Developing a Novel Framework for Analyzing Dynamic Interactions in Movement Data

开发用于分析运动数据中的动态交互的新框架

• 批准号: 1424920
• 负责人: Jennifer Miller
• 金额: $ 26.5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1424920&HistoricalAwards=false
• 关键词: Developing; Novel; Framework; Analyzing; Dynamic

Technological advancements in global positioning systems and related satellite tracking technologies have resulted in significant increases in the availability of highly accurate data about moving objects. These advances, however, have dramatically outpaced the development of appropriate methods for analyzing movement data. This research project will develop a benchmarking framework for measuring and interpreting dynamic interactions based on both temporal and spatial criteria among individual entities such as people or animals. The project will advance the spatial study of movement beyond the identification of the location of individual entities as specific objects and consider interaction as a variable for which a basic unit of observation is a set of locations with time stamps for multiple individual entities. Analyses of interactions, for example, can provide more information about possible attraction and avoidance of individuals that are in the same area at the same time and can be useful for understanding how two individuals interact in the context of disease transmission and social behavior. While this research focuses on animal movement and interactions, the results will be applicable to other moving objects that interact. Project results therefore could potentially be used to study a wide variety of phenomena, including disease spread, information diffusion, crowd movement, and traffic behavior. The project also will promote education and training through support of student research and development of curricular materials. This research will involve the conduct of an extensive comparison of existing dynamic interaction metrics. The investigator will examine their strengths and weaknesses, how they are affected by temporal resolution, spatial uncertainty, and level of interaction. The ability of these metrics to quantify interactions will be assessed using two simulated movement datasets as well as one high-quality real dataset for which the level of interaction is known. This comparison will contribute recommendations and guidelines for research that aims to quantify interactions among animals, people, automobiles, or other moving objects. The results from the comparison also will be used to develop new interaction metrics that will be more appropriate for measuring context- or behavior-specific types of interactions.

全球定位系统和相关卫星跟踪技术的技术进步使得有关移动物体的高精度数据的可用性显着增加。 然而，这些进步已经大大超过了分析运动数据的适当方法的开发速度。 该研究项目将开发一个基准框架，用于根据人或动物等个体实体之间的时间和空间标准来测量和解释动态相互作用。 该项目将推进运动的空间研究，超越将​​单个实体的位置识别为特定对象的范围，并将交互视为一个变量，其中基本观察单位是一组带有多个单个实体时间戳的位置。 例如，交互分析可以提供有关同时位于同一区域的个体可能吸引和回避的更多信息，并且有助于了解两个个体在疾病传播和社会行为的背景下如何交互。 虽然这项研究的重点是动物运动和相互作用，但结果也适用于其他相互作用的运动物体。 因此，项目结果可用于研究各种现象，包括疾病传播、信息传播、人群流动和交通行为。 该项目还将通过支持学生研究和课程材料开发来促进教育和培训。这项研究将涉及对现有动态交互指标进行广泛的比较。 研究者将检查它们的优点和缺点，以及它们如何受到时间分辨率、空间不确定性和交互水平的影响。 这些指标量化交互的能力将使用两个模拟运动数据集以及一个交互级别已知的高质量真实数据集进行评估。 这种比较将为旨在量化动物、人、汽车或其他移动物体之间相互作用的研究提供建议和指南。 比较的结果还将用于开发新的交互指标，该指标将更适合测量特定于上下文或行为的交互类型。