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首页> 外文期刊>IEEE transactions on visualization and computer graphics >COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series
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COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

机译：COPE：空间时间序列中共现模式的交互式探索

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摘要

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: discover temporal relationship patterns between event locations, i.e., repeated cases when there is a specific temporal relationship (same time, before, or after) between events occurring at two locations. This can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.
机译：空间时间序列是许多领域（如经济统计和环境科学）处理的常见数据类型。已有许多研究着眼于发现和分析时间序列中的各种事件。术语“事件”是指由连续属性值形成的特定模式的重大变化或出现。我们将重点放在事件分析的进一步步骤上：发现事件位置之间的时间关系模式，即在两个位置发生的事件之间存在特定时间关系（相同时间，之前或之后）的重复情况。这可以为理解事件的形成和传播机制以及空间位置之间的相互依赖性提供重要线索。我们提出了一种视觉探索框架COPE（共现模式探索），该框架允许用户从数据中提取感兴趣的事件并检测其中的各种共现模式。进行了案例研究和专家审查，以使用两个真实的数据集验证COPE的有效性和可扩展性。

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