ATD: Collaborative Research: Multivariate Quantiles for Rapid Spatio-Temporal Threat Detection

本项目将研究从多个来源、多个地点和不同时间点观察到的不同类型的社会属性数据。这些数据的几何特性将被分析，以量化和表征数据中的正常模式，然后利用这些模式来识别社会中突然偏离正常模式的情况。本项目将设计用于理解数据中的正常模式和快速检测数据的一个或多个方面的变化的方法。将对来自世界各地不同地点的数据进行分析，并用于制定减轻风险和应急反应的战略。本课题将研究高维时空数据的几何特性，构建多维极值指标。该指标以及其他统计和机器学习技术将在各种技术条件和框架下用于快速时空变化检测。这种变化可能是朝着特定的已知方向，或普遍偏离正常模式。检测多变量概率分布的极值和尾部变化的方法也将作为本项目的一部分发展。然后将研究社会，经济和供应链物流数据，以使用数据驱动技术制定政策和快速响应策略。

Different kinds of data on societal attributes, observed from multiple sources, at multiple locations, and at different points in time, will be studied in this project. The geometrical properties of such data will be analyzed to quantify and characterize normal patterns in the data, which will then be leveraged to identify sudden departures from normal patterns within societies. Methodology for understanding normal patterns in the data and rapidly detecting change in one or more aspects of the data will be devised in this project. Data from different locations around the world will be analyzed and used to formulate strategies for risk mitigation and emergency responses.The geometric properties of high-dimensional spatio-temporal data will be studied in this project to construct a multi-dimensional extremity indicator. This indicator and other statistical and machine learning techniques will be used for rapid spatio-temporal change detection, under a variety of technical conditions and frameworks. Such changes may be towards specific known directions, or generic departures from normal patterns. Methods for detecting changes in extremes and tails of multivariate probability distributions will likewise be developed as part of this project. Social, economic, and supply chain logistics data will then be studied to develop policy and rapid response strategies using data-driven techniques.