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

建模和分析空间确定和时变（时空）相互作用是许多科学和工程学科研究的前沿，包括社会和行为科学，交通，医疗保健，经济学和流行病学。 该项目将实体的时空交互表示为动态复杂网络，旨在开发用于建模，分析和监控动态交互的方法。这项工作中开发的方法将为威胁检测相关问题提供可扩展的解决方案，包括通过人类邻近网络了解疾病和病毒的传播，通过地理标记的社交媒体数据了解人类迁移模式，以及通过智能城市中的视频片段和传感器日志监控多模式城市移动网络。研究生和本科生将通过参与研究进行跨学科数据科学培训。新的数据结构，模型，和时空网络的操作和分析算法将在广泛使用的NetworkX Python包中实现。该项目旨在通过开发新的模型和方法来表示，监测和预测时空网络分析领域。该研究引入了新的问题公式，新的分析方法和新的算法技术的实施。该项目有三个主要目标。首先，该项目将在潜在双曲空间中开发一个动态嵌入模型来表示时空网络。该模型能够跟踪网络级和实体对级的拓扑变化。接下来，该项目将研究基于多分辨率指数随机图模型的网络监控框架，以监控复杂时空系统的实时异常和威胁。第三，该项目将在时空网络中的参与者集合上开发一个多变量点过程，以模拟空间中不同区域的时间戳定向事件。该项目旨在建立一个综合框架，利用多模式网络监测技术，同时监测系统风险和检测系统面临的迫在眉睫的威胁。正在开发的技术将用于监测大量时空数据积累所产生的复杂系统，包括通过物理接近进行的人类接触数据、社交媒体数据以及城市街区凶杀案和国家间冲突等事件数据。在这项工作中得出的基本成果将指导研究在建模和推理的动态网络，并将作为一个基准，为未来的工作。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

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.