ATD: Sparsity Models for Forecasting Spatio-Temporal Human Dynamics

美国在科学和技术前沿的持续成就需要对信息技术的新研究进行大量投资，以解决人类活动数字记录所产生的巨大数据足迹所带来的最具挑战性的问题。 该项目开发了新的模型和方法，用于使用稀疏，异构数据预测人类活动的时间和空间。这些目标非常笼统，侧重于预测和填补缺失的数据。该项目处理的数据类型的一个例子是来自一个主要城市的一年的地理标记Twitter数据沿着来自该地区的其他信息地理空间信息。该项目结合了数学和人类学高级科学家的专业知识。 该项目开发分析工具，以了解各种各样的网络-地理空间-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。学员将接受研究培训，以书面和口头形式展示他们的工作，重点是经过评审的期刊出版物和会议演讲。他们还将与未来的雇主建立联系，并将在整个培训期间获得职业建议，该项目侧重于大规模文化、社会和行为过程与导致具体行为表达的情境条件之间的界面上的信息技术。这项工作扩展了基于文本的主题建模的一般概念化，以处理不同的数据类型的集合。该项目开发的方法来检测情景概率的影响，通过空间明确的主题建模。一个目标是将情境效应组织成不同的类别：（a）相对静止（例如，物理机场在驱动机场相关主题中所起的空间离散但时间稳定的作用），（B）间歇性的（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，而第三个目标是从潜在空间中填充缺失的信息。研究方法侧重于足够灵活的算法，以扩展到各种数据集。这项工作交织了几个非常有用的大数据模型和算法，包括时间信息的自激点过程模型，数据线性混合模型的非负矩阵分解和潜在Dirichlet分配等软主题建模，围绕图和图拉普拉斯算子的总变差最小化构建的硬聚类方法，数据融合方法是将这些思想结合起来，研究潜在的空间信息，用于预测和填补缺失信息。

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.