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一个主要问题。该项目旨在通过识别和整合迄今为止分类的异构数据，并分析连接社区各组成部分之间的因果关系和时空相互联系，包括环境因素(即交通、照明、贫困水平、商业邻近程度（如银行/自动取款机）、犯罪历史和社会事件，开发一个框架，以提高犯罪易发环境中的安全性和预防犯罪。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“热点地区”，但该项目采用了一种根本不同的，数据驱动的方法，用于集成多尺度数据分析，以识别易犯罪环境的特征和特征。这项高风险、高回报的项目研究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，本研究旨在证明一种综合系统方法来预防犯罪的迫切需要，而不是专注于危机后管理。这项跨学科的研究旨在开发跨公共城市景观预防犯罪的计算方法，需要将空间和时间上的数据挖掘和统计方法相结合，以从被动数据集中提取有用的特征并发现模型。该项目将开发1)通过分析相互作用的网络物理实体之间的复杂过程所导致的时变和特定地点的系统和模式，来基本理解犯罪行为的新工具；2)用于犯罪预测的可扩展数据驱动的临近预测算法，该算法将通过不断学习丰富的空间和人口特征来适应不断发展的犯罪活动状态。包括交通，空间属性，社区的社会经济特征，当前时间，以及环境。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），用于跨空间和时间的预测。所提出的数据驱动模型的输出将提供一个新的多目标优化公式，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，使决策者能够在任何给定的时间选择最佳的行动方案。该项目将引领预防犯罪技术的发展，直接适用于美国各地的智能和互联社区，有可能将来自混合城市社区的白人和蓝领居民聚集在一起，这些社区包括大学校园居民、校外社区居民、企业及其员工、过境通勤者和执法部门，其主题是使社区更加安全。该项目将利用南加州大学生活实验室，一个独特的？城中之城？校园及其邻近社区作为相互关联的基础设施连接社区的现实用例。

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.

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