Workshop on Decision Analytics for Dynamic Policing; Arlington, Virginia; May 9-10, 2019

动态警务决策分析研讨会；

• 批准号: 1917624
• 负责人: Victoria Chen
• 金额: $ 5万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917624&HistoricalAwards=false
• 关键词: Workshop; Decision; Analytics; Dynamic; Policing

This award supports a conference that convenes researchers from the fields of operations engineering and criminal justice, along with police department leadership from across the US, to explore research directions in decision analytics and operations research to improve dynamic, data-driven policing methods. Beginning in 2008, so-called "predictive policing", which entails using analytical methods to predict how, when, where, and by whom crimes will occur, has been deployed by police departments in many states. These methods rely on historical data collected on time, location, and details of crimes, as well as on characteristics of perpetrators and victims. While some jurisdiction have shown an overall decrease in crime after implementation of predictive policing, other evaluative studies have shown the effects to be negligible or even detrimental. These methods have been criticized on three critical aspects: (1) they are not informed by a sociological perspective; (2) they assume that data include the necessary information for both prediction and effective response; and (3) they ignore the dynamic nature of the policing system. This conference will bring together appropriate researchers, along with practitioners, to build a cross-disciplinary understanding of policing challenges that may be addressable by decision analytics and operations research.One of the most serious challenges facing police departments is the effective use of resources to address calls for service, especially when responding to serious crimes. Police departments recognize that part of a comprehensive approach to community safety involves protecting the rights of all involved during police encounters. The focus of this workshop is on identifying operational aspects, including data collection and analysis, planning, training, and crime scene investigation, where additional research may lead to improved resource allocation. Day 1 of the conference includes keynotes on predictive policing and data analytic case studies, as well as breakout sessions for working groups. Day 2 of the conference will include a keynote speaker addressing international policing strategies, and working groups will continue to discuss and document research needs. Four teams of researchers who have established relationships with large police organizations serve as workshop organizers. Additional teams of attendees (operations engineers, criminal justice researchers, police department leadership) will be recruited to participate in the workshop. This activity will provide a unique forum to bring together a variety of stakeholders to encourage research on this important societal topic.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持了一次会议，该会议召集了来自运营工程和刑事司法领域的研究人员，以及来自美国各地的警察部门领导，以探索决策分析和运营研究中的研究指示，以改善动态，数据驱动的警务方法。 从2008年开始，所谓的“预测警务”需要使用分析方法来预测许多州的警察部门如何部署，何时，何时，何地和犯罪。 这些方法依赖于在时间，位置和犯罪的细节以及肇事者和受害者的特征上收集的历史数据。 尽管某些管辖权在实施预测性警务后显示犯罪的总体下降，但其他评估研究表明，这种影响可以忽略不计甚至有害。 这些方法已经在三个关键方面受到批评：（1）他们没有以社会学的观点告知它们； （2）他们假设数据包括预测和有效响应的必要信息； （3）他们忽略了警务系统的动态性质。 这次会议将汇集适当的研究人员以及从业人员，以跨学科对警务挑战的理解，这是由决策分析和运营研究可解决的。警察部门面临的最严重挑战之一是有效地使用资源来解决资源来解决问题。呼吁服务，尤其是在回应严重犯罪时。 警察部门认识到，全面的社区安全方法涉及保护警察遭遇期间所有人的权利。 该研讨会的重点是确定运营方面，包括数据收集和分析，计划，培训和犯罪现场调查，在此过程中，其他研究可能会导致资源分配的改善。 会议的第1天包括有关预测性警务和数据分析案例研究的主题演讲，以及工作组的突破会议。会议的第2天将包括一个针对国际警务策略的主题演讲者，工作组将继续讨论和记录研究需求。 与大型警察组织建立关系的四个研究人员团队是研讨会组织者。 将招募其他参与者团队（运营工程师，刑事司法研究人员，警察局领导）参加研讨会。 这项活动将为一个独特的论坛提供各种利益相关者，以鼓励对这个重要的社会主题进行研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。