ATD: Multiscale Anomaly Detection in Spatio-Temporal Multilayer Networks Encoding Human Mobility

ATD：编码人类移动性的时空多层网络中的多尺度异常检测

• 批准号: 2319250
• 负责人: Dane Taylor
• 金额: $ 25万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319250&HistoricalAwards=false
• 关键词: ATD; Multiscale; Anomaly; Detection; Spatio

Human mobility data from anonymized mobile phone devices are becoming increasingly available, enabling the detection of anomalies and potential threats from human movements. Due to the massive scale of data, it is difficult to detect anomalies that occur at different spatial and temporal resolutions. In addition, human movements are often associated with different categories of places (e.g., grocery stores and schools), and anomalies that are evident in one category may be obscure in another. This project will furnish new mathematical models and theories for encoding different categories of human movements as spatial-temporal multilayer networks. It will develop new algorithms to detect movement-pattern anomalies, which can help better forewarn anomalous events concerning national security. It will also advance our understanding of the impacts of anomalous and disastrous events on different categories of human movements. This project will contribute toward education by supporting graduate students and will facilitate interdisciplinary research between mathematics and geography. Open-source software tools will be implemented and publicly shared to help researchers and decision makers better predict, detect, and plan responses to future anomalous events. To represent human movements in different categories and effectively detect the associated anomalies, the investigators will pursue three targets in this project. Target 1 will develop spatial-temporal multilayer network models encoding anonymized mobile phone location data of the United States. The network-structural properties associated with both normal and expected anomalous situations (e.g., holidays) will be extensively examined to develop a family of realistic generative models. Target 2 will build algorithms to detect and characterize movement-pattern anomalies based on the multilayer network models using unsupervised spectral algorithms. Random matrix theory will be employed to obtain theoretic guidelines for how to optimally preprocess data to maximize the “detectability” of anomalies at different spatial and temporal resolutions. Target 3 will apply the developed multilayer network models and anomaly detection algorithms to two case studies, to further refine our models and algorithms and to gain new insights into the evolutions and impacts of the two important events.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.

来自匿名移动的电话设备的人类移动数据变得越来越可用，从而能够检测来自人类移动的异常和潜在威胁。由于数据规模庞大，很难检测到在不同空间和时间分辨率下发生的异常。此外，人类运动通常与不同类别的地点相关联（例如，杂货店和学校），在一个类别中明显的异常可能在另一个类别中模糊不清。该项目将提供新的数学模型和理论，用于将不同类别的人类运动编码为时空多层网络。它将开发新的算法来检测运动模式异常，这有助于更好地预警涉及国家安全的异常事件。它还将促进我们了解异常和灾难性事件对不同类别的人员流动的影响。该项目将通过支持研究生来促进教育，并将促进数学和地理之间的跨学科研究。开源软件工具将被实施并公开共享，以帮助研究人员和决策者更好地预测、检测和计划对未来异常事件的应对。为了代表不同类别的人类运动并有效地检测相关的异常，研究人员将在该项目中追求三个目标。目标1将开发编码美国匿名移动的电话位置数据的时空多层网络模型。与正常和预期异常情况相关联的网络结构特性（例如，假期）将被广泛审查，以开发一个家庭的现实生成模型。目标2将使用无监督频谱算法，基于多层网络模型构建检测和表征运动模式异常的算法。随机矩阵理论将用于获得理论指导，指导如何最佳地预处理数据，以最大限度地提高不同空间和时间分辨率下异常的“可探测性”。目标3将把开发的多层网络模型和异常检测算法应用到两个案例研究中，进一步完善我们的模型和算法，并对这两个重要事件的演变和影响获得新的见解。该奖项反映了NSF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。