ATD: Collaborative Research: Algorithms and Data for High-Frequency, Real-Time Anomaly Detection

来自互联网和移动通信的迅速增长的数字数据可以为人们提供低成本和高分辨率的视角，了解健康和社会经济等领域的人类行为。来自互联网和移动连接来源的个人生成的数据提供了独特的洞察力，捕捉到了人类行为的各个方面，这些方面将是繁重的，或者不可能通过其他数据源进行量化。此外，这些数据通常是实时可用的，并可以链接到特定的位置。这一研究项目解决了使用这种非结构化时空数据集来检测异常事件所固有的统计学挑战。这类数据需要新的统计方法来预处理并从数据中提取表格，以便可靠地用于事件检测。此外，数据的连续性意味着什么构成异常行为取决于时间尺度和潜在事件的类型。该项目旨在开发1)从社交媒体数据中生成相关特征的方法，这些方法解释了数据的观察性质，并可用于现实世界行为的时空模型，以及2)新的多尺度方法来建模依赖结构，使用新信息不断改进模型并准确评估异常情况。该项目中的方法既适合并利用了社交媒体数据的连续性和观察性。这项研究将在经验数据集上得到验证，展示出实用价值。预计结果将适用于进一步使用公开可用的地理空间数据源，并了解无法通过其他手段衡量的人类动态。

The rapidly burgeoning amount of digital data from Internet and mobile-enabled communications can offer low-cost and high-resolution views into human behavior across areas such as health and socio-economics. Personally-generated data from Internet and mobile-connected sources offer unique insight, capturing aspects of human behavior that would be taxing or impossible to quantify through other data sources. Moreover, the data is often available in real time and can be linked to specific locations. This research project addresses the statistical challenges inherent in using such unstructured spatio-temporal data sets for detection of anomalous events.Such data requires new statistical approaches to pre-process and extract forms from the data that can reliably be used for event detection. Further, the continuous nature of the data means that what constitutes anomalous behavior depends on the time-scale and on the type of underlying event. This project aims to develop 1) approaches for generating relevant features from social media data that account for the observational nature of the data and can be used in spatio-temporal models of real-world behavior and 2) a new multi-scale approach to modeling dependence structures that uses new information to continuously refine the model and accurately assess anomalies. The approach in this project is both suited to and harnesses the continuous and observational nature of social media data. The research will be validated on empirical data sets, demonstrating practical utility. It is anticipated that the results will be applicable to further the use of publicly-available geospatial data sources and understand human dynamics that are not measurable through other means.