  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.
