EAGER: Safer Connected Communities Through Integrated Data-driven Modeling, Learning, and Optimization

EAGER：通过集成的数据驱动建模、学习和优化打造更安全的互联社区

• 批准号: 1637372
• 负责人: Viktor Prasanna
• 金额: $ 20万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637372&HistoricalAwards=false
• 关键词: EAGER; Safer; Connected; Communities; Through

Crime is a major problem in many urban communities. This project focuses on developing a framework for increased security and crime prevention in crime-prone environments by identifying and integrating hitherto disaggregated heterogeneous data and analyzing the causal and spatio-temporal interconnections between constituent parts of a connected community including environmental aspects (i.e., traffic, lighting, poverty levels, business proximity such as banks/ATMs), crime history, and social events. While existing crime prediction and prevention methods focus on the location of the crimes to detect ``hot-zones'', this project takes a fundamentally different, data-driven approach towards integrated multi-scale data analytics for identifying the characteristics and features of crime-prone environments. This high-risk high-payoff project research is based on real-time crime data and interactions with crime prevention and safety agencies. By revealing the connections between crime and environmental, social, and economic factors, this research aims to demonstrate the critical need of an integrated systems approach to crime prevention, instead of focusing on post-crisis management. This interdisciplinary endeavor of developing computational methods for crime prevention across public urban landscapes requires the combination of data mining and statistical methods in space and time to extract useful features and discover models from passive data sets. The proposed project will develop 1) new tools for the fundamental understanding of criminal behavior by analyzing the time varying and location-specific systems and patterns observed as a result of complex processes between interacting cyber-physical entities, and 2) scalable data-driven Nowcasting algorithms for crime prediction that will adapt with the constantly evolving state of criminal activity by continuously learning from a rich set of spatial and demographic features, including traffic, spatial attributes, socio-economic characteristics of neighborhoods, and current time, as well as context. To enable continuous forecasting over streaming data, while maintaining high prediction accuracy and low time complexity, the project will develop and train crime prediction artificial neural networks (CANN) for prediction across space and time. The output of the proposed data-driven models will feed a novel multi-objective optimization formulation that will be used for the integrated optimization of personnel positioning, patrol scheduling and safest route calculation. The resulting decision support environment, will be transferred to the USC Department of Public Safety (DPS), the Los Angeles Police Department (LAPD), and South Park Business Improvement District (SPBID) for integration with their systems to enable decision makers to choose the best course of action at any given time. This project will lead to the development of technology for crime prevention that will be directly applicable to smart and connected communities across the US, with the potential to bring together white and blue-collar residents from mixed urban communities- college campus residents, off-campus neighborhood residents and businesses with their employees, transiting commuters and law enforcement under the theme of making the communities quantifiably more secure. The project will leverage the USC Living Laboratory, a unique ?city within a city? campus and its adjacent neighborhoods as a real-world use case of a connected community of interrelated infrastructures.

犯罪是许多城市社区的主要问题。该项目的重点是制定一个框架，在犯罪多发环境中加强安全和预防犯罪，方法是查明和整合迄今为止分类的各种数据，分析一个相连社区各组成部分之间的因果关系和时空相互联系，包括环境方面（即，交通、照明、贫困程度、商业邻近性（如银行/ATM）、犯罪历史和社会事件。虽然现有的犯罪预测和预防方法侧重于犯罪的位置，以发现"热区"，但该项目采取了一种根本不同的数据驱动方法，对综合多尺度数据分析进行分析，以确定犯罪多发环境的特点和特征。这项高风险高回报的项目研究是基于实时犯罪数据以及与犯罪预防和安全机构的互动。通过揭示犯罪与环境、社会和经济因素之间的联系，这项研究旨在表明，迫切需要采取综合系统的方法预防犯罪，而不是侧重于危机后管理。这种跨学科的努力，开发计算方法，预防犯罪的公共城市景观需要在空间和时间的数据挖掘和统计方法相结合，以提取有用的功能，并发现模型从被动的数据集。拟议的项目将开发1）通过分析作为交互网络物理实体之间复杂过程的结果而观察到的时变和特定位置的系统和模式，2）可扩展数据-用于犯罪预测的驱动的临近预报算法，通过不断从丰富的空间和地理信息中学习，人口统计特征，包括交通、空间属性、社区的社会经济特征、当前时间以及上下文。为了实现对流数据的连续预测，同时保持高预测精度和低时间复杂度，该项目将开发和训练犯罪预测人工神经网络（CANN），以进行跨空间和时间的预测。拟议的数据驱动模型的输出将提供一个新的多目标优化公式，用于人员定位、巡逻调度和最安全路线计算的综合优化。由此产生的决策支持环境，将被转移到南加州大学公共安全部（DPS），洛杉矶警察局（LAPD），和南方公园商业改善区（SPBID）与他们的系统集成，使决策者能够选择最佳的行动方案在任何给定的时间。该项目将导致犯罪预防技术的发展，该技术将直接适用于美国各地的智能和互联社区，并有可能将来自混合城市社区的白色和蓝领居民-大学校园居民，校外社区居民和企业及其员工，过境通勤者和执法部门聚集在一起，使社区更加安全。该项目将利用南加州大学生活实验室，一个独特的？城市中的城市？校园及其邻近的社区作为一个现实世界的使用案例，一个相互关联的基础设施连接的社区。

项目成果

Network-based intervention strategies to reduce violence among homeless

• DOI: 10.1007/s13278-019-0584-8
• 发表时间: 2019-07-27
• 期刊: SOCIAL NETWORK ANALYSIS AND MINING
• 影响因子: 2.8
• 作者: Srivastava, Ajitesh;Petering, Robin;Prasanna, Viktor K.
• 通讯作者: Prasanna, Viktor K.

How to Stop Violence Among Homeless: Extension of Voter Model and Intervention Strategies

如何制止无家可归者中的暴力：选民模型的扩展和干预策略

• DOI: 10.1109/asonam.2018.8508641
• 发表时间: 2018
• 期刊: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM
• 作者: Srivastava, Ajitesh;Petering, Robin;Kannan, Rajgopal;Rice, Eric;Prasanna, Viktor K.
• 通讯作者: Prasanna, Viktor K.

FActCheck: Keeping Activation of Fake News at Check

• 发表时间: 2018-07
• 作者: Ajitesh Srivastava;R. Kannan;C. Chelmis;V. Prasanna