Collaborative Research: ATD: Statistical Detection of New Patterns and Potential Threats in Geospatial Sequences of Social and Political Events

合作研究：ATD：社会和政治事件地理空间序列中新模式和潜在威胁的统计检测

• 批准号: 1737978
• 负责人: Latifur Khan
• 金额: $ 10万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737978&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Statistical; Detection

The project focuses on the statistical detection and interpretation of new trends and geospatial pattern changes in sequences of social and political events. Millions of such events occur on a local, regional, national, and international scale. They are being recorded in publicly available domains, fed from a multitude of sources, from news media to social media, blogs, and tweets. Events include civic unrests, demonstrations, crimes, arrests, human rights activities, political conflicts, protests, cyber attacks, terrorist activities, publication of news, their analyses and discussions, and so on. Such events occur at random times, while their location, size, and consequences involve a lot of uncertainty. An abrupt change of pattern, appearance of a new distribution or trend is typically caused by a new circumstance that may represent a potential threat. For the prompt detection of such threats, sensitive yet reliable and computationally feasible statistical algorithms for change-point detection in geospatial sequences will be elaborated, followed by social, economic, and political interpretation of statistically detectable changes. The project will provide general tools for the prompt reaction to threatening anomalies identified in large continuously monitored databases, with a special focus on new patterns that represent potential threats to homeland security. Quick detection of sudden changes and unexpectedly appearing new trends is crucially important for the prompt reaction to potential security threats. To handle large data sets of high dimension in change-point detection problems, to combine simultaneously observed geospatial sequences of event data, and to develop computationally feasible algorithms for fast threat detection, three general approaches are exploited: (1) recursive change-point detection algorithms that are updated with each new data point while storing and processing minimum required information at each step; (2) auxiliary change-point warning schemes represented by computationally inexpensive and fast algorithms for the early detection of potential threats; (3) sequentially planned change-point detection algorithms that invoke the main detection scheme at the special interim time points only, and (4) maximum use of prior information by means of Bayesian detection algorithms.

该项目的重点是对新趋势的统计检测和解释以及社会和政治事件序列的地理空间模式变化。数百万此类事件发生在地方，地区，国家和国际规模上。从新闻媒体到社交媒体，博客和推文中，它们都被记录在公开可用的域中，这些域名是从多种来源提供的。事件包括公民动荡，示威，犯罪，逮捕，人权活动，政治冲突，抗议活动，网络攻击，恐怖活动，新闻发布，他们的分析和讨论等等。此类事件在随机时间发生，而其位置，大小和后果涉及很多不确定性。模式的突然变化，新分布或趋势的外观通常是由可能代表潜在威胁的新情况引起的。为了迅速检测此类威胁，将详细阐述地理空间序列中更敏感但可靠且计算可行的统计算法，以进行地理空间序列中的更改点检测，然后进行社会，经济和政治解释，对统计上可检测到的变化。该项目将为迅速反应提供一般工具，以威胁在大型连续监控数据库中识别出的异常情况，并特别关注代表对国土安全性潜在威胁的新模式。快速发现突然变化和出乎意料的新趋势对于对潜在安全威胁的迅速反应至关重要。要处理更改点检测问题中的高维数据集，以同时观察到事件数据的地理空间序列，并开发用于快速威胁检测的计算可行算法，利用了三种一般方法：（1）递归性更改点检测算法在每个数据点上更新，并在每个数据点上更新，并在每个数据点上进行了启用，则在每个数据点上都需要在各个数据点上进行信息，并在各个过程中进行了信息。 （2）辅助变更点警告方案由计算廉价和快速算法表示，用于早期发现潜在威胁； （3）仅在特殊临时时间点调用主要检测方案的顺序计划的更改点检测算法，以及（4）通过贝叶斯检测算法最大程度地使用先前信息。