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.

中文翻译：

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ReAD：一种基于动态分区的区域异常检测框架

从城市数据中检测异常区域是一个重要的研究问题。然而，据我们所知，以前针对时空异常设计的方法是基于道路或基于网格的，这通常会导致数据稀疏问题并影响检测结果。在本文中，我们提出了一种动态区域划分方法来解决上述问题。此外，我们提出了一种无监督的区域异常检测框架（ReAD），通过联合考虑空间和时间特性来检测任意形状的异常区域。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。它保持同一区域的观测具有相邻的位置和相似的非空间属性读数，与基于网格的方法相比，可以减轻数据的稀疏性和异构性。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，最终可以通过加权方法或波浪方法来检测异常区域。在模拟数据集和实际应用程序上的实验证明了所提出框架的有效性和实用性。