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

重新统计犯罪：提高犯罪估计准确性的新方法

基本信息

批准号：
ES/T015667/1
负责人：
Ian Brunton-Smith
金额：
$ 30.88万
依托单位：
University of Surrey
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FT015667%2F1
关键词：
Re counting crime New methods

项目摘要

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.

可能没有其他科学努力比准确统计犯罪与犯罪学领域更相关了。犯罪估计是政策的核心。它们被用于分配警察资源，更普遍的是，它们是政治辩论的中心主题，犯罪率明显增加，成为对现行法律和秩序政策的控诉。学者们还在他们的工作中经常使用犯罪统计数据，既试图了解为什么某些地方和人群更容易发生犯罪，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，许多购房网站现在都包含历史犯罪趋势。目前，估计犯罪数量的方法主要有两种：直接使用警方所知的事件记录；通过英格兰和威尔士犯罪调查等受害调查来估算犯罪情况，其中样本被要求报告过去一年中的任何受害情况。理论工作强调了这些数据中的许多潜在错误来源，表明这两种方法都有缺陷。然而，我们目前缺乏对每个误差源的差异进行可靠的实证量化。我们也不完全了解这些错误可能对利用这些数据进行分析的估计产生的潜在影响，尽管其他领域的证据表明这可能至少是重大的。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。利用 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。一旦完成对犯罪数据中是否存在测量误差的检查，我们将利用我们的发现来生成英格兰和威尔士的调整后犯罪计数，从而提供不同犯罪在空间和时间上如何变化的独特画面。最后，我们将使用这些新的犯罪估计与“现成的”测量误差调整技术来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练习，为研究人员提供必要的技能，将测量误差调整纳入他们自己的犯罪数据工作中。