A spatiotemporal bayesian hierarchical approach to investigating patterns of confidence in the police at the neighbourhood level - Williams,D; Haworth,J; Blangiardo,MAG; Cheng,T (Geographical Analysis, 2019)
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 approachesdonot consider that public confidence varies across geographic space as well as in time.Neighbourhood level 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 and forecastingpublic confidence at theneighbourhood leveland 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 problemand specifically, we investigate the effect of the spatiotemporal representation structurechosenon theestimatesof public confidence produced. We then investigate the use of the model for forecasting by producing one-step ahead forecasts ofthe 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 alsoprovided the best forecasts at the London-wide, Borough and neighbourhood level.