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点模式条件下的犯罪嫌疑人时空同现模式挖掘与分析
Mining and Analyzing Spatiotemporal Co-occurrence Patterns among Criminal Suspects under Point Pattern

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李智   李卫红 *  
文摘 现有时空同现模式挖掘方法因其在空间和时间频繁阈值等参数值的设定上存在困难且缺乏客观依据的问题而难以被应用到犯罪地理研究中。为此,本文通过引入时空状态同现模式和最小时空参与率等概念对现有挖掘方法进行了重新建模,并结合广义Grubbs异常值检验提出了一种点模式分布下的犯罪嫌疑人时空同现模式挖掘框架。基于该框架对中国某省部分犯罪嫌疑人的真实移动轨迹数据的实验分析结果表明,本文所提出的方法能够有效地挖掘出嫌疑人间显著的时空同现模式,且这些模式的时空分布特征不仅与犯罪活动易发生在非农业生产区这一共识基本相符,还与日常活动理论的基本观点相适应。本文拓展了时空同现模式挖掘在犯罪地理研究中的应用,研究成果对公安机关等执法部门在重点监控某些犯罪嫌疑人以及合理分配和部署警力资源方面具有重要意义。
其他语种文摘 Spatiotemporal co-occurrence patterns represent subsets of different object-types whose instances are frequently located together in both space and time. Using movement data to mine and analyze spatiotemporal co-occurrence patterns among diverse criminal suspects not only can help us better understand those unusual moving behaviors and relationships of them, but also provide decision-making supports for police departments in key suspects monitoring or arresting. Therefore, such pattern is one of the most important and useful way for the geography of crime researchers and police officers to extract and comprehend the implicit knowledge in large police databases which hold a large amount of crime data with spatiotemporal information. Additionally, to some extent, mining spatiotemporal co-occurrence patterns can also assist the police departments to save the limited police resources and improve their efficiency of handling criminal cases. However, current methods for mining spatiotemporal co-occurrence patterns can hardly be applied to the geography of crime studies directly because the way of determining spatial and temporal prevalence thresholds is presently difficult and lack of objectivity. Thus, in this paper, a novel candidate spatiotemporal co-occurrence pattern mining model was first built based on the spatiotemporal status co-occurrence pattern and the minimum spatiotemporal participation rate. Then, a framework for mining spatiotemporal co-occurrence patterns among criminal suspects under the point distribution was given through combining our proposed model and generalized ESD test. Finally, based on the proposed framework, a real case study in a province of China was conducted with an amount of real trajectory data of two criminal type (fraud and theft). The result shows that our proposed method is feasible in mining and analyzing the spatiotemporal co-occurrence patterns among criminal suspects. Specifically, 219 candidate spatiotemporal co-occurrence patterns were discovered under the condition that spatial neighbor distance equals to 688 meters and temporal neighbor distance equals to 504 seconds, and 6 of them were identified as the spatiotemporal co-occurrence patterns under the condition that significance level equals to 0.05. Importantly, the spatiotemporal distributions of those detected spatiotemporal co-occurrence patterns are not only approximately consistent with the common sense that criminal activities are more common in non-agricultural production areas, but also conform to the basic viewpoints of routine activity theory. This research expands the application of spatiotemporal co-occurrence pattern mining method to the geography of crime studies, and the study result can play an important role for police departments in key suspects monitoring and police resources allocation and deployment.
来源 地球信息科学学报 ,2018,20(6):827-836 【核心库】
DOI 10.12082/dqxxkx.2018.180009
关键词 犯罪嫌疑人 ; 移动轨迹 ; 时空同现 ; 异常检验 ; 时空分布
地址

华南师范大学地理科学学院, 广州, 510631

语种 中文
文献类型 研究性论文
ISSN 1560-8999
学科 自动化技术、计算机技术
基金 公安部科技强警基础工作专项项目
文献收藏号 CSCD:6258648

参考文献 共 31 共2页

1.  刘大有. 时空数据挖掘研究进展. 计算机研究与发展,2013,50(2):225-239 CSCD被引 60 次    
2.  Celik M. Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Transactions on Knowledge and Data Engineering,2008,20(10):1322-1335 CSCD被引 5 次    
3.  Wang J. A framework for mining topological patterns in spatio-temporal databases. Proceedings of the 14th ACM International Conference on Information and Knowledge Management,2005:429-436 CSCD被引 2 次    
4.  Yoo J S. Discovery of co-evolving spatial event sets. Sixth SIAM International Conference on Data Mining,2006:306-315 CSCD被引 1 次    
5.  Celik M. Partial spatio-temporal co-occurrence pattern mining. Knowledge and Information Systems,2015,44(1):27-49 CSCD被引 4 次    
6.  Qian F. Mining spread patterns of spatio-temporal co-occurrences over zones. International Conference on Computational Science and Its Applications, ICCSA 2009,2009:677-692 CSCD被引 1 次    
7.  Zhang Z. Composite spatio-temporal co-occurrence pattern mining. 3rd International Conference on Wireless Algorithms, Systems, and Applications, WASA 2008,2008:454-465 CSCD被引 1 次    
8.  Mohan P. Cascading spatio-temporal pattern discovery. IEEE Transactions on Knowledge and Data Engineering,2012,24(11):1977-1992 CSCD被引 7 次    
9.  Qian F. Mining spatio-temporal co-location patterns with weighted sliding window. 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009,2009:181-185 CSCD被引 1 次    
10.  Huo J. On co-occurrence pattern discovery from spatio-temporal event stream. 14th International Conference on Web Information Systems Engineering, WISE 2013,2013:385-395 CSCD被引 1 次    
11.  Pillai K G. Spatio-temporal co-occurrence pattern mining in data sets with evolving regions. 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012,2012:805-812 CSCD被引 1 次    
12.  Akbari M. A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution. Journal of Geographical Systems,2015,17(3):249-274 CSCD被引 12 次    
13.  Shekhar S. Discovering spatial co-location patterns: a summary of results. 7th International Symposium on Spatial and Temporal Databases, SSTD 2001,2001:236-256 CSCD被引 1 次    
14.  Huang Y. Discovering colocation patterns from spatial data sets: A general approach. IEEE Transactions on Knowledge & Data Engineering,2004,16(12):1472-1485 CSCD被引 60 次    
15.  Miller D. Optics of the normal eye. Ophthalmology. 3rd ed,2008:54 CSCD被引 1 次    
16.  Bergman R T. Cephalometric soft tissue facial analysis. American Journal of Orthodontics and Dentofacial Orthopedics,1999,116(4):373-389 CSCD被引 9 次    
17.  肖惠. 中国成年人头面部尺寸的研究. 人类工效学,1998,4(4):25-30 CSCD被引 4 次    
18.  Bohannon R W. Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants. Age and ageing,1997,26(1):15-19 CSCD被引 11 次    
19.  Aggarwal C C. Outlier analysis,2013 CSCD被引 7 次    
20.  Tietjen G L. Some Grubbs-type statistics for the detection of several outliers. Technometrics,1972,14(3):583-597 CSCD被引 4 次    
引证文献 6 篇

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