This paper presents amethodology to identify high density accident
hotspots and in turn create a clustering technique which determines
casual indicators more likely to be present at certain clusters,
therefore being able tocompare like with like across time and space.
The kernel density estimation tool enabled an overarching visualisation
and manipulation of the accidents based on density which
was used in turn to create the basic spatial unit for the hotspot clustering
method. The classification of road accident hotspots in road
safety still remains an important and yet under developed theme.
These typologies provide a snapshot of the processes which are
occurring at these sites and the people upon whom they impact.
This information can lead road safety professionals to a better
understanding, not only of the types of hotspots but their patterns
across London. There are some evident potential policy implications
for certain clusters. For example, C10 highlights the need for safety
of cyclists in central London, whether this is the mandatory use of
cycle helmets, better cycle lane provision or better cyclist/driver
education. One of the most important recommendations which
reflects the current local governmental policy is the focus on community
and neighbourhood. By drilling down to specific accident
clusters in specific areas, allows for a greater neighbourhood participation
in understanding people’ road user risk.