Data-Driven Optimal Police Patrol Zone Districting and Staffing

该奖项通过开发旨在改善警察和第一反应人员行动的数据驱动的决策工具，为国家的安全和福利做出贡献。该项目同时解决了改进巡逻区设计和提高警官配置效率的问题。美国大城市的警区设计可能会过时，如果不能有效地进行修改，以跟上人口增长、不断变化的交通模式和城市发展的步伐，使脆弱的社区处于危险之中。此外，全国范围内的警察人员配置继续受到预算有限和高自然减员率的限制。该项目将利用当今可用的海量数据来创建新的分析模型和算法，这些模型和算法有望显著改进传统方法。该项目的研究成果将由佐治亚州亚特兰大市的数据提供信息，但将对全国警察机构具有价值。特别是，该项目将开发具有交互式图形界面的计算决策支持工具，以可视化警区重新配置和警察工作量变化。研究小组将向其他感兴趣的机构免费提供用于数据分析、区域设计和人员规划的计算机代码。该项目所采用的研究方法将在运筹学和数据分析的多个领域之间架起桥梁，包括统计学中的时空模型、应用概率中的排队论和离散随机优化。该项目将利用大规模的警察报告以及人口普查和交通数据，为警察紧急服务系统创建高保真模型。通过利用现代统计和排队方法，将使用随机方法对服务系统进行建模和分析，以估计紧急呼叫的强度、位置和类别，以及警察和急救人员的服务能力和旅行时间。该项目使用优化方法来开发基于这些随机模型的区域设计和人员配置的高效算法。通过对警务数据的实证分析，验证该模型在预防警务和公平覆盖方面的有效性。该项目将涉及研究生，他们将学习如何使用现代优化方法来改进对国家安全至关重要的服务部门的设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award contributes to the nation’s security and welfare by developing data-driven decision tools designed to improve police and first-responder operations. The project simultaneously addresses improved patrol zone design and efficient officer staffing. Police zone designs in large U.S. cities can become outdated if not effectively revised to keep up with population growth, changing transportation patterns, and urban development, leaving vulnerable communities at risk. In addition, police staffing nationwide continues to be constrained by limited budgets and high attrition rates. This project will leverage the vast amounts of data that are available today to create new analytical models and algorithms that promise significant improvements over traditional approaches. The research outcomes in this project will be informed by data from the City of Atlanta, Georgia but will be of value to police agencies nationwide. In particular, the project will develop computational decision support tools with interactive graphical interfaces to visualize police zone reconfiguration and police workload change. The research team will make available computer codes for data analysis, zone design and staff planning free of charge to other interested agencies. The research methods employed in this project will bridge several fields in operations research and data analytics, including spatial-temporal models in statistics, queueing theory in applied probability, and discrete stochastic optimization. The project will create high fidelity models for police emergency service systems using large-scale police reports and census and transportation data. By leveraging modern statistical and queueing methods, the service system will be modeled and analyzed using stochastic methods in order to estimate intensity, location, and categories of emergency calls, as well as the service capacity and travel time of police officers and first-responders. The project uses optimization methods to develop efficient algorithms for zone design and staffing based on these stochastic models. The model’s effectiveness in preventive policing and equitable coverage will be back tested through empirical analysis of policing data. The project will involve graduate students who will be learn how modern optimization methods can be used to improve design in a service sector critical to the nation's security.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.