Scalable Analysis of Movement Data for Extracting and Exploring Significant Places
Scalable Analysis of Movement Data for Extracting and Exploring Significant Places
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DOI：
10.1109/tvcg.2012.311
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发表时间：
2013-07-01
影响因子：
5.2
通讯作者：
Wrobel, Stefan
中科院分区：
文献类型：
--
作者：
Andrienko, Gennady;Andrienko, Natalia;Wrobel, Stefan
以地点为导向的运动数据分析，即记录运动物体的轨迹，包括寻找某些类型的运动事件反复发生的感兴趣的地点，调查这些地方事件发生的时间分布，可能还包括这些地方的其他特征和它们之间的联系。对于这类问题，我们提出了一个可视化分析过程，包括四个主要步骤：1)从轨迹中提取事件；2)基于事件聚类的相关地点提取；3)事件或轨迹的时空聚合；4)汇总数据分析。所有的步骤都可以以一种可扩展的方式来完成，相对于所分析的数据量；因此，该过程不受计算机RAM大小的限制，可以应用于非常大的数据集。我们通过两个需要在不同空间尺度上分析的现实世界问题的例子来演示该程序的使用。
Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.