Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19655
Title: Introduction of STEM : Space-Time-Event Model for crime pattern analysis
Authors: Leong, K
Chan, S 
Ng, V 
Shiu, S 
Keywords: Spatial-temporal data mining
Crime analysis
Data mining
Knowledge discovery
Association rule
Clustering
Issue Date: 2008
Source: Asian journal of information technology, 2008, v. 7, no. 12, p. 516-523 How to cite?
Journal: Asian journal of information technology 
Abstract: Successful law enforcement depends upon information availability. In criminal knowledge discovery, many techniques have been developed for analysis, mapping, modeling and prediction. However, most approaches treat the spatial and temporal aspects of crime as distinct entities, thus, ignoring the necessary interaction of space and time to produce criminal opportunities. In this study, a new crime pattern analysis model, STEM (Space-Time-Event Model) is presented. The new model allows users to investigate the spatio-temporal patterns of events. We also discuss relevant crime theories and related data mining methods. Two experiments were conduced to test the model. Using STEM, we found strong correlations between holidays and crime clusters. On the other hand, we could not find obvious seasonal dependency, at least in our test data set. These findings are corroborated by related empirical crime studies.
URI: http://hdl.handle.net/10397/19655
ISSN: 1682-3915
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