Modelagem de tópicos criminais usando Machine Learning

Com o aumento da violência no nosso país (em que temos mais de 60 mil assassinatos por ano) é de fundamental importância que todas as secretarias e demais departamentos burocráticos do estado estejam um passo a frente do crime e não só isso: façam o mapeamento correto das ocorrências para que medidas preventivas  (e.g. patrulhamento, inteligência, et cetera) tenham o máximo de assertividade possível.

E não só isso: com um mapeamento correto, além de questões de policiamento que podem ser corrigidas, mas também questões de tomada de decisão para criação/alteração da legislação podem ser tomadas em bases mais sólidas descartando todo o proselitismo que é feito sobre essa questão.

Crime Topic Modeling - Da Kuang, P. Jeffrey Brantingham, Andrea L. Bertozzi

Abstract: 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. Crime topic modeling positions behavioral situations as the focal unit of analysis for crime events. Though unlikely to replace formal legal crime classifications, crime topics provide a unique window into the heterogeneous causal processes underlying crime. We discuss whether automated procedures could be used to cross-check the quality of official crime classifications.

Objectives 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. Methods 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. Results 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. Conclusions Crime topic modeling positions behavioral situations as the focal unit of analysis for crime events. Though unlikely to replace formal legal crime classifications, crime topics provide a unique window into the heterogeneous causal processes underlying crime.

crime-topic-modeling