ATD: Sparsity Models for Forecasting Spatio-Temporal Human Dynamics

ATD：预测时空人类动力学的稀疏模型

• 批准号: 1737770
• 负责人: Andrea Bertozzi
• 金额: $ 56.45万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737770&HistoricalAwards=false
• 关键词: ATD; Sparsity; Models; Forecasting; Spatio

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.

美国继续在科学技术的最前沿取得成就，需要对信息技术的新研究进行大量投资，以解决由数字记录人类活动创造的巨大数据足迹所带来的最具挑战性的问题。 该项目开发了使用稀疏，异构数据在时空预测人类活动的新型模型和方法。目标非常笼统，专注于预测和填写丢失的数据。该项目所说的数据类型的一个示例将是从一个主要城市中获得的地理标签Twitter数据，以及该地区其他信息的地理空间信息。该项目结合了数学和人类学方面的高级科学家的专业知识。 该项目开发了分析工具，以了解各种网络地理空间数据集。该项目虽然专注于基础研究，但具有影响国家安全的巨大潜力。这个为期三年的项目培训了博士后，研究生和本科研究人员。受训者将接受研究的培训，以书面和口头形式的作品介绍他们的作品，重点是审理的期刊出版物和会议演讲。他们还将与未来的雇主联系在一起，并将在整个培训的整个培训期间得到职业建议。该项目侧重于大型文化，社会和行为过程与导致特定行为表达的情况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，以处理数据类型的各种集合。该项目开发了通过空间解释的主题建模来检测情况概率效应的方法。一个目标是将情境影响组织为不同的类别：（a）相对固定（例如，实体机场在驾驶机场相关的主题中的空间离散但在时间上稳定的角色），（b）间歇性（例如离散的假期）和（c）短暂的（例如，c）。另一个目标是时间预测，而第三个目标是填写潜在空间中缺少的信息。研究方法着重于足够灵活的算法，以扩展到各种数据集。这项工作与大量数据的几个非常有用的模型和算法交织在一起，包括用于时间信息的自我激发点过程模型，软主题建模，例如非负矩阵分解和用于数据的线性混合模型的潜在dirichlet分配，围绕图形和图形的laplacians和图形的信息构建的，围绕这些信息和数据融合的空间构建的硬聚类混合方法，以将这些方法组合成曲目，并将其组合成均匀的信息。缺少信息。

项目成果

Alternative SIAR models for infectious diseases and applications in the study of non-compliance

• DOI: 10.1142/s0218202522500464
• 发表时间: 2022-11-04
• 期刊: MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES
• 影响因子: 3.5
• 作者: Bongarti，Marcelo;Galvan，Luke Diego;Bertozzi，Andrea L.
• 通讯作者: Bertozzi，Andrea L.

Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial

• DOI: 10.1080/2330443x.2018.1438940
• 发表时间: 2018-02-08
• 期刊: STATISTICS AND PUBLIC POLICY
• 影响因子: 1.6
• 作者: Brantingham, P. Jeffrey;Valasik, Matthew;Mohler, George O.
• 通讯作者: Mohler, George O.

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

多元时空霍克斯过程与网络重建

• DOI: 10.1137/18m1226993
• 发表时间: 2019
• 期刊: SIAM Journal on Mathematics of Data Science
• 影响因子: 3.6
• 作者: Yuan, Baichuan;Li, Hao;Bertozzi, Andrea L.;Brantingham, P. Jeffrey;Porter, Mason A.
• 通讯作者: Porter, Mason A.

Competitive dominance, gang size and the directionality of gang violence

• DOI: 10.1186/s40163-019-0102-3
• 发表时间: 2019-08-30
• 期刊: CRIME SCIENCE
• 影响因子: 6.1
• 作者: Brantingham, P. Jeffrey;Valasik, Matthew;Tita, George E.
• 通讯作者: Tita, George E.

A year in Madrid as described through the analysis of geotagged Twitter data

• DOI: 10.1177/2399808318764123
• 发表时间: 2019-11
• 期刊: Environment and Planning B: Urban Analytics and City Science
• 作者: Travis R. Meyer;D. Balagué;M. Camacho-Collados;Hao Li;Kathy Khuu;P. Brantingham;A. Bertozzi