基于加权时空关联规则的公交扒窃犯罪模式识别
作者简介:叶文菁(1987-),女,福建长泰人,硕士生,研究方向为时空数据挖掘。E-mail:yewenjing06@gmail.com
收稿日期: 2014-02-10
要求修回日期: 2014-03-20
网络出版日期: 2014-07-10
基金资助
国家“863”重大项目课题(2012AA12A208)
Identifying Crime Patterns of Bus Pickpocketing Using Weighted Spatio-Temporal Association Rules Mining
Received date: 2014-02-10
Request revised date: 2014-03-20
Online published: 2014-07-10
Copyright
近年来,公交扒窃案呈上升趋势,为了预防和打击此类犯罪,需要有效识别其犯罪模式。传统的犯罪分析方法,往往将时间和空间分割开来研究,本文则引入加权时空关联规则进行挖掘分析,试图找出公交扒窃的案发时空规律。首先,对公交扒窃数据进行时间粒度和空间粒度的划分,将公交主要运营时间以2 h为单位划分成等间隔的公交时段并对其进行编码,将公交线路按公交站点划分成公交路段;其次,对数据进行空间分析和时间归并,提取出每个案件发生的公交路段和案发时段,并将案发时段归并到公交时段中;再次,由于每个公交路段的案发率不同,其对结果的贡献率也不同,因此,给每个路段赋予一个权重;最后,用Apriori算法进行加权关联规则挖掘,得到公交扒窃的时空犯罪模式。研究表明,这种挖掘方法具有以下特点:(1)按公交站点进行公交路段的划分具有创新性;(2)通过对案发路段的加权,能将空间位置重要程度的差异区分开来,更符合实际情况;(3)挖掘过程中同时考虑了时间与空间属性。
叶文菁 , 吴升* . 基于加权时空关联规则的公交扒窃犯罪模式识别[J]. 地球信息科学学报, 2014 , 16(4) : 537 -544 . DOI: 10.3724/SP.J.1047.2014.00537
In recent years, the number of bus pickpocketing is on the rise, which has caused great danger to the public. In order to prevent and combat bus pickpocketing, we need to effectively identify the crime patterns. However, since bus pickpocketing may occur when the bus is moving, it is difficult to define a location and a crime time for it, and the traditional methods of crime analysis often focus on spatial or temporal properties of crimes separately. Therefore, this paper introduced the weighted spatio-temporal association rules mining to find out the spatio-temporal crime patterns of bus pickpocketing. It can be carried out through four steps. Firstly, conduct the temporal and spatial granularity division: divide the main bus running time into equal units of 2h and encode these units, which are entitled by “bus time”. Then, divide the bus routes into sections according to the bus stops, and entitle these sections by “bus section”. Secondly, conduct the spatial analysis and time merge process: extract and gather bus sections and crime time information for pickpocketing cases, and merge the crime time with “bus time”. Thirdly, as the crime rate of each “bus section” varies, their contributions to the result will also be different. Therefore, this paper assigned each “bus section” a weight according to their crime rates, based on the assumption that every “bus section” of a crime case has the same crime rate. Finally, conduct the weighted spatio-temporal association rules mining: use Apriori algorithm to find out the spatio-temporal crime patterns of bus pickpocketing. The results prove that the proposed mining model has the following characteristics: (1) it is innovative to extract bus sections according to the bus stops; (2) It is more realistic to assign a weight to each “bus section” to distinguish the importance of spatial location; (3) The crime patterns show the correspondence between the bus sections and “bus time” of high crime rates. Further work would focus on the efficiency of Apriori algorithm, the evaluation of association rules, the weight settings (consolidating other factors) and the result visualization.
Fig.1 Schematic of pickpocketing on buses图1 公交扒窃案示意图 |
Tab.1 The codes of bus time segments表1 公交时段分段编码 |
时间段 | 编码 |
---|---|
[07:00,09:00) | 1 |
[09:00,11:00) | 2 |
[11:00,13:00) | 3 |
[13:00,15:00) | 4 |
[15:00,17:00) | 5 |
[17:00,19:00) | 6 |
[19:00,21:00) | 7 |
[21:00,23:00] | 8 |
Fig.2 The merging of crime time with bus time图2 案发时段与公交时段的归并 |
Fig.3 Schematic of weighted bus sections图3 公交路段权重示意图 |
Fig.4 Work flow of spatio-temporal association rules mining in bus pickpocketing图4 案件加权时空关联规则挖掘技术路线图 |
Tab.2 Spatio-temporal data of bus pickpocketing表2 案件时空数据表 |
Tid | 公交路段 | 公交时段编码 | 权重 |
---|---|---|---|
1 | 新店-浮村,浮村-湖前,湖前-灰泸头,灰泸头-龙腰 | 7 | 0.25 |
2 | 洋下新村-湖塍,湖塍-斗门(华林路) | 4 | 0.5 |
3 | 宝龙城市广场-祥坂路口 | 5 | 1 |
Tab.3 Interesting spatio-temporal association rules表3 强关联规则 |
项集 | 加权支持度(%) | 加权置信度(%) | ||
---|---|---|---|---|
1 | 宝龙城市广场-祥坂路口5 | 2.34 | 50.1 | |
2 | 宝龙城市广场-祥坂路口3 | 2.33 | 49.9 | |
3 | 湖塍-斗门4 | 1.50 | 100 | |
4 | 中洲岛-中亭街5 | 1.25 | 93.8 |
The authors have declared that no competing interests exist.
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