Mining co-distribution patterns for large crime datasets

被引：31

作者：

Phillips, Peter ^{[2
]}

Lee, Ickjai ^{[1
]}

机构：

[1] James Cook Univ, Sch Business IT, Cairns, Qld 4870, Australia

[2] James Cook Univ, Sch Business IT, Townsville, Qld 4811, Australia

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 14期

关键词：

Co-distribution; Areal aggregated data; Crime data mining; Correlation; SPATIAL DATA SETS; URBAN-CRIME; INFORMATION; DATABASES;

D O I：

10.1016/j.eswa.2012.03.071

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：11556 / 11563

页数：8

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