Work

Spatio-temporal modelling of crime using low discrepancy sequences

Year: 2016

Type: conference-paper

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.

Source: Research Commons (University of Waikato)

Authors: Paul Brown, Chaitanya Joshi, Stephen Joe, Nigel H. McCarter

Language: English

Topic: Crime Patterns and Interventions

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: green

OpenAlex ID: W2557780859

FWCI:

Cites: 3

Cited by:

Related to: 10