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Attribute trajectory analysis: a framework to analyse attribute changes using trajectory analysis techniques

机译:属性轨迹分析:使用轨迹分析技术分析属性变化的框架

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Trajectory analysis has attracted growing attention in the research field of geography. Beyond traditional moving object trajectories, another type of trajectory exists in which the coordinates are object attributes rather than geographical coordinates. In this paper, a framework to analyse these so-called attribute trajectories is proposed that uses four techniques typically employed in the analysis of moving object trajectories: the Reeb graph, the similarity matrix, the convoy and the mega-convoy. The Reeb graph provides the ability to visualise the temporal dynamics of attribute similarities. The similarity matrix is a supplement of the Reeb graph whose purpose is to visualise the pairwise similarities among the attributes. Moreover, the similarity matrix forms a basis for clustering. The convoy highlights objects whose attributes remain similar for a sufficiently long period. The mega-convoy reduces the number of convoys and reveals their evolutionary histories by merging overlapping convoys. A small real-world meteorological dataset is used as an example to illustrate the attribute trajectory analysis framework and the techniques. This paper aims to form a starting point for applying trajectory analysis techniques in many research fields.
机译:轨迹分析在地理学研究领域引起了越来越多的关注。除了传统的运动对象轨迹之外,还存在另一种类型的轨迹,其中坐标是对象属性而不是地理坐标。在本文中,提出了一个分析这些所谓的属性轨迹的框架,该框架使用了通常在运动对象轨迹分析中使用的四种技术:Reeb图,相似矩阵,车队和巨型车队。 Reeb图提供了可视化属性相似性的时间动态的能力。相似度矩阵是Reeb图的补充,其目的是可视化属性之间的成对相似度。而且,相似度矩阵构成聚类的基础。护卫队突出显示其属性在足够长的时间内保持相似的对象。大型车队通过合并重叠的车队减少了车队的数量并揭示了他们的进化历史。以一个小型的现实世界气象数据集为例,说明属性轨迹分析框架和技术。本文旨在为在许多研究领域中应用轨迹分析技术提供一个起点。

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