Spatio-temporal modelling of crime using low discrepancy sequences

Brown, Paul T.; Joshi, Chaitanya; Joe, Stephen; McCarter, Nigel

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dupuy.perso.math.cnrs.fr

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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: https://hdl.handle.net/10289/10770

Abstract

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.

Date

2016

Type

Conference Contribution

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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).

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Computing and Mathematical Sciences Papers [1431]