Re-counting crime: New methods to improve the accuracy of estimates of crime

在犯罪学领域，恐怕没有比准确统计犯罪数量更相关的科学研究了。犯罪估算是政策的核心。它们被用于警察资源的分配，更广泛地说，它们是政治辩论的中心主题，犯罪的明显增加是对现有法律和秩序政策的控诉。学者们也经常在他们的工作中使用犯罪统计数据，既试图理解为什么有些地方和人更容易犯罪，又利用犯罪的变化来帮助解释其他社会结果。当然，公众也会参考这些信息。例如，历史犯罪趋势现在包括在许多购房网站上。目前，估计犯罪数量的方法主要有两种：一种是直接利用警方掌握的犯罪记录；并使用受害者调查来近似犯罪，如英格兰和威尔士的犯罪调查，在那里，人们被要求报告过去一年的任何受害者。理论工作强调了这些数据中潜在错误的一些来源，表明这两种方法都有缺陷。然而，我们目前缺乏经验稳健量化的差异来源的误差在每个。我们也不完全了解这些误差可能对利用这些数据的分析估计产生的潜在影响，尽管来自其他领域的证据表明，这可能至少是实质性的。在这个项目中，我们将使用流行病学、生物统计学和调查研究领域开发的尖端统计模型来估计和调整警方记录的犯罪和犯罪调查数据中存在的测量误差问题。根据2011年至2019年的数据，我们将展示这两个数据源中系统偏差和随机误差的程度，以及这些误差如何随着时间的推移而演变。一旦对犯罪数据中存在的测量误差的检查完成，我们将使用我们的发现来生成英格兰和威尔士的调整后的犯罪计数，提供不同犯罪如何在空间和时间上变化的独特图片。最后，我们将使用这些新的犯罪估计与“现成的”测量误差调整技术，以证明测量误差对现有研究结果的潜在影响。除了这项严格的实证工作外，我们还将开展一系列能力建设活动，为研究人员提供必要的技能，使他们能够在自己的工作中结合犯罪数据进行测量误差调整。

There is probably no other scientific endeavour more relevant to the field of Criminology than to count crime accurately. Crime estimates are central to policy. They are used in the allocation of police resources, and more generally they are a central theme of political debate with apparent increases in crime serving as an indictment on existing law and order policies. Academics also make regular use of crime statistics in their work, both seeking to understand why some places and people are more prone to crime, and using variations in crime to help explain other social outcomes. And of course, members of the public also refer to this information. For example, historic crime trends are now included on many house-buying websites. Currently, there are two main ways of estimating the amount of crime: directly using police records of incidents that they are aware of; and approximating crime using victimisation surveys like the Crime Survey for England and Wales, where a sample of people are asked to report any victimisations in the past year. Theoretical work has highlighted a number of sources of potential error in these data, suggesting that both approaches are deficient. However, we currently lack an empirically robust quantification of the difference sources of error in each. Nor do we fully understand the potential impact that these errors might have on the estimates from analyses that makes use of this data, although evidence from other fields suggests that this may be at a minimum substantial. In this project we will use cutting edge statistical models developed in the fields of epidemiology, biostatistics and survey research to estimate and adjust for problems of measurement error present in police recorded crime and crime survey data. Drawing on data from 2011 to 2019 we will show the extent of systematic bias and random error in these two data sources, and how these errors may have evolved over time. Once the examination of the presence of measurement error in crime data is completed, we will use our findings to generate adjusted counts of crime across England and Wales, providing a unique picture of how different crimes vary across space and time. Finally, we will use these new crime estimates in tandem with 'off the shelf' measurement error adjustment techniques to demonstrate the potential influence that measurement error has on the findings of existing research. Alongside this rigorous empirical work, we will also engage in a range of capacity building exercises to furnish researchers with the necessary skills to incorporate measurement error adjustments in their own work with crime data.