Data-Driven Optimal Police Patrol Zone Districting and Staffing

数据驱动的最佳警察巡逻区分区和人员配置

• 批准号: 2015787
• 负责人: He Wang
• 金额: $ 56.45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015787&HistoricalAwards=false
• 关键词: Data; Driven; Optimal; Police; Patrol

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 Police zone designs in large U.S. cities can become outdated if not effectively revised to keep up with population growth, changing transportation patterns, and urban development, leaving vulnerable communities at risk.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.

该奖项通过开发旨在改善警察和急救人员操作的数据驱动决策工具，为国家安全和福利做出贡献。该项目同时解决了改进巡逻区的设计和有效的人员配备问题。美国大城市的警区设计，如果不进行有效修改，以跟上人口增长、交通模式变化和城市发展的步伐，可能会过时，使脆弱的社区处于危险之中。此外，由于预算有限和人员流失率高，全国的警察人员配置继续受到限制。该项目将利用目前可用的大量数据来创建新的分析模型和算法，这些模型和算法有望大大改进传统方法。该项目的研究成果将以佐治亚州亚特兰大市的数据为依据，但对全国的警察机构也有价值。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，以可视化警察区域重构和警察工作量变化。研究小组将免费向其他感兴趣的机构提供计算机代码，用于数据分析、区域设计和工作人员规划。本项目采用的研究方法将在运筹学和数据分析的多个领域之间建立桥梁，包括统计学中的时空模型、应用概率论中的排队理论和离散随机优化。该项目将利用大规模警察报告、人口普查和交通数据，为警察应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，服务系统将使用随机方法建模和分析，以估计紧急呼叫的强度、位置和类别，以及警察和第一响应者的服务能力和旅行时间。该项目利用优化方法开发基于这些随机模型的高效区域设计和人员配置算法。该模型在预防性警务和公平覆盖方面的有效性将通过警务数据的实证分析进行回测。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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Spatiotemporal-textual point processes for crime linkage detection

用于犯罪关联检测的时空文本点过程

• DOI: 10.1214/21-aoas1538
• 发表时间: 2022
• 期刊: The Annals of Applied Statistics
• 作者: Zhu, Shixiang;Xie, Yao
• 通讯作者: Xie, Yao

Data-Driven Optimization for Police Districting in South Fulton, Georgia.

佐治亚州南富尔顿警察分区的数据驱动优化。

• 发表时间: 2021
• 期刊: KDD 2021 Workshop on Data Science for Social Good
• 作者: Zhu, Shixiang;Bukharin, Alexander;Lu, Le;Wang, He;Xie, Yao
• 通讯作者: Xie, Yao

Data-Driven Optimization for Atlanta Police-Zone Design

亚特兰大警区设计的数据驱动优化

• DOI: 10.1287/inte.2022.1122
• 发表时间: 2022
• 期刊: INFORMS Journal on Applied Analytics
• 影响因子: 1.4
• 作者: Zhu, Shixiang;Wang, He;Xie, Yao
• 通讯作者: Xie, Yao

Data-Driven Optimization for Police Districting in South Fulton, Georgia

佐治亚州南富尔顿警察分区的数据驱动优化

• 发表时间: 2021
• 期刊: KDD Workshop on Data Science for Social Good
• 作者: Zhu, Shixiang;Bukharin, Alexander;Lu, Le;Wang, He;Xie, Yao
• 通讯作者: Xie, Yao