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Crime topic modeling
Crime Science ( IF 3.1 ) Pub Date : 2017-12-01 , DOI: 10.1186/s40163-017-0074-0
Da Kuang , P. Jeffrey Brantingham , Andrea L. Bertozzi

The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these details cannot be captured by singular crime type labels. This information loss impacts our ability to not only understand the causes of crime, but also how to develop optimal crime prevention strategies. We apply machine learning methods to short narrative text descriptions accompanying crime records with the goal of discovering ecologically more meaningful latent crime classes. We term these latent classes ‘crime topics’ in reference to text-based topic modeling methods that produce them. We use topic distributions to measure clustering among formally recognized crime types. Crime topics replicate broad distinctions between violent and property crime, but also reveal nuances linked to target characteristics, situational conditions and the tools and methods of attack. Formal crime types are not discrete in topic space. Rather, crime types are distributed across a range of crime topics. Similarly, individual crime topics are distributed across a range of formal crime types. Key ecological groups include identity theft, shoplifting, burglary and theft, car crimes and vandalism, criminal threats and confidence crimes, and violent crimes. Though not a replacement for formal legal crime classifications, crime topics provide a unique window into the heterogeneous causal processes underlying crime.

中文翻译:

犯罪主题建模

将犯罪分为离散类别会导致大量信息丢失。犯罪是由行为和情况的复杂组合产生的,但是这些细节中的大多数无法通过单一的犯罪类型标签来捕获。这种信息丢失不仅影响我们了解犯罪原因的能力,而且还会影响如何制定最佳的犯罪预防策略。我们将机器学习方法应用于伴随犯罪记录的简短叙事文字描述中,以发现生态上更有意义的潜在犯罪类别。我们将这些潜在类称为“犯罪主题”,是指产生它们的基于文本的主题建模方法。我们使用主题分布来衡量正式认可的犯罪类型之间的聚类。犯罪主题复制了暴力犯罪和财产犯罪之间的广泛区别,而且还揭示了与目标特征,情境条件以及攻击工具和方法有关的细微差别。正式犯罪类型在主题空间中不是离散的。相反,犯罪类型分布在一系列犯罪主题中。同样,个人犯罪主题分布在一系列正式犯罪类型中。主要的生态群体包括身份盗窃,入店行窃,盗窃和盗窃,汽车犯罪和故意破坏,刑事威胁和信任犯罪以及暴力犯罪。虽然不能代替正式的法律犯罪分类,但犯罪主题为了解犯罪背后的多种因果过程提供了一个独特的窗口。犯罪类型分布在一系列犯罪主题中。同样,个人犯罪主题分布在一系列正式犯罪类型中。主要的生态群体包括身份盗窃,入店行窃,盗窃和盗窃,汽车犯罪和故意破坏,刑事威胁和信任犯罪以及暴力犯罪。虽然不能代替正式的法律犯罪分类,但犯罪主题为了解犯罪背后的多种因果过程提供了一个独特的窗口。犯罪类型分布在一系列犯罪主题中。同样,个人犯罪主题分布在一系列正式犯罪类型中。主要的生态群体包括身份盗窃,入店行窃,盗窃和盗窃,汽车犯罪和故意破坏,刑事威胁和信任犯罪以及暴力犯罪。虽然不能代替正式的法律犯罪分类,但犯罪主题为了解犯罪背后的多种因果过程提供了独特的窗口。
更新日期:2017-12-01
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