Deep Learning for Spatio-Temporal Data Mining: A Survey

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care environmental management. As number, volume resolution increase rapidly, traditional mining methods, especially statistics-based methods for dealing with are becoming overwhelmed. Recently deep learning models recurrent neural network (RNN) convolutional (CNN) have achieved remarkable success in domains due powerful ability automatic feature representation learning, also widely applied (STDM) tasks predictive anomaly detection classification. In this paper, we provide a comprehensive review recent progress applying STDM. We first categorize into five different types, then briefly introduce that used Next, classify existing literature based on types data, tasks, models, followed by STDM on-demand service, climate & weather analysis, mobility, location-based social network, crime neuroscience. Finally, conclude limitations current research point out future directions.