ATD: Collaborative Research: Spatio-Temporal Data Analysis with Dynamic Network Models

University Of Toledo, Toledo OH

Investigators

Abstract

Modeling and analyzing spatially-determined and time-varying (spatiotemporal) interactions is at the forefront of research in many scientific and engineering disciplines, including the social and behavioral sciences, transportation, healthcare, economics, and epidemiology. This project represents spatiotemporal interactions of entities as a dynamic complex network and aims to develop statistically-principled methods for modeling, analyzing, and monitoring the dynamic interactions. The methods developed in this work will provide scalable solutions for problems relevant to threat detection, including understanding spreading of diseases and viruses through human proximity networks, understanding human migration patterns through geo-tagged social media data, and monitoring multi-modal urban mobility networks through video footage and sensor logs in a smart city. Graduate and undergraduate students will be trained in interdisciplinary data science through involvement in the research. New data structures, models, and algorithms for manipulating and analyzing spatiotemporal networks will be implemented in the widely-used NetworkX Python package. The project aims to advance the field of spatiotemporal network analysis by developing new models and methods for representing, monitoring, and predicting spatiotemporal interactions. The research introduces new problem formulations, new analytical methods, and new algorithmic techniques for implementation. This project has three primary aims. First, the project will develop a dynamic embedding model in a latent hyperbolic space to represent spatiotemporal networks. This model enables tracking topological changes both at the network level and at the level of pairs of entities over time. Next, the project will investigate a network surveillance framework based on a multi-resolution exponential random graph model to monitor complex spatiotemporal systems for real-time anomalies and threats. Third, the project will develop a multivariate point process on collections of actors in a spatiotemporal network to model timestamped directed events across different regions in space. This project seeks to create an integrated framework for simultaneously monitoring systematic risk and detecting imminent threat to a system using multi-modal network monitoring techniques. The techniques under development will be utilized to monitor complex systems arising from massive spatiotemporal data accumulation, including data on human contacts through physical proximity, social media data, and event data such as homicides in city neighborhoods and conflicts between countries. The fundamental results derived in this work will guide research in modeling and inference on dynamic networks and will serve as a benchmark for future work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.