TY  - JOUR
T1  - Introduction of STEM: Space-Time-Event Model for Crime Pattern Analysis
AU - , Kelvin Leong AU - , Stephen Chan AU - , Vincent Ng AU - , Simon Shiu 
JO  - Asian Journal of Information Technology
VL  - 7
IS  - 12
SP  - 516
EP  - 523
PY  - 2008
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2008.516.523
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2008.516.523
KW  - Spatial-temporal data mining
KW  -crime analysis
KW  -data mining
KW  -knowledge discovery
KW  -association rule
KW  -clustering
AB  - 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.
ER  -