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A Multiscale Approach for Spatio-Temporal Outlier Detection

机译:时空离群值检测的多尺度方法

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摘要

A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio-temporal domain and define a spatial-temporal outlier (ST-outlier) to be a spatial-temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST-outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial-temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST-outliers by evaluating the change between consecutive spatial and temporal scales. A four-step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST-outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study.
机译:空间离群值是空间参考对象,其主题属性值与其空间邻域中的其他空间参考对象的主题属性值显着不同。它代表的对象与其邻域有很大不同,即使它与总体人口可能没有显着不同。在这里,我们将这个概念扩展到时空域,并将时空离群(ST-outlier)定义为时空对象,其主题属性值在其空间或空间上与其他时空参考对象的主题属性值明显不同/和时间街区。对ST离群值的识别可以导致发现意外的,有趣的和隐式的知识,例如局部不稳定性或变形。最近已经提出了许多检测空间离群值的方法,但是很少讨论如何检测时间离群值或时空离群值。在本文中,我们提出了一种通过评估连续空间和时间尺度之间的变化来检测ST离群值的多尺度方法。提出了一种由分类,聚合,比较和验证组成的四步过程,以解决用于地理信息的异常检测的地理现象的语义和动态特性。实际的沿海地貌研究证明了该方法的有效性。

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