Urban crime analysis through areal categorized multivariate association mining

Lee, Ickjai, and Phillips, Peter (2008) Urban crime analysis through areal categorized multivariate association mining. Applied Artificial Intelligence, 22 (5). pp. 483-499.

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Abstract

As geospatial data grows explosively, there is a great demand for the incorporation of data mining techniques into a geospatial context. Association rules mining is a core technique in data mining and is a solid candidate for the associative analysis of large geospatial databases. In this article, we propose a geospatial knowledge discovery framework for automating the detection of multivariate associations based on a given areal base map. We investigate a series of geospatial preprocessing steps involving data conversion and classification so that the traditional Boolean and quantitative association rules mining can be applied. Our framework has been integrated into GISs using a dynamic link library to allow the automation of both the preprocessing and data mining phases to provide greater ease of use for users. Experiments with real-crime datasets quickly reveal interesting frequent patterns and multivariate associations, which demonstrate the robustness and efficiency of our approach.

Item ID: 5771
Item Type: Article (Refereed Research - C1)
Keywords: crime analysis; association mining; data mining
ISSN: 1087-6545
Date Deposited: 22 Dec 2009 00:53
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080604 Database Management @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified @ 50%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 70%
89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 30%
Citation Count from Web of Science Web of Science 6
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