Visual Analytics for Understanding Spatial Situations from Episodic Movement Data
Visual Analytics for Understanding Spatial Situations from Episodic Movement Data
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DOI：
10.1007/s13218-012-0177-4
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
N. Andrienko;G. Andrienko;Hendrik Stange;T. Liebig;D. Hecker
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作者：
N. Andrienko;G. Andrienko;Hendrik Stange;T. Liebig;D. Hecker
现代数据采集技术的不断进步导致有关移动物体的地理参考数据量迅速增长，并出现了新的数据类型。我们将情节运动数据定义为一种新的复杂数据类型，在与数据分析相关的研究领域中需要考虑。在偶发运动数据中，位置测量可能被大的时间间隔分开，其中运动对象的位置是未知的并且不能可靠地重建。现有的许多运动分析方法都是针对具有精细时间分辨率的数据而设计的，不能应用于不连续的轨迹。我们提出了一种方法，利用可视化分析方法来探索和理解的时空聚合的时空变化的情景运动数据的空间情况。这些情况是根据不同地方的移动物体的存在和地方之间的流动（集体运动）来定义的。该方法结合了交互式视觉显示和空间情况聚类，并以蓝牙传感器采集的真实的数据集为例进行了说明。
Continuing advances in modern data acquisition techniques result in rapidly growing amounts of geo-referenced data about moving objects and in emergence of new data types. We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilising Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data by means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) between the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors.