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.

在许多城市社区中，犯罪是一个主要问题。 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.尽管现有的犯罪预测和预防方法着重于犯罪的位置以检测``热区''的位置，但该项目采用了从根本上不同的，数据驱动的方法来综合多规模数据分析，以识别识别犯罪措施环境的特征和特征。这项高风险的高薪资项目研究基于实时犯罪数据以及与预防犯罪和安全机构的互动。通过揭示犯罪与环境，社会和经济因素之间的联系，该研究旨在证明对预防犯罪的综合系统方法的迫切需要，而不是专注于危机后的管理。这项跨学科的努力是开发跨公共城市景观的预防犯罪的计算方法，需要在时空中结合数据挖掘和统计方法，以提取有用的功能并从被动数据集中发现模型。拟议的项目将开发1）通过分析时间变化的时间和特定位置的系统和模式来开发对犯罪行为的基本理解的新工具，这是由于相互作用的网络物理实体之间的复杂过程而观察到的，并且2）可扩展的数据驱动的现有算法，这些算法将通过不断发展的犯罪行为来逐步进行，这些算法将不断发展，并涉及不断发展的犯罪行为，并在犯罪行动中逐渐发展，并涉及不断发展的犯罪行为，并且涉及犯罪性的范围。社区的属性，社会经济特征以及当前的时间以及背景。为了使对流数据的持续预测，同时保持高预测准确性和较低的时间复杂性，该项目将开发和培训犯罪预测人工神经网络（CANN），以跨越时空的预测。所提出的数据驱动模型的输出将提供一种新型的多目标优化公式，该公式将用于人员定位，巡逻调度和最安全的路线计算的集成优化。由此产生的决策支持环境将转移到USC公共安全部（DPS），洛杉矶警察局（LAPD）和South Park商业改善区（SPBID），以与他们的系统集成，以使决策者能够在任何给定时间选择最佳的行动。该项目将导致预防犯罪技术的技术发展，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汇集在一起​​ - 大学校园校园居民，校外社区居民，校外居民和企业与他们的员工，跨性别的通勤者，跨性别的通勤者，以及使社区的主题使社区更加安全。该项目将利用USC生活实验室，这是一个独特的城市？校园及其相邻社区是一个相互关联的基础设施社区的现实世界用例。

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.

FActCheck: Keeping Activation of Fake News at Check

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

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.