The immense amount of data at hand in modern applications creates challenges in storing, processing, and mining the data. Furthermore, datasets often are distributed, so that no single data center has all relevant data available to analyze. New mathematical and statistical algorithms are needed for analyzing distributed databases, particularly datasets that consist of spatiotemporal data. This project aims to address challenges posed by such massive datasets in an effort to better understand human dynamics. This project has the potential to benefit society in several ways. First, by developing more accurate and efficient mathematical algorithms for processing distributed 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, curriculum development, and outreach activities. Third, results of the project will provide avenues for incorporation of new algorithms for anomaly detection to public entities and other stakeholders, particularly in the context of transportation networks and food safety. Fourth, the project will address the ethical, legal, and societal impacts of the research, especially societal concerns regarding the collection and analysis of data.