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

American University, Washington DC

Investigators

Abstract

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.