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

全球定位系统和卫星跟踪技术的最新进展使得能够前所未有地收集移动的物体的位置信息。 虽然可以捕获描述人、动物、车辆和其他类型移动物体的运动的高度准确的空间信息，但可用于支持此类数据分析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，不允许映射和量化对象的不确定性运动分布。 虽然传统的时间地理学擅长描述一个物体在一段时间内可能移动的空间范围，但它并没有揭示物体在这个边界内的可能位置。 这在许多实质性领域都是一个重要问题，特别是在野生动物生态学领域，对动物的空间使用特征或活动范围进行量化和绘图是一项关键的管理任务。 该项目以空间统计和时间地理学方面的最新工作为基础，开发了时间地理的概率测量方法，可用于分析跟踪数据。 特别是，几个时间-地理密度估计将开发，以产生连续的概率密度函数跟踪数据。 这些方法的制定将沿着几条轨道进行，其中包括审查适当的采样方案、跟踪间隔、距离加权函数、速度参数规格、移动物体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的标记番鸭（Cairina moschata）收集一组丰富的高频基线数据。 这些数据也将被用作评估新的和现有的动物家域估计方法的基础。 最后，该项目将调整开发的概率时间地理方法，用于网络空间。该项目将推进地理信息科学中关于如何最好地分析移动的物体的运动模式的基本知识。 将产生若干新的空间统计技术。 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。 虽然该项目的应用主要是生态的性质，开发的方法将相关的一些领域，空间参考移动对象数据越来越多地用于其他领域，如犯罪，交通和国土安全。 除了研究成果被纳入PI的课程设置，该项目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.