Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression
Abstract Objectives This paper describes the use of machine learning techniques to implement a Bayesian approach to modelling the dependency between offence data and environmental factors such as demographic characteristics and spatial location. The main goal of this paper is to provide a fully prob...
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2018-06-01
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| Series: | Security Informatics |
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| Online Access: | http://link.springer.com/article/10.1186/s13388-018-0030-x |
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doaj-art-3de768d8e53949e6a19c9d94b4b512502018-08-15T22:56:41ZengSpringerSecurity Informatics2190-85322018-06-017111910.1186/s13388-018-0030-xApplying machine learning to criminology: semi-parametric spatial-demographic Bayesian regressionRoman Marchant0Sebastian Haan1Garner Clancey2Sally Cripps3Centre for Translational Data Science, The University of SydneySydney Informatics Hub, The University of SydneySydney Institute of Criminology, The University of SydneyCentre for Translational Data Science, The University of SydneyAbstract Objectives This paper describes the use of machine learning techniques to implement a Bayesian approach to modelling the dependency between offence data and environmental factors such as demographic characteristics and spatial location. The main goal of this paper is to provide a fully probabilistic approach to modelling crime which reflects all uncertainties in the prediction of offences as well as the uncertainties surrounding model parameters. Methods The proposed method is based on a Bayesian framework, with a Gaussian Process prior and MCMC, allowing uncertainties in prediction and inference to be quantified via the posterior distributions of interest. By using Bayesian updating, these predictions and inferences are dynamic in the sense that they change as new information becomes available. Results We applied the proposed methodology to particular offence data, such as domestic violence-related assaults, burglary and motor vehicle theft, in the state of New South Wales (NSW), Australia. Our results demonstrate the strength of the technique by validating the factors that are associated with high and low criminal activity, including bounds on the degree of the relation. Conclusions We argue that this fully probabilistic approach will improve prediction, in the sense that the uncertainties are more accurately quantified, with attendant benefits to policymakers and policing organisations seeking to deploy limited criminal justice resources to prevent and control crime. While limitations and areas for potential improvement are identified, the success of the Bayesian approach, implemented using machine learning techniques, in a criminological context represents an exciting development.http://link.springer.com/article/10.1186/s13388-018-0030-xSemi-parametric regressionCrime ratesMachine learningBayesian methodsGaussian process |
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Open Data Bank |
| collection |
Open Access Journals |
| building |
Directory of Open Access Journals |
| language |
English |
| format |
Article |
| author |
Roman Marchant Sebastian Haan Garner Clancey Sally Cripps |
| spellingShingle |
Roman Marchant Sebastian Haan Garner Clancey Sally Cripps Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression Security Informatics Semi-parametric regression Crime rates Machine learning Bayesian methods Gaussian process |
| author_facet |
Roman Marchant Sebastian Haan Garner Clancey Sally Cripps |
| author_sort |
Roman Marchant |
| title |
Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression |
| title_short |
Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression |
| title_full |
Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression |
| title_fullStr |
Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression |
| title_full_unstemmed |
Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression |
| title_sort |
applying machine learning to criminology: semi-parametric spatial-demographic bayesian regression |
| publisher |
Springer |
| series |
Security Informatics |
| issn |
2190-8532 |
| publishDate |
2018-06-01 |
| description |
Abstract Objectives This paper describes the use of machine learning techniques to implement a Bayesian approach to modelling the dependency between offence data and environmental factors such as demographic characteristics and spatial location. The main goal of this paper is to provide a fully probabilistic approach to modelling crime which reflects all uncertainties in the prediction of offences as well as the uncertainties surrounding model parameters. Methods The proposed method is based on a Bayesian framework, with a Gaussian Process prior and MCMC, allowing uncertainties in prediction and inference to be quantified via the posterior distributions of interest. By using Bayesian updating, these predictions and inferences are dynamic in the sense that they change as new information becomes available. Results We applied the proposed methodology to particular offence data, such as domestic violence-related assaults, burglary and motor vehicle theft, in the state of New South Wales (NSW), Australia. Our results demonstrate the strength of the technique by validating the factors that are associated with high and low criminal activity, including bounds on the degree of the relation. Conclusions We argue that this fully probabilistic approach will improve prediction, in the sense that the uncertainties are more accurately quantified, with attendant benefits to policymakers and policing organisations seeking to deploy limited criminal justice resources to prevent and control crime. While limitations and areas for potential improvement are identified, the success of the Bayesian approach, implemented using machine learning techniques, in a criminological context represents an exciting development. |
| topic |
Semi-parametric regression Crime rates Machine learning Bayesian methods Gaussian process |
| url |
http://link.springer.com/article/10.1186/s13388-018-0030-x |
| _version_ |
1612702222875885568 |