ATD: Sparsity Models for Forecasting Spatio-Temporal Human Dynamics

美国要想继续保持在科技前沿的成就，就需要在信息技术的新研究上投入大量资金，以解决数字记录人类活动所产生的海量数据足迹所带来的最具挑战性的问题。这个项目开发了新的模型和方法，利用稀疏的、不同种类的数据来预测人类在时间和空间上的活动。目标非常笼统，重点放在预测和填补缺失的数据上。这个项目涉及的数据类型的一个例子是来自一个大城市的带有地理标记的Twitter数据以及来自该地区的其他信息丰富的地理空间信息。这个项目结合了数学和人类学方面的资深科学家的专业知识。该项目开发分析工具，以了解各种网络-地理-时间数据集。虽然该项目专注于基础研究，但它具有影响国家安全的巨大潜力。这个为期三年的项目培养博士后、研究生和本科生研究人员。受训者将接受研究方面的培训，以书面和口头形式介绍他们的工作，重点是经过评审的期刊出版物和会议报告。他们还将与未来的雇主建立联系，并在整个培训过程中获得职业建议。该项目侧重于大规模文化、社会和行为过程与导致特定行为表达的情景条件之间的信息技术。这项工作扩展了基于文本的主题建模的一般概念，以处理不同的数据类型集合。该项目开发了通过空间显式主题建模来检测情景概率影响的方法。一个目标是将情景影响分成不同的类别：(A)相对固定的(例如，物理机场在推动机场相关主题方面发挥的空间离散但时间稳定的作用)，(B)间歇性(例如，离散的假期)和(C)短暂的(例如，Foursquare)。另一个目标是时间预测，而第三个目标是填补潜在空间中缺失的信息。研究方法侧重于足够灵活的算法，可以扩展到各种数据集。该工作交织了几个非常有用的大数据模型和算法，包括用于时间信息的自激发点过程模型，用于数据的线性混合模型的软主题模型(如非负矩阵因式分解和潜在Dirichlet分配)，围绕图和图拉普拉斯的总变异最小化建立的硬聚类方法，以及将这些思想结合在一起的数据融合方法，其中研究潜在空间信息来预测和填充缺失信息。

The US continued achievement at the forefront of science and technology requires a significant investment in new research in information technology to tackle the most challenging problems created by the vast data footprint created by digital recording of human activity. This project develops novel models and methods for forecasting human activity in time and space using sparse, heterogeneous data. The goals are very general and are focused on predicting and filling in missing data. An example of the type of data this project addresses would be a year's worth of geotagged Twitter data from a major city along with other informative geospatial information from that region. This project combines expertise of senior scientists in both Mathematics and Anthropology. The project develops analytical tools for understanding a diverse array of cyber-geospatial-temporal datasets. While focused on basic research, the project has tremendous potential to impact national security. This three-year project trains postdocs, graduate students, and undergraduate researchers. The mentees will be trained in research, in presentation of their work in written and spoken formats, with an emphasis on refereed journal publications and conference presentations. They will also be connected to future employers and will be given career advice throughout the length of their training.The project focuses on information technology at the interface between large-scale cultural, social and behavioral processes and the situational conditions that lead to the expression of specific behaviors. This work extends a general conceptualization of text-based topic modeling to handle diverse collections of data types. The project develops methods to detect situational probabilistic effects through spatially-explicit topic modeling. One goal is to organize situational effects into different categories: (a) relatively stationary (e.g., the spatially discrete, but temporally stable role that the physical airport plays in driving airport related topics), (b) intermittent (e.g., discrete holidays) and (c) ephemeral (e.g., Foursquare). Another goal is temporal forecasting while a third goal is filling in missing information from a latent space. The research approach focuses on algorithms that are flexible enough to extend to a variety of datasets. The work interweaves several very useful models and algorithms for large data including self-exciting point process models for temporal information, soft topic modeling such as nonnegative matrix factorization and latent Dirichlet allocation for linear mixture models of data, hard clustering methods built around total variation minimization on graphs and graph Laplacians, and data fusion methods to combine these ideas in which latent space information is studied for forecasting and filling in missing information.