ATD: Understanding and Predicting User Mobility through Bayesian Models

点击翻译按钮获取中文摘要

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.