Matching and Predicting Crimes
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Oatley, Giles, Zeleznikow, John 
ORCID: 0000-0002-8786-2644 and Ewart, Brian
(2004)
Matching and Predicting Crimes.
In:
Applications and Innovations in Intelligent Systems XII. Proceedings of AI2004 The Twenty-fourth SGAI International Conference on Knowledge Based Systems and Applications of Artificial Intelligence.
Macintosh, Ann, Ellis, Richard and Allen, T, eds.
Springer, London, pp. 19-32.
Abstract
Our central aim is the development of decision support systems based on appropriate technology for such purposes as profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents research in this area for the high-volume crime of Burglary Dwelling House, with examples taken from the authors' own work a United Kingdom police force. Discussion and experimentation include exploratory techniques from spatial statistics and forensic psychology. The crime matching techniques used are case-based reasoning, logic progranmiing and ontologies, and naive Bayes augmented with spatio-temporal features. The crime prediction techniques are survival analysis and Bayesian networks.
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| Item type | Book Section |
| URI | https://vuir.vu.edu.au/id/eprint/10647 |
| DOI | 10.1007/1-84628-103-2_2 |
| Official URL | http://link.springer.com/content/pdf/10.1007%2F1-8... |
| ISBN | 9781852339081 (print) 9781846281037 (online) |
| Subjects | Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing Historical > FOR Classification > 0803 Computer Software Historical > FOR Classification > 0806 Information Systems Historical > FOR Classification > 1801 Law Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems |
| Keywords | ResPubID6871, geographical information system, GIS, crimes with similarity-based retrieval, CBR, k-nearest neighbour, KNN, Tversky's contrast model, TC, modified-cosine rule, MCR, Bayesian belief network, survival/failure time analysis, logic programming |
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