Using advanced data analytics to assess the spatial causal effects of policing policies and practices

程序正义的理论是建立在这样一种理念之上的，即当人们评价他们与警察的互动时，他们主要关注的是警察是否做出客观和中立的决定，并以公平和尊重的方式对待他们。当人们在程序上受到公正对待时，他们往往会认为当局在道德上是适当的，并同意他们的行动和要求，即使他们不同意。反过来，当人们认为法律的权威是适当和公正的，他们就会感到有一种基于规范的义务，即遵守法律并与警察和刑事法院合作。合法性的概念是民主警务的核心，在民主社会中，警察必须通过公正行事、适度使用强制手段并说服公民他们是一个有权被服从的机构来寻求和维持公众的支持。然而，在程序正义文献中，迄今为止收集的大多数经验证据都是观察性的，并依赖于对统计关联的解释。事实上，缺乏系统地评估该理论因果关系的研究。在最近的文献综述中，Nagin和Telep（2017：18）表达了他们的担忧：“尚未确定的是，这些关联是否反映了程序公正的待遇与感知的合法性和遵守之间的因果关系。“然而，如果没有实证研究证明强有力的因果关系，就很难制定成功的政策举措。因此，我参加这项研究的主要目的是测试和推进对警务文献中一些核心因果主张的理论理解。具体而言，我将审查邻里水平和基于位置的警察效果。公民对警察的经历和看法存在很大差异，但尚不清楚这在多大程度上可归因于不同街区的不同警务策略。通过使用地理编码的行政警察数据，并将其与公众态度调查相结合，我的研究可以确定在特定社区最有效的警务实践，为警察部队提供量身定制的建议。为了确定因果效应，我将使用最先进的因果推理技术，多级匹配和基于位置的回归不连续设计。在一个nushell，目前的建议计划解决一个关键方面的程序正义警务：如何影响警务举措不同的邻里与不同的特点？

Brief outline of the research agendaThe theory of procedural justice is built on the idea that when people evaluate their interactions with the police, they are primarily focussed on whether or not the officer makes objective and neutral decisions and treats them in a fair and respectful manner. When people are treated in a procedurally just manner, they tend to find the authorities morally appropriate and give consent to their actions and demands even when they disagree with them. In turn, when people view legal authorities as proper and just, they feel a normatively grounded duty to comply with the law and cooperate with the police and criminal courts. The concept of legitimacy lies at the heart of democratic policing, in that in a democratic society police must seek and maintain public support by acting impartially, using coercion proportionately and persuading the citizenry that they are an institution that is entitled to be obeyed.Yet, in the procedural justice literature, most of the empirical evidence gathered so far is observational in nature, and rely on the interpretation of statistical associations. In fact, there is a dearth of research systematically assessing the causal claims made by the theory. In a recent review of the literature, Nagin and Telep (2017: 18) voiced their concern: "What has not been established is whether these associations reflect a causal connection between procedurally just treatment and perceived legitimacy and compliance." However, without empirical research demonstrating robust causal relationships, it is difficult to devise successful policy initiatives.Thus, my principal aim with this fellowship is to test and advance theoretical understanding of some core causal claims of the policing literature. Specifically, I will scrutinise neighbourhood-level and location-based police effects. There is a substantial heterogeneity in the citizens' experiences and views regarding police officers but it is yet unclear to what extent this can be attributed to varying policing strategies in different neighbourhoods. By using geo-coded administrative police data, and merging it with public attitudes surveys, my research can identify policing practices that work best in particular neighbourhoods, to provide tailored recommendations to police forces. To identify causal effects, I will use state-of-the-art causal inference techniques, multilevel matching and location-based regression discontinuity designs. In a nushell, the current proposal plans to address one crucial aspect of procedural justice policing: how the effects of policing initiatives vary across neighbourhoods with different characteristics?