Developing a Novel Framework for Analyzing Dynamic Interactions in Movement Data

全球定位系统和相关卫星跟踪技术的技术进步大大增加了有关移动物体的高精度数据的可获得性。然而，这些进展大大超过了分析运动数据的适当方法的发展。这一研究项目将开发一个基准框架，用于根据时间和空间标准衡量和解释人或动物等个体之间的动态互动。该项目将推动对运动的空间研究，超越将单个实体确定为具体物体的位置，并将相互作用视为一个变量，其基本观察单位是一组具有多个单独实体的时间戳的位置。例如，对相互作用的分析可以提供更多关于可能吸引和避免同时在同一地区的个人的信息，并有助于理解两个人在疾病传播和社会行为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移动物体。因此，项目结果可能被用于研究各种各样的现象，包括疾病传播、信息传播、人群运动和交通行为。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的动态交互度量进行广泛的比较。研究人员将检查它们的优势和劣势，以及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。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已知交互级别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指标，这些指标将更适合于衡量特定于环境或行为的交互类型。

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.