From Links to Meaning: A Burglary Data Case Study

Full text for this resource is not available from the Research Repository.

Oatley, Giles, Zeleznikow, John, Leary, Richard and Ewart, Brian (2005) From Links to Meaning: A Burglary Data Case Study. In: Knowledge-based intelligent information and engineering systems: 9th international conference, KES 2005, Melbourne, Australia, September 14-16, 2005 : proceedings. Khosla, Rajiv, Howlett, Robert J and Jain, Lakhmi C, eds. Lecture Notes in Computer Science ; 0302-9743 (3684). Springer, Berlin, Germany, pp. 813-822.

Abstract

Our central aim is the development of decision support systems for purposes such 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, examining the operational use of networks and the metric of brokerage from the social network analysis literature. Our work builds upon several years of experimentation using forensic psychology guided exploratory techniques from artificial intelligence, statistics and spatial statistics.

Dimensions Badge

Altmetric Badge

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/10651
DOI https://doi.org/10.1007/11554028_114
Official URL http://link.springer.com/chapter/10.1007%2F1155402...
ISBN 3540288978 (print) 9783540319979 (online)
Subjects Current > FOR Classification > 0801 Artificial Intelligence and Image Processing
Current > FOR Classification > 0803 Computer Software
Current > FOR Classification > 0806 Information Systems
Current > FOR Classification > 1801 Law
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Keywords ResPubID8597, ResPubID12235, criminal networks, brokerage metric, burglary arrest, PAJEK software, spatial density representation, FLINTS, Forensic Led Intelligence System, Advanced Information Technologies, Link discovery tool
Citations in Scopus 5 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login