We present a novel exploratory application of unsupervised machine-learning methods to identify clusters of specific crime problems from unstructured modus operandi free-text data within a single administrative crime classification. To illustrate our proposed approach, we analyse police recorded free-text narrative descriptions of residential burglaries occurring over a two-year period in a major metropolitan area of the UK. Results of our analyses demonstrate that topic modelling algorithms are capable of clustering substantively different burglary problems without prior knowledge of such groupings. Subsequently, we describe a prototype dashboard that allows replication of our analytical workflow and could be applied to support operational decision making in the identification of specific crime problems. This approach to grouping distinct types of offences within existing offence categories, we argue, has the potential to support crime analysts in proactively analysing large volumes of modus operandi free-text data—with the ultimate aims of developing a greater understanding of crime problems and supporting the design of tailored crime reduction interventions.

中文翻译：

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从警察自由文本数据中无监督地识别犯罪问题

我们提出了一种无监督机器学习方法的新颖探索性应用，可以从单一行政犯罪分类中的非结构化方式操作自由文本数据中识别特定犯罪问题的集群。为了说明我们提出的方法，我们分析了警察记录的在英国主要大都市地区两年内发生的住宅盗窃案的自由文本叙述性描述。我们的分析结果表明，主题建模算法能够对本质上不同的盗窃问题进行聚类，而无需事先了解此类分组。随后，我们描述了一个原型仪表板，该仪表板可以复制我们的分析工作流程，并可以用于支持在确定特定犯罪问题时进行业务决策。