EAGER: Spatiotemporal Big Data Analysis to Understand COVID-19 Effects

COVID-19大流行影响了公共卫生，造成大量死亡，并使失业率上升至历史高位，破坏了经济。该项目的目标是研究新的时空大数据的潜力，以帮助识别COVID-19相关的地理模式，例如人群长期访问，重叠时间以及往返热点的地点。这些模式引起了政策制定者和公共卫生研究人员的极大兴趣，但在传统的移动数据集中很难找到，例如不频繁的旅行调查和城市公路交通数据。示例时空大数据包括最近为COVID-19研究开放的移动的设备的受隐私保护的聚合位置轨迹。如果成功，其结果将为疾病传播模型和政策干预提供信息，以拯救生命并安全地重新开放经济。该项目预计将导致多项数据科学和计算机科学创新。首先，它将定义和量化hangout-venue，这是一种新的时空模式家族，模拟了许多重叠的长期访问的地方。例子包括提供全方位服务的餐厅，这些餐厅有许多长期访问，但不包括有限服务的餐厅，这些餐厅大多只有短期访问。其次，它将探索新的兴趣度量，不仅区分模式（例如，全方位服务的餐馆）和非模式（例如，有限服务的餐馆），而且还支持基于诸如反单调的性质的计算上有效的算法的设计。第三，它将设计新颖的和可扩展的算法，用于分析大数据集（数十TB）的聚会场所。第四，它将调查选择偏差和噪音的影响，从不同的隐私计划。这些结果有可能用新的模式家族（例如，hangout-venue），并提高对选择偏差和由差分隐私方案添加的噪声对模式挖掘方法及其结果的影响的理解。此外，该项目将与公共卫生研究人员和决策者密切合作，共同产生知识。研究结果有可能通过利用新兴的时空大数据来改变对公共交通的理解，从而建立疾病传播动力学模型。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 COVID-19 pandemic has impacted public health with a large number of mortalities and ravaged the economy by increasing unemployment to a historically high level. The goal of this project is to investigate the potential for novel spatiotemporal big data to assist in identifying COVID-19 related geographic patterns, such as locations where groups of people visit for long, overlapping times, and travel to and from hotspots. Such patterns are of great interest to policy-makers and public health researchers, but are difficult to find in traditional mobility datasets such as infrequent travel surveys and urban highway traffic data. Example spatiotemporal big data include privacy-protected aggregated location traces of mobile devices that have recently been opened for COVID-19 research. If successful, the results will inform disease spread models and policy-interventions to save lives and reopen the economy safely.This project is expected to result in multiple data science and computer science innovations. First, it will define and quantify hangout-venues, a novel spatiotemporal pattern family modeling the places with many overlapping long visits. Examples include full-service dine-in restaurants which have many long visits, but not limited-service restaurants which mostly have short visits. Second, it will probe new interest measures to not only distinguish between patterns (e.g., full-service restaurants) and non-patterns (e.g., limited-service restaurants) but also support the design of computationally efficient algorithms based on properties such as anti-monotone. Third, it will design novel and scalable algorithms for analyzing the large (tens of terabytes) dataset for hangout-venues. Fourth, it will investigate the impact of selection bias and noise from differential privacy schemes. The results have the potential to transform data science knowledge with novel pattern families (e.g., hangout-venue) and improve the understanding of the impact of selection bias and noise added by differential privacy schemes on pattern mining methods and their results. Furthermore, the project will co-produce knowledge in close collaboration with public health researchers and policymakers. The results have the potential to transform the understanding of the public mobility for modeling disease transmission dynamics by leveraging the emerging spatiotemporal big data.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.