thus, we defined and investigated different spatial reference units, which served as spatial aggregation levels:

Choosing the adequate spatial reference unit is a trade-off between detail and reliability (statistical robustness). Shape and size (level of aggregation) of the spatial reference units are expected to impact the analysis results.

Choosing the adequate spatial reference unit is a trade-off between detail and reliability (statistical robustness). Shape and size (level of aggregation) of the spatial reference units are expected to impact the analysis results.

Whenever point incidents are spatially analyzed, two well-known and still challenging phenomena need to dealt with: spatial heterogeneity and the modifiable areal unit problem (MAUP).
Although the Geography literature on these two implications is full, they are hardly ever anticipated in (bicycle) crash analyses. We therefore regard our paper not only as a presentation of our analysis results, but also as an example for how to adequately deal with geo-located data.
Here is the abstract internet of the paper (full text internet), which was published in a special issue of the OA journal “Safety” internet:

Currently, mainly aggregated statistics are used for bicycle crash risk calculations. Thus, the understanding of spatial patterns at local scale levels remains vague. Using an agent-based flow model and a bicycle crash database covering 10 continuous years of observation allows us to calculate and map the crash risk on various spatial scales for the city of Salzburg (Austria). In doing so, we directly account for the spatial heterogeneity of crash occurrences. Additionally, we provide a measure for the statistical robustness on the level of single reference units and consider modifiable areal unit problem (MAUP) effects in our analysis. This study is the first of its kind. The results facilitate a better understanding of spatial patterns of bicycle crash rates on the local scale. This is especially important for cities that strive to improve the safety situation for bicyclists in order to address prevailing safety concerns that keep people from using the bicycle as a utilitarian mode of (urban) transport.

salzburg-small-multiples

Crash locations (left); Risk calculations for the whole city of Salzburg and census districts (right). Each risk map is supplemented with a map that shows the 95% confidence interval of the incident rates (= indicator for statistical robustness of results).

With this analysis we have successfully demonstrated that mapping bicycle risk patterns on the local scale reveals relevant information for policy makers and authorities, which aggregated approaches would not have been able to uncover. To our current knowledge this is the first study, which calculates crash rates on the local scale. However, with the increasing amount of available data and improved (spatial) models, we are quite sure that many more analyses like this one will follow – for the good of bicyclists and building blocks for evidence-based safety strategies.

As the number of geographers dealing with bicycle safety and crash analysis is rather small, I’d be more than happy to read from you. Do you have any questions, ideas for further studies, data or just a comment – feel free to leave your note below, connect on Twitter internet or get in touch with me via the contact form.

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