Collaborative Research: Developing a Statistical Time Geography for Analyzing Animal Movements and Interactions

全球定位系统（GPS）和卫星跟踪技术的最新进展使移动物体的位置信息收集成为可能。虽然有可能捕捉到描述人、动物、车辆和其他类型运动物体运动的高度精确的空间信息，但支持分析这些数据的理论和方法仍然有限。在地理信息科学中，被称为“时间地理学”的领域是运动模式分析的基础知识体系。然而，正如目前理论化和理解的那样，传统的时间地理学有一个关键的缺点：它是基于空间的离散表示，不允许对物体的运动分布进行不确定性的映射和量化。虽然传统的时间地理学擅长于描述一个物体在一段时间内可能运动的空间范围，但它并没有揭示物体在这个边界内的位置的可能性。这是许多实质性领域的一个重要问题，特别是在野生动物生态学中，对动物的空间利用特征或活动范围进行量化和绘制是一项关键的管理任务。在空间统计和时间地理学最新工作的基础上，本项目开发了可用于分析跟踪数据的时间地理学概率度量。特别是，为了从跟踪数据生成连续的概率密度函数，将开发几个时间地理密度估计器。这些方法的制定将沿着几个轨道进行，包括检查适当的抽样方案、跟踪间隔、距离加权函数、速度参数规范、移动对象不活动和位置不确定性。为了支持这些新方法的发展，研究人员将从佛罗里达州标记的番鸭（Cairina moschata）收集一组丰富的高频基线数据。这些数据也将作为评估新的和现有的动物活动范围估计方法的基础。最后，该项目将把已开发的概率时间地理方法应用于网络空间。该项目将推进地理信息科学的基础知识，即如何最好地分析移动物体的运动模式。将产生几种新的空间统计技术。将收集描述动物运动的基线数据集，并与其他研究人员共享。虽然项目应用主要是生态性质的，但开发的方法将与许多领域相关，因为空间参考移动物体数据越来越多地用于其他领域，如犯罪、交通和国土安全。除了将研究成果纳入PI的课程外，该项目还将直接支持至少两名研究生。

Recent advances in global positioning system (GPS) and satellite tracking technologies have enabled unprecedented collection of locational information for mobile objects. Though it is possible to capture highly accurate spatial information describing the movements of people, animals, vehicles, and other types of moving objects, the theory and methods available to support analysis of such data remain limited. Within geographic information science, the area known as "time geography" is the foundational body of knowledge for movement pattern analysis. However, as it is currently theorized and understood, traditional time geography suffers from one critical shortcoming: it is based on a discrete representation of space that does not permit the mapping and quantification of an object's movement distribution with uncertainty. While traditional time geography is adept at describing the spatial extent of an object's possible movement over a period of time, it does not reveal the likelihood of where the object was located within this boundary. This is an important question in many substantive domains, particularly in wildlife ecology, where quantifying and mapping the space-use characteristics, or home ranges, of animals is a key management task. Building on recent work in spatial statistics and time geography, this project develops probabilistic measures of time geography that can be used to analyze tracking data. In particular, several time-geographic density estimators will be developed in order to generate continuous probability density functions from tracking data. Formulation of these methods will proceed along several tracks that include examining appropriate sampling schemes, tracking intervals, distance-weighting functions, velocity parameter specifications, moving object inactivity, and positional uncertainty. To support the development of these new methods, the investigators will collect a rich set of high-frequency baseline data from tagged Muscovy ducks (Cairina moschata) in the State of Florida. This data will also be used as a basis for evaluating new and existing methods of animal home range estimation. Lastly, the project will adapt the developed probabilistic time geographic methods for use in network space. The project will advance basic knowledge in geographic information science regarding how the movement patterns of mobile objects can best be analyzed. Several new spatial statistical techniques will be produced. A baseline dataset describing animal movements will be collected and shared with the other researchers. Although the project applications primarily are ecological in nature, developed methods will be relevant to a number of fields, as spatially-referenced moving object data are increasingly used in other areas such as crime, transportation, and homeland security. In addition to the research outcomes being incorporated into the PI's course offerings, the project will directly support at least two graduate students.