Public confidence in the police is crucial to effective policing. Improving understanding of
public confidence at the local l
evel will better enable the police to conduct proactive
confidence interventions to meet the concerns of local communities.
Conventional
approaches
do
not consider that public confidence varies across geographic space as well
as in time.
Neighbourhood leve
l approaches to modelling public confidence in the police
are hampered by the small number problem and the resulting instability in the estimates
and uncertainty in the results.
This
research illustrates a spatiotemporal Bayesian
approach for estimating an
d forecasting
public confidence at the
neighbourhood level
and
we use it to examine trends in public confidence in the police in London, UK, for Q2 2006
to Q3 2013. Our approach
overcomes the limitations of the small number problem
and
specifically
, we inv
estigate the effect of the spatiotemporal representation structure
chosen
on the
estimates
of public confidence produced. We then investigate the use of the model
for forecasting by producing one
-
step ahead forecasts
of
the final third of the time
-
series
.
The results are compared with
the forecasts from traditional time
-
series forecasting
methods like naïve, exponential smoothing, ARIMA, STARIMA and others. A model with
spatially structured and unstructured random effects as well as a normally distributed
s
patiotemporal interaction term was the most parsimonious and produced the most
realistic estimates.
It also
provided the best forecasts at the London
-
wide, Borough and
neighbourhood level.