Detecting spatio-temporal outliers in climate dataset: a method study

Outlier detecting is one of the most important data analysis technologies in data mining, which can be used to discover anomalous phenomena in huge dataset. Many literatures on spatial outlier detecting and time series outlier detecting have appeared, while the area of spatio-temporal outliers considering both spatial and temporal dimensions has still rarely been touched. Defining outliers in traditional dataset is more explicit because the data structure we need to focus on is very straightforward (e.g., a spatial point or a transaction record). However, it is much more difficult to give outlier a definite characterization in spatio-temporal lattice data, since there are so many data structures we can pay attention to. With the aim of detecting useful and meaningful outliers in climate dataset, we introduce a formalized way to define outliers in spatio-temporal lattice data, in which the importance of clarifying basic data structure (we call it basic element in our paper) is stressed. As a case study, we define two kinds of spatio-temporal outliers based on a global climate dataset, according to the three aspects we propose in defining an outlier. The introduction of basic element and the formulation of outlier definition process make it easier and clearer to define meaningful outliers. Thus outlier detecting in spatio-temporal lattice data will provide us with really interesting and useful knowledge.