  In the aim to support London's safer recovery from the pandemic by improving
road safety intelligently, this study investigated the spatiotemporal patterns
of age-involved car crashes and affecting factors, upon answering two main
research questions: (1)"What are the spatial and temporal patterns of car
crashes as well as their changes in two typical years, 2019 and 2020, in
London, and how the influential factors work?"; (2)"What are the spatiotemporal
patterns of casualty by age groups, and how people's daily activities affect
the patterns pre- and para- the pandemic"? Three approaches, i.e., spatial
analysis (network Kernel Density Estimation, NetKDE), factor analysis, and
spatiotemporal data mining (tensor decomposition), had been implemented to
identify the temporal patterns of car crashes on weekly and daily basis
respectively, detect the crashes' hot spots, and to gain better understanding
the effect from citizens' daily activity on crashes' patterns pre- and para-
the pandemic. It had been found from the study that car crashes mainly
clustered in the central part of London, especially busier areas around denser
hubs of point-of-interest (POIs); the POIs, as a reflector for citizens' daily
activities and travel behaviours, can be of help to gain a better understanding
of the crashes' patterns, upon further assessment on interactions through the
geographical detector; the crashes' casualty patterns varied by age group, with
distinctive relationships between POIs and crashes' pattern for corresponding
age group categorised. In all, the paper provided an in-depth exploratory
analysis of car crashes and their casualty patterns in London to facilitate
deployment policies towards post-pandemic safer recovery upon COVID-19.
