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

改进运动数据中时空交互模式的追踪和情境化方法

• 批准号: 2217460
• 负责人: Somayeh Dodge
• 金额: $ 23万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217460&HistoricalAwards=false
• 关键词: Advancing; Methods; Trace; Contextualize; Space

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.

这个研究项目将推进计算方法来跟踪和表征代理之间的互动和关键遭遇在移动的网络。网络的例子包括城市中的人，生态系统中的一群动物，或一队船只。尽管在跟踪技术的进步，计算运动分析方法仍然有限的量化和表征的动态互动模式在大型移动的网络。随着这十年转向2020年代，社会见证了SARS-CoV-2通过呼吸道飞沫通过密切接触和/或个体之间的滞后互动广泛传播。这导致了一系列前所未有的非药物干预措施，包括数字接触者追踪，以减缓COVID-19的传播。然而，目前的技术对于追踪和检测健康个体和潜在感染个体之间的关键或危险接触或暂时滞后的相互作用是低效的。使用运动观察，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。研究结果将加强接触者追踪技术，以检查人类可能接触的健康风险或传染性物质。更一般地说，即将开发的方法将使科学家能够模拟人类和动物网络中的社会行为。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。该研究将通过优化的计算算法开发和评估新颖的上下文感知时间-地理分析方法，以（1）使用大型移动数据集跟踪动态交互并测量个体之间相遇的持续时间和频率，以及（2）将相遇，并发交互，和滞后的交互，以更好地识别关键或危险的联系人。本研究将探讨三个首要的研究问题：（1）我们如何最好地利用统计方法和时间地理方法，更好地估计通过运动的接触？(2)鉴于大的运动观察，我们如何能够有效地和高效地跟踪和识别个体之间的“危险”或“有趣”的遭遇？(3)互动分析可以用来理解人类和动物的社交网络中的集体运动模式吗？将开发一套案例研究和开放式分析工具，利用对人和动物的真实的全球定位系统观测来证明分析框架的效力。本研究开发的分析方法将被推广到理解社会和生态系统的相互作用，为人类的社会行为和关键物种的竞争提供新的知识。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。