Mining co-distribution patterns for large crime datasets
Phillips, Peter, and Lee, Ickjai (2012) Mining co-distribution patterns for large crime datasets. Expert Systems with Applications, 39 (14). pp. 11556-11563.
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Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. We analyze crime datasets in conjunction with socio-economic and socio-demographic factors to discover co-distribution patterns that may contribute to the formulation of crime. We propose a graph based dataset representation that allows us to extract patterns from heterogeneous areal aggregated datasets and visualize the resulting patterns efficiently. We demonstrate our approach with real crime datasets and provide a comparison with other techniques.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||co-distribution; areal aggregated data; crime data mining; correlation|
|Date Deposited:||11 Sep 2012 05:50|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 50%
89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 50%
|Citation Count from Web of Science||