ATD: Collaborative Research: Point Process Algorithms for Threat Detection from Heterogeneous Human Mobility and Activity Data

Indiana University, Bloomington IN

Investigators

Abstract

This project aims to develop new algorithms and models for analyzing human-generated, space-time marked event data in order to quickly identify anomalous patterns that may represent active threats or threats about to occur. The general motivating principle behind the work is that such events, especially when intimately tied to human mobility, generally display robust patterning, such that significant deviations from the typical patterns, either on an individual or collective level, are cause for suspicion and should be treated as potentially dangerous. Thus, this project will better enable authorities to quickly determine when threatening situations arise, so that they can react to them rapidly, mitigating potentially devastating consequences. To achieve this overarching objective, several sub-objectives will be met: 1) constructing models to integrate disparate datasets on human events at varying levels of granularity, from individual-based to neighborhood-based to region-based; 2) developing a framework that ties this integrated data together with models of human mobility; 3) creating methods for detecting sudden changes within the data given the framework, at varying scales, including individuals, groups of individuals, or spatial regions. This project will construct a framework for analyzing human spatio-temporal event data arising from heterogeneous sources, based on the mathematics of stochastic point processes, but specifically tailored to represent events whose underlying structure is intimately tied to human mobility. Several ideas will be united in this framework, such that the resulting algorithms are able to identify anomalous behavior or events that may represent ongoing or emerging threats. First, new methods will be explored for pre-processing high frequency human mobility data -- eg., gps trace data -- to reduce dimensionality and better fit within the marked point process framework. Next, new classes of marked point processes will be developed that are better able to handle the detailed geometric structure often underlying spatial human event data, given the regularity of human motion upon which such events are often layered; high-order Hawkes processes geometrically embedding human mobility motifs are proposed specifically for this task. New methods will be developed for clustering data subject to these point processes at varying levels of abstraction and physical relevance, from individuals, to linked social groups, to neighborhoods, in order to better identify geographic regions or subsets of individuals that may be displaying or reacting to anomalous behavior. To go along with this, new ways will be developed to quickly detect anomalies within the data as compared to the expected point process through goodness of fit measures, again at differing levels of clustering; proposed here is a Bayesian method to detect emerging patterns for even very limited datasets using transfer learning. The end result with be a suite of tools that are all individually useful, and that combined will serve as a powerful new method of organizing and analyzing large datasets of human events to detect threatening behavior.