A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level 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. Neighborhood level approaches to modeling 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 and forecasting public confidence at the neighborhood 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 investigate 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 spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London‐wide, Borough, and neighborhood level