[1]段炼,党兰学,李铭,等.位置数据稀疏约束下的疑犯时空位置预测[J].郑州大学学报(工学版),2018,39(05):58-62.
 Spatiotemporal Prediction of Suspect under Location Data Sparsity Constraint[J].Journal of Zhengzhou University (Engineering Science),2018,39(05):58-62.
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位置数据稀疏约束下的疑犯时空位置预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年05期
页码:
58-62
栏目:
出版日期:
2018-08-21

文章信息/Info

Title:
Spatiotemporal Prediction of Suspect under Location Data Sparsity Constraint
作者:
段炼党兰学李铭高超朱欣焰
文献标志码:
A
摘要:
低强度的社会活动监控方式,使警方难以准确掌握疑犯的社会时空移动模式,也限制了嫌疑人排查及拦截围堵等警务行动开展的有效性。为此,本文基于张量联合分解位置(Tensor Collective Decomposition Location Prediction , TCDLP)模型,在疑犯时空位置数据的稀疏约束下,估算疑犯个体在任意时段的空间分布概率。该方法利用三维张量表达各疑犯在多个时空节点上的访问强度,基于张量分解算法,融合多源社会环境数据所刻画的区域间关联性,解算出该张量中的缺失值,进而获取各疑犯的潜在时空分布模式。实验采用包含了241个疑犯、约1.9w个位置记录的真实疑犯位置数据集进行了模型测试,结果表明本文方法优于其他位置预测方法。
Abstract:
Due to the low monitoring intensities on key tracking persons(suspects), the police suffered from the very small amounts of suspect social location data, which was hard to effectively reveal the social mobility patterns of suspects, and restrict the police action validity for suspects filtering and crime blockading etc. Facing this data sparsity challenge, a novel Tensor Collective Decomposition Location Prediction (TCDLP) MODEL WAS PROPOSED, to estimate the latent visiting intensity at an arbitrary spatiotemporal node. Specifically, it modeled the visiting intensities of suspects with 3D tensor, where the three dimensions stood for suspects, locations, and time slots respectively. Then, the missing entries in the tensor would be filled through a multi-data fusion tensor decomposition approach, which integrates the correlations of locations and suspects relying on multiple social environment data. So by supplementing the visiting intensities in this tensor, the social spatiotemporal distribution pattern for each suspect could uncovered.TCDLP was evalvated by using a real-world suspect dataset collected form 241 suspects over 6 months with about 19 thousands location records, showing our model outperformed ststed-of -the -art approaches to the problem..
更新日期/Last Update: 2018-08-22