Spatio Temporal Data Mining
Large volumes of spatio temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio temporal data differ from relational data for which computational approaches are developed in the data mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio temporal data have been studied for over a decade in the data mining community. In this article, we present a broad survey of this relatively young field of spatio temporal data mining. We discuss different types of spatio temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio temporal data mining problems in each of these categories.
ACM Comput. Surv.