Brown, P. T., Joshi, C., Joe, S., & McCarter, N. (2016). Spatio-temporal modelling of crime using low discrepancy sequences. In Proceedings of the 31st International Workshop on Statistical Modelling Volume II, 4–8 July 2016, Rennes, France.
Permanent Research Commons link: http://hdl.handle.net/10289/10770
We perform spatio-temporal modelling of burglary data in order to predict areas of high criminal risk for local authorities. We wish to account for several spatio-temporal factors as latent processes to make the model as realistic as possible, thus creating a model with a large latent eld with several hyperparameters. Analysis of the model is done using Integrated Nested Laplace Approximations (INLA) (Rue et al. 2009), a fast Bayesian inference methodology that provides more computationally efficient estimations than Markov Chain Monte Carlo (MCMC) methods.
This paper was published as a part of the proceedings of the 31st International Workshop on Statistical Modelling, INSA Rennes, 4–8 July 2016. The copyright remains with the author(s).