Mining co-distribution patterns for large crime datasets

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• 作者：Peter Phillips^a ; ^{Peter.Phillips@jcu.edu.au} ; Ickjai Lee^b ; ^{Ickjai.Lee@jcu.edu.au}
• 关键词：Co-distribution ; Areal aggregated data ; Crime data mining ; Correlation
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：15 October, 2012
• 卷：39
• 期：14
• 页码：11556-11563
• 文件大小：2206 K

摘要

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.