RAPID: Using location-based big-data to model people's mobility patterns during the COVID-19 outbreak

COVID-19在美国的爆发为研究人员提供了一个重要的机会来研究快速蔓延的流行病对人类流动性的影响。本研究探讨如何使用被动收集的位置数据的大型数据集来衡量快速发展的COVID-19疫情对人员集体流动的影响。它考察了一个州内的地点如何应对公共政策的实施和关键公共信息的发布时间。对人员流动模式的详细了解可以帮助公职人员确定热点和严重孤立的人口，并了解那些继续为工作或其他目的旅行的群体。这项研究有助于改善公众对紧急情况的反应，并有助于弥合不同利益相关者的缓解战略。人们如何应对快速蔓延的全球流行病的详细知识非常有限，我们对这些反应的了解主要是在小范围内。这项研究将使用在大流行期间通过使用基于位置的应用程序被动收集的近实时基于位置的数据集。该项目将开发可扩展的、基于位置的大型算法，以提取行程并研究整个大流行期间流动模式的演变，并识别不同的流动模式。我们将开发基于map-reduce的分布式算法，以基于大的基于位置的数据来扩大移动性度量计算，并开发熵度量来捕获与旅行模式相关的时变特征，并设计策略来纠正位置数据中可能存在的偏差。这项研究的方法和结果将有助于了解在影响社区的其他灾害期间的流动性，例如严重的洪水，以了解由于灾害和政策指令的强制性而导致的旅行如何改变。该奖项反映了NSF的法定使命，并被认为值得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来支持。

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to study the impacts of a rapidly expanding pandemic on human mobility. This research investigates how to measure changes in collective movement of people in response to the fast-evolving COVID-19 outbreak using large datasets of passively collected location data. It examines how locations within a state respond to public policy implementation and times of critical public messaging. Detailed knowledge on movement patterns of people can help public officials identify hotspots and critically isolated populations, as well as shed light on those groups who continue to travel for work or other purposes. This research contributes to improving the public response to an emergency and contributes to bridging different stakeholder mitigation strategies.Detailed knowledge of how people respond to a fast-spreading global pandemic is very limited and our understanding of these responses is mostly for small areas. This research will use a near real-time location-based dataset passively collected through the use of location-based apps during the period of pandemic. The project will develop scalable, big location-based algorithms to extract trips and examine the evolution of mobility patterns throughout the pandemic, and identify different mobility patterns. We will develop map-reduce based distributed algorithms to scale up mobility measure calculations based on the big location-based data as well as develop entropy measures to capture the time-varying characteristics associated with the travel patterns, and design strategies to correct biases that may be present in the location data. The methods and results of this research will be useful for understanding mobility during other hazards that affect communities, such as severe flooding to understand how travel is changed as a result of imperatives stemming from both the hazard and policy directives.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.