从警方自由文本数据中无监督识别犯罪问题

IF 3.1 Q1 CRIMINOLOGY & PENOLOGY
Daniel Birks, Alex Coleman, David Jackson
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引用次数: 0

摘要

我们介绍了一种新颖的无监督机器学习方法的探索性应用,该方法可从单一行政犯罪分类中的非结构化作案手法自由文本数据中识别出特定犯罪问题群组。为了说明我们提出的方法,我们分析了警方记录的两年内发生在英国一个大都市地区的住宅盗窃案的自由文本叙述描述。我们的分析结果表明,主题建模算法能够在不事先了解此类分组的情况下对实质性不同的入室盗窃问题进行聚类。随后,我们介绍了一个仪表板原型,该仪表板可以复制我们的分析工作流程,并可用于支持识别特定犯罪问题的业务决策。我们认为,这种在现有犯罪类别中对不同类型的犯罪进行分组的方法有可能支持犯罪分析人员主动分析大量的作案手法自由文本数据,其最终目的是加深对犯罪问题的了解,并支持设计有针对性的减少犯罪干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised identification of crime problems from police free-text data
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.
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来源期刊
Crime Science
Crime Science Social Sciences-Cultural Studies
CiteScore
11.90
自引率
8.20%
发文量
12
审稿时长
13 weeks
期刊介绍: Crime Science is an international, interdisciplinary, peer-reviewed journal with an applied focus. The journal''s main focus is on research articles and systematic reviews that reflect the growing cooperation among a variety of fields, including environmental criminology, economics, engineering, geography, public health, psychology, statistics and urban planning, on improving the detection, prevention and understanding of crime and disorder. Crime Science will publish theoretical articles that are relevant to the field, for example, approaches that integrate theories from different disciplines. The goal of the journal is to broaden the scientific base for the understanding, analysis and control of crime and disorder. It is aimed at researchers, practitioners and policy-makers with an interest in crime reduction. It will also publish short contributions on timely topics including crime patterns, technological advances for detection and prevention, and analytical techniques, and on the crime reduction applications of research from a wide range of fields. Crime Science publishes research articles, systematic reviews, short contributions and theoretical articles. While Crime Science uses the APA reference style, the journal welcomes submissions using alternative reference styles on a case-by-case basis.
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