ReAD：一种基于动态划分的区域异常检测框架
ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition
摘要：
城市数据中异常区域的检测是一个重要的研究课题。然而，据我们所知，以前的方法设计的时空异常是基于道路或网格的，这通常会导致数据稀疏性问题，影响检测结果。针对上述问题，提出了一种动态区域划分方法.此外，我们提出了一个无监督的区域异常检测框架（ReAD）检测异常区域的任意形状，联合考虑空间和时间属性。具体来说，所提出的框架首先通过动态区域划分方法生成区域。它保持了同一区域内的观测值具有相邻的位置和相似的非空间属性读数，并且与基于网格的方法相比可以减轻数据稀疏性和异质性。然后，将通过区域差异计算方法为每个区域计算异常度量。根据不同的场景，最终可以采用加权或波浪的方法检测出异常区域。在模拟数据集和实际应用中的实验表明了该框架的有效性和实用性。
英文摘要：
The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies are road-based or grid-based, which usually causes the data sparsity problem and affects the detection results. In this paper, we proposed a dynamic region partition method to address the above issues. Besides, we proposed an unsupervised REgional Anomaly Detection framework (ReAD) to detect abnormal regions with arbitrary shapes by jointly considering spatial and temporal properties. Specifically, the proposed framework first generate regions via a dynamic region partition method. It keeps that observations in the same region have adjacent locations and similar non-spatial attribute readings, and could alleviate data sparsity and heterogeneity compared with the grid-based approach. Then, an anomaly metric will be calculated for each region by a regional divergence calculation method. The abnormal regions could be finally detected by a weighted approach or a wavy approach according to the different scenario. Experiments on both the simulated dataset and real-world applications demonstrate the effectiveness and practicability of the proposed framework.
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