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

一段时间以来，犯罪学的研究一直集中在犯罪的人的类型以及他们为什么会这样做。然而，在过去的十年中，关于犯罪发生的方式、地点和时间是否存在规律这一补充性问题的研究取得了实质性进展。这些研究表明，犯罪的风险不是随机的，但对某些人来说，特别是在某些社区和某些家庭，犯罪的风险更大。同样，研究表明，犯罪的风险随着时间的推移而变化。例如，犯罪在一天中的特定时间和一年中的特定月份更容易发生。这些发现对警务工作有明确的影响，目前为许多英国警察部队采用的减少犯罪战略提供了信息。申请人最近进行的学术研究表明，犯罪模式中存在其他规律，对这些规律的识别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，当入室盗窃发生在一个地方时，在不久的将来，其他入室盗窃更有可能发生在同一地点或附近。换句话说，入室盗窃风险的传播方式与传染病的传播方式大致相同。重要的是，这种规律的识别可以提供情报，可以告知警察可以采取什么措施来预防或侦查犯罪。此外，根据这些发现，有可能开发数学模型来预测未来犯罪事件的地点。这样的模型已经被证明优于现有的犯罪地图绘制方法。在警察预算被削减的时候，识别这些规律并利用它们来告知警察优先事项或战术的能力尤为重要。然而，由于上面提到的分析类型处于学术研究的前沿，并且是为研究目的而产生的，因此它们不能使用可用的软件进行。拟议项目的目的是弥合这种知识与实践之间的差距，确定最有前途的分析类型，并开发可用的软件包，这些软件包将对警察实践产生真正的影响。这项合作将促进警务人员和学者之间的双向交流，并将前沿研究成果转化为可用于防止危害社会的工具。我们设想该项目还将导致确定新的研究问题，这些问题将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.