This paper introduced the spatio-temporal association rules based on clustering minging to find out the spatio-temporal crime patterns of bus pickpocketing. It can be carried out through three steps. Firstly, extract time, places and other information from the case information by text extraction. Then, confirm the boarding stations and getting off stations of victims using the geocoding service and POI search capability of Amap API. Divide the bus routes into sections according to the bus stops and merge the crime time into time interval. Thirdly, the analysis of association rules based on clustering is carried out to discover the patterns of bus pickpocketing. The results prove that the proposed mining model has the following characteristics: ①This method can reduce the database scanning times, the candidate item sets amount and improve time efficiency of the searching. ②After clustering, the data in a cluster is similar and the characteristics are more obvious. On this basis, the association rules of high confidence are extracted. ③When the analysis was carried out, the temporal and spatial characteristics of the bus pickpocketing crime were also considered.
YAN Mi-qiao
,
GUO Zhong-yang
,
REN Zhe-hao
. Spatio-temporal analysis of bus pickpocketing using association rules based on clustering[J]. Journal of East China Normal University(Natural Science), 2017
, (3)
: 145
-152
.
DOI: 10.3969/j.issn.1000-5641.2017.03.016
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