Predictive analytics and Policing: Translating cutting-edge academic research into actionable intelligence and developing useable software tools

摘要犯罪学研究一段时间以来一直专注于犯罪的类型以及他们可能这样做的原因。然而，在过去十年中，关于犯罪方式、地点和时间是否存在规律性这一补充问题的研究取得了实质性进展。这类研究表明，犯罪风险不是随机的，而是对一些人，特别是社区和一些家庭来说，犯罪风险更大。同样，研究表明，犯罪的风险随着时间的推移而变化。例如，犯罪更有可能发生在一天中的某些时间段和一年中的某些月份。这些发现对警务行动具有明显的影响，目前为许多英国警察部队采取的减少犯罪战略提供了参考。申请者最近进行的学术研究表明，犯罪模式中还存在其他规律，识别这些规律可以为警察决策提供信息，从而更有效地利用警察(和其他)资源。例如，我们的研究表明，当一个地点发生入室盗窃时，不久的将来，其他地点更有可能发生在同一地点或附近。换句话说，入室盗窃的风险传播方式与传染病的传播方式大致相同。重要的是，识别这种规律可以提供情报，告诉警方可以采取什么行动来预防或侦查犯罪。此外，根据这些发现，有可能开发数学模型，以产生关于犯罪事件未来地点的预测。这类模型已被证明优于现有的犯罪测绘方法。在警察预算被削减的时候，能够查明这类规则并利用它们为警察的优先事项或战术提供信息特别重要。然而，由于上述类型的分析处于学术研究的前沿，而且是为研究目的而产生的，因此无法使用现有软件进行。拟议项目的目的是弥合这一知识与实践之间的差距，确定最有希望的分析类型，并开发将对警察实践产生真正影响的可用的软件包。这种合作将促进警察从业人员和学者之间的双向交流，并导致将尖端研究转化为可用于防止对社会造成伤害的工具。我们预计，该项目还将导致确定新的研究问题，为学术议程提供信息。

SummaryCriminological research has for some time focused on the types of people that commit crime and why they might do so. However, over the last decade, there has been substantial progress in research concerned with the complimentary question of whether there exist regularities in how, where and when crimes are committed. Such research has demonstrated that the risk of crime is not random, but that the risk of crime is greater for some people, in particular neighbourhoods, and at some homes. Similarly, research demonstrates that the risk of crime varies over time. For instance, crime is more likely at certain times of the day and months of the year. These findings have clear implications for operational policing and currently inform the crime reduction strategies adopted by many UK police forces. More recent academic research conducted by the applicants demonstrates that other regularities in crime patterns exist and that their identification can inform police decision-making in such a way so as to lead to the more effective use of police (and other) resources. For example, our research shows that when a burglary occurs at one location, others are more likely to occur at the same location or nearby in the near future. Put another way, the risk of burglary spreads in much the same ways as a communicable disease. Importantly, the identification of such regularities can provide intelligence that can inform what the police might do to prevent or detect crime. Furthermore, based on such findings, it is possible to develop mathematical models to generate predictions regarding the future locations of crime events. Such models have been shown to outperform existing methods of crime mapping. The ability to identify these kinds of regularities and to use them to inform police priorities or tactics is particularly important at a time when police budgets are being cut. However, because the types of analysis noted above are at the cutting edge of academic research, and were generated for research purposes, they cannot be conducted using available software. The aim of the proposed project is to bridge this knowledge-practice gap, identifying the most promising types of analysis and developing usable software packages that would make a real difference to police practice. The collaboration would facilitate a two-way exchange between police practitioners and academics and lead to the translation of cutting edge research into tools that can be used to prevent harm to society. We envisage that the project would also lead to the identification of new research questions that would inform the academic agenda.