ATD: Understanding and Predicting User Mobility through Bayesian Models

该项目的目标是开发新的数学和统计工具，用于分析与人员流动模式有关的地理空间数据集。重点是使用从中等到大量行为者的时空数据对流动性进行建模和预测，目的是确定可以作为关键事件领先指标的不寻常行为模式或既定模式(在人口或个人层面)的突然变化。建议的模型对社会学和人类学理论的评估以及国防和自然安全应用都有影响。这项研究围绕三个子项目展开。第一个重点是开发用于高分辨率地理参考数据的Mattern排斥点过程模型的变体，该模型能够捕获真实流动性数据的众所周知的特征。PI既考虑了人口一级的模型，也考虑了个人一级的模型，这些模型具有高度的可解释性，并允许随着时间的推移而演变过程和确定系统中的结构变化。第二个子项目集中于(可能隐藏的)关联数据的马尔可夫模型，其中只有粗略的地理空间信息可用(例如，可能知道个人在诸如无线接入点等固定点的范围内，但不知道其确切位置)。同样，重点是确定个人和群体层面行为模式的变化，并对未来模式做出短期预测。最后，第三个子项目重点研究了组合多源高分辨率和低分辨率地理空间数据以提高算法精度的问题。在所有情况下，重点都是贝叶斯模型，它简化了不确定性量化，并可以很容易地纳入正式的决策分析框架。

The goal of this project is to develop novel mathematical and statistical tools for the analysis of geospatial datasets associated with human mobility patterns. The focus is on modeling and prediction of mobility using spatio-temporal data from a moderate to large number of actors with the objective of identifying either unusual behavioral patterns or abrupt changes in well-established patterns (at either the population or individual level) that can serve as leading indicators of critical events. The proposed models have implications for the evaluation of sociological and anthropological theories, as well as for defense and natural security applications.The research revolves around three subprojects. The first one focuses on the development of variants of Mattern repulsive point process models for high-resolution geo-referenced data that are able to capture well-known features of real-life mobility data. The PI considers both population-level and individual-level models that are highly interpretable and allow for the evolution of the process over time and the identification of structural changes in the system. The second subproject focuses on (possibly hidden) Markov models for association data in which only coarse geo-spatial information is available (e.g., it might be known that the individual is within range of a fixed point, such as an wireless access point, but not their exact location). Again, the focus is on identifying changes in individual- and population-level behavioral patterns and making short-term predictions about future patterns. Finally, the third subproject focuses on the problem of combining multiple sources of high- and low-resolution geo-spatial data to improve the accuracy of algorithms. In all cases the emphasis is on Bayesian models, which simplify uncertainty quantification and can be easily incorporate into formal decision analytic frameworks.