ATD: Nonparametric Testing and Fast Computing Methods for Spatiotemporal Models with Applications to Threat Detection

人类动力学的研究旨在通过分析模型来理解人类的行为。它不仅具有探测人类异常现象的潜力，而且具有遏制人类社会潜在的灾难性损害和精神恐怖的能力，因此在安全和国防领域受到了极大的关注。最近社交媒体的发展彻底改变了人们的日常生活，开启了人类动力学研究的新时代。已经证明，某些人类行为可以使用代理工具进行定量建模。社交媒体数据和可穿戴设备数据是两个主要的代理来源，可以用来理解时空趋势，从中我们可以识别人类动态的异常模式。异常模式可以作为灾害的一个指标。虽然社交媒体数据和可穿戴设备数据包含了丰富的信息来理解人类的行为，但随着用户产生新的内容或引入新的路线，它们也在不断发展。经典的统计模型不足以模拟不断变化的时空趋势。更重要的是，时空模型的计算成本非常高，这对人体动态数据的实时分析提出了重大挑战。为了克服这些挑战，我们提出了新的统计理论、方法和算法来有效地分析时空模型的局部和全局趋势。该项目将培养学生参与前沿和跨学科的大数据研究。尽管迫切需要，但人类动态研究的统计工具仍然缺乏。在这个项目中，我们的目标是开发时空模型，以了解社交媒体和个人可穿戴设备数据的内在时空趋势。分析大规模时空数据的关键挑战是超大样本量。例如，Twitter每天接收数以亿计的推文。这些推文中的许多都被记录在公共流中。我们开发可扩展的计算方法来克服这一挑战。该项目开发的快速网络计算原理和中间件是“大数据”计算和自治系统必不可少的基础工具。所提出的框架可用于发现任何超大动态数据集中的异常事件，并激发大数据分析的新研究方向。提出的统计工具广泛适用于科学、工程和人文学科。这项研究将与地理和统计方面的专家合作进行；因此，建议的工作将通过快速经验反馈得到信息，并可以结合时空建模的现代进展。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。

The study of human dynamics aims to understand human behaviors using analytical models. It has received substantial attention in the security and defense area not only for its potential in detecting human anomalies but also for its capability in containing potential disastrous damages and mental horror in human society. The recent development in social media has revolutionized daily life and inaugurated a new era in human dynamics study. It has been demonstrated that certain human behavior can be modeled quantitatively using proxy tools. Social media data and wearable device data are two major sources of proxies that can be used to understand spatiotemporal trends from which we can identify abnormal patterns in human dynamics. The abnormal patterns can be used as an indicator for disasters. Although social media data and wearable device data contain a wealth of information to understanding human behavior, they are constantly evolving as users generate new content or as new routes are introduced. Classic statistical models are not enough to model constantly-evolving spatial and temporal trends. More importantly, the computational cost for a spatiotemporal model is extremely high, which poses a significant challenge for real-time analysis of human dynamics data. To overcome these challenges, we propose novel statistical theory, methods, algorithms for efficiently analyzing local and global trends of a spatiotemporal model. The project will train students to participate in cutting-edge and interdisciplinary big data research. Despite the urgent need, statistical tools for human dynamic studies are still lacking. In this project, we aim to develop spatiotemporal models to understand the inherent spatial/temporal trends of social media and personal wearables data. The key challenge for analyzing large-scale spatiotemporal data is the super-large sample size. For example, every day Twitter takes in hundreds of million tweets. Many of these tweets are recorded in public streams. We develop scalable computational methods to surmount the challenge. The fast, in-network computing principles and middleware developed in this project are fundamental and indispensable tools for "big data" computation and autonomous systems. The proposed framework can be used to discover unusual events in any super-large dynamic data set and inspire a new line of research in big data analytics. The proposed statistical tools are widely applicable in science, engineering, and humanities. The research will be conducted in collaboration with experts in both geography and statistics; consequently, the proposed work will be informed by rapid empirical feedback and can incorporate modern advances in spatiotemporal modeling.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.