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.

该项目的重点是统计发现和解释社会和政治事件序列中的新趋势和地理空间格局变化。数百万这样的事件发生在地方、地区、国家和国际范围内。它们被记录在公开可用的领域，从新闻媒体到社交媒体、博客和推特等众多来源提供信息。事件包括公民骚乱、示威、犯罪、逮捕、人权活动、政治冲突、抗议、网络攻击、恐怖活动、新闻发布、新闻分析和讨论等等。这些事件是随机发生的，而它们的位置、大小和后果都包含很多不确定性。模式的突然变化、新分布或趋势的出现通常是由可能代表潜在威胁的新情况引起的。为了及时发现这些威胁，将详细阐述用于地理空间序列变化点检测的敏感、可靠和计算上可行的统计算法，然后是对统计可检测变化的社会、经济和政治解释。该项目将提供通用工具，以便对大型连续监测数据库中发现的威胁异常作出迅速反应，并特别关注对国土安全构成潜在威胁的新模式。快速发现突然变化和意外出现的新趋势对于迅速应对潜在的安全威胁至关重要。为了在变点检测问题中处理高维的大数据集，结合同时观测到的事件数据的地理空间序列，并开发计算上可行的快速威胁检测算法，采用了三种一般方法：(1)递归变点检测算法，该算法随着每个新数据点的更新而更新，同时在每一步存储和处理所需的最小信息；(2)以计算成本低、速度快的算法为代表的辅助变点预警方案，用于早期发现潜在威胁；(3)顺序规划的变化点检测算法，仅在特定的过渡时间点调用主检测方案；(4)通过贝叶斯检测算法最大限度地利用先验信息。