ATD: Quantifying Human Mobility using Topological and Time-Frequency Analysis

现代应用程序中的海量数据在存储、处理和挖掘数据方面带来了挑战。 当数据涉及人类行为并涉及空间和时间组件时，这些挑战尤其困难。 该项目旨在应对这些数据集带来的挑战，以更好地了解人类动态和流动性。该项目有可能以多种方式造福社会。首先，通过开发更准确和有效的数学算法来处理时空数据，项目成果可以改进异常和威胁检测。第二，该项目将通过培训研究生和博士后助理、课程开发和外联活动，促进STEM劳动力的发展。第三，该项目的结果将为分析描述人类行为的数据集的新算法的整合提供途径。第四，该项目将解决研究的伦理、法律的和社会影响，特别是社会对数据收集和分析的关注，以及处理这些数据所产生的不同影响。该项目旨在开发新的成果和算法工具包，适用于各种时空数据集，用于异常检测的拓扑数据分析。这些新的结果和算法的重点是结合拓扑数据分析的空间分量的数据集和时间的时间分量的表示表示的时频分析。 新算法将专注于体积交通数据和美国人口普查数据中的异常检测，但将推广到其他时空数据集。拟议的研究还将解决算法的不公平性和有偏见的数据集，制定战略，以减轻我们的新算法可能产生的不同影响。该项目将在多个尺度上模拟跨越政治边界的人员流动，以识别和预测住宅的不稳定性。 该项目还将解决社会对自动决策技术（如人工智能）在人类动力学背景下的部署的关注。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The immense amount of data at hand in modern applications creates challenges in storing, processing, and mining the data. These challenges are particularly difficult when the data concerns human behavior and involves both spatial and temporal components. This project aims to address challenges posed by such datasets in an effort to better understand human dynamics and mobility. This project has the potential to benefit society in several ways. First, by developing more accurate and efficient mathematical algorithms for processing spatiotemporal data, the project results can lead to improved anomaly and threat detection. Second, the project will contribute to STEM workforce development through training of graduate students and postdoctoral associates, curriculum development, and outreach activities. Third, results of the project will provide avenues for incorporation of new algorithms for analyzing datasets that describe human behavior. Fourth, the project will address the ethical, legal, and societal impacts of the research, especially societal concerns regarding the collection and analysis of data as well as disparate impacts resulting from the processing of that data.This project aims to develop new results and a toolkit of algorithms, applicable to a wide variety of spatiotemporal datasets, in topological data analysis for anomaly detection. These new results and algorithms focus on combining topological data analysis for the representation of spatial components of datasets and time-frequency analysis for the representation of the temporal components. The new algorithms will focus on anomaly detection in volumetric traffic data and US census data, but will be generalizable to other spatiotemporal datasets. The proposed research will also address algorithmic unfairness and biased datasets, developing strategies to mitigate disparate impacts that may occur as a result of our novel algorithms. The project will model human mobility across political boundaries across multiple scales, in order to identify and predict residential instability. The project will also address societal concerns regarding the deployment of automated decision making technologies--such as artificial intelligence--in the context of human dynamics.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.