Developing a Novel Framework for Analyzing Dynamic Interactions in Movement Data

全球定位系统和有关卫星跟踪技术的技术进步，大大增加了关于移动物体的高度精确数据的可用性。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发展。该研究项目将开发一个基准框架，用于测量和解释基于时间和空间标准的个体实体（如人或动物）之间的动态相互作用。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，并将相互作用视为一个变量，其中观察的基本单位是一组具有多个单个实体时间戳的位置。例如，对相互作用的分析可以提供更多关于同时在同一地区的个体可能受到吸引和回避的信息，并且有助于理解两个个体在疾病传播和社会行为的背景下如何相互作用。虽然这项研究的重点是动物的运动和相互作用，但结果将适用于其他相互作用的运动物体。因此，项目成果有可能用于研究各种各样的现象，包括疾病传播、信息扩散、人群流动和交通行为。该项目还将通过支持学生研究和开发课程材料来促进教育和培训。这项研究将涉及对现有动态交互度量进行广泛的比较。研究者将研究它们的优点和缺点，它们如何受到时间分辨率、空间不确定性和相互作用水平的影响。将使用两个模拟运动数据集以及一个已知交互水平的高质量真实数据集来评估这些指标量化交互的能力。这种比较将为量化动物、人、汽车或其他运动物体之间的相互作用的研究提供建议和指导。比较的结果还将用于开发新的交互度量标准，这些度量标准将更适合度量特定于上下文或特定于行为的交互类型。

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.