Advancing Methods to Trace and Contextualize Space-Time Interaction Patterns in Movement Data

这项研究项目将推进计算方法，以跟踪和表征移动网络中代理之间的交互和关键遭遇。网络的例子包括城市中的人、生态系统中的一群动物或船队。尽管跟踪技术取得了进步，但计算运动分析方法在量化和描述大型移动网络中的动态交互模式方面仍然受到限制。随着十年进入本世纪20年代，社会见证了SARS-CoV-2通过密切接触和/或个人之间滞后的互动通过呼吸道飞沫广泛传播。这导致了一系列史无前例的非药物干预，包括数字接触者追踪，以缓解新冠肺炎的传播。然而，目前的技术在跟踪和检测关键或危险的遭遇或健康和潜在感染者之间的临时滞后互动方面效率低下。利用移动观测，该项目将提供有关移动代理之间相互作用的数据驱动结果。这一结果将加强接触者追踪技术，以检查人类可能接触到的健康风险或传染病病原体。更广泛地说，即将开发的方法将使科学家能够对人类和动物网络中的社会行为进行建模。该项目将创建开放获取/开源分析工具，使地理和其他领域的研究人员、教育工作者和学生更容易获得空间数据科学。该项目将为研究生提供培训和研究经验。这项研究将通过优化的计算算法开发和评估新的情境感知时间-地理分析方法，以(1)跟踪动态交互，并使用大型移动数据集测量个人之间相遇的持续时间和频率，以及(2)将相遇、并发交互和滞后交互关联起来，以更好地识别关键或危险接触。这项研究将调查三个主要的研究问题：(1)我们如何最好地利用统计方法和时间地理方法来更好地估计通过移动进行的接触？(2)考虑到大量的移动观察，我们如何有效和高效地跟踪和识别个人之间的“危险的”或“有趣的”接触？(3)交互分析能否被用来理解人类和动物的社会网络中的集体移动模式？将开发一套案例研究和开放式分析工具，以使用对人和动物的真实全球定位系统观测来展示分析框架的效力。这项研究中开发的分析方法将被推广到理解社会和生态系统中的相互作用，为人类的社会行为和关键物种的竞争提供新的知识。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will advance computational approaches to trace and characterize interactions and critical encounters between agents in a mobile network. Examples of networks include people in a city, a group of animals in an ecosystem, or a fleet of vessels. Despite the advances in tracking technologies, computational movement analysis methods remain limited in quantification and characterization of dynamic interaction patterns in large mobile networks. As the decade turned to the 2020s, society witnessed the widespread transmission of SARS-CoV-2 through respiratory droplets via close contacts and or lagged interactions between individuals. This led to a set of unprecedented non-pharmaceutical interventions including digital contact tracing to mitigate the spread of the COVID-19. However, current techniques are inefficient for tracing and detecting critical or risky encounters or temporally lagged interactions between healthy and potentially infected individuals. Using movement observations, this project will provide data-driven results about interactions between moving agents. The results will enhance contact-tracing technologies for examining potential human exposure to health risks or infectious agents. More generally, the methods to be developed will enable scientists to model social behaviors in human and animal networks. The project will create open-access/open-source analytical tools which will make spatial data science more accessible to researchers, educators, and students in geography and other fields. The project will provide training and research experiences for graduate students.This research will develop and evaluate novel context-aware time-geographic analytical methods through optimized computational algorithms to (1) trace dynamic interactions and measure the duration and frequency of encounters between individuals using large movement data sets, and (2) to contextualize encounters, concurrent interactions, and lagged interactions to better identify critical or risky contacts. The research will investigate three overarching research questions: (1) How can we best leverage statistical approaches and time geographic methods for better estimation of contact through movement? (2) Given large movement observations, how can we effectively and efficiently trace and identify 'risky' or 'interesting' encounters between individuals? (3) Can interaction analytics be used to understand collective movement patterns in social networks of humans and animals? A set of case studies and open analytical tools will be developed to demonstrate the efficacy of the analytical framework using real GPS observations of people and animals. The analytical methods to be developed in this study will be generalizable to understanding interaction in both social and ecological systems, contributing new knowledge about social behavior of humans and competition of keystone species.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.