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

该项目旨在开发新的算法和模型，用于分析人类生成的时空标记事件数据，以快速识别可能代表活跃威胁或即将发生的威胁的异常模式。这项工作背后的一般动机原则是，这些事件，特别是当与人类流动密切相关时，通常会显示出强大的模式，因此，无论是在个人还是集体层面上，与典型模式的重大偏差都是可疑的原因，应该被视为潜在的危险。因此，该项目将使当局能够更好地迅速确定何时出现威胁局势，以便能够迅速作出反应，减轻潜在的破坏性后果。 为了实现这一总体目标，将实现几个子目标：1）构建模型，以整合不同粒度级别的人类事件的不同数据集，从基于个人到基于社区到基于区域; 2）开发一个框架，将这种整合的数据与人类流动模型联系在一起; 3）创建用于检测给定框架的数据内的突然变化的方法，在不同的尺度上，包括个人、个人群体或空间区域。该项目将构建一个框架，用于分析人类时空事件数据产生的异构源，基于随机点过程的数学，但专门定制，以代表事件的底层结构是密切联系到人类的流动性。几个想法将在这个框架中统一起来，这样产生的算法就能够识别可能代表正在进行或正在出现的威胁的异常行为或事件。首先，将探索用于预处理高频人类移动数据的新方法-例如，全球定位系统跟踪数据-减少维度，更好地适应标记点进程框架。接下来，将开发新的标记点过程类，能够更好地处理详细的几何结构，往往是潜在的空间人类事件数据，考虑到人类运动的规律性，这些事件往往是分层的;高阶霍克斯过程几何嵌入人类移动图案提出专门为这项任务。将开发新的方法，用于在不同的抽象和物理相关性级别上对这些点过程的数据进行聚类，从个人到相关的社会群体，再到社区，以便更好地识别可能显示或对异常行为做出反应的地理区域或个人子集。为了沿着，将开发新的方法来快速检测数据中的异常，与通过拟合优度测量的预期点过程相比，再次在不同的聚类水平;这里提出了一种贝叶斯方法，使用迁移学习来检测即使是非常有限的数据集的新兴模式。最终的结果是一套工具，这些工具都是单独有用的，并且结合起来将作为一种强大的新方法来组织和分析人类事件的大型数据集，以检测威胁行为。

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.