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.

该研究项目将推进计算方法，以追踪和表征移动网络中代理之间的相互作用和关键相遇。网络的例子包括城市中的人们，生态系统中的一群动物或一群船只。尽管跟踪技术取得了进步，但计算运动分析方法在大型移动网络中动态相互作用模式的定量和表征仍然有限。十年变成2020年代，社会通过密切接触和或个人之间的滞后相互作用见证了SARS-COV-2通过呼吸液滴的广泛传播。这导致了一套前所未有的非药物干预措施，包括数字触点跟踪以减轻COVID-19的传播。但是，当前的技术在追踪和检测健康和潜在感染的个体之间的关键或风险遭遇或暂时滞后的相互作用效率低下。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。结果将增强接触追踪技术，以检查潜在的人类对健康风险或传染性药物的接触。更一般而言，要开发的方法将使科学家能够在人类和动物网络中建模社会行为。该项目将创建开放式/开源分析工具，这将使研究人员，教育工作者和地理和其他领域的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来开发和评估新颖的上下文感知时间地理分析方法，以（1）痕量动态互动并测量使用大型运动数据集的个人之间的相遇持续时间和频率，以及（2）与互动相互作用，并识别出差异或差异或识别差异或差异或识别差异或差异。该研究将研究三个总体研究问题：（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运动估算接触？ （2）鉴于大型运动观察，我们如何有效地有效地追踪和识别个人之间的“风险”或“有趣”的相遇？ （3）可以使用互动分析来了解人类和动物的社交网络中的集体运动模式？将开发一系列案例研究和开放分析工具，以证明使用人和动物的实际GPS观察结果来证明分析框架的功效。这项研究中要开发的分析方法将是可以推广的，可以概括地了解社会和生态系统中的互动，对人类社会行为的新知识以及基石物种的竞争。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。