Abstract:
Spatial-temporal data is increasingly available due to the rapid development of GPS, mobile devices, and remote sensing. Human mobility understanding, smart transportation, urban planning, public safety, health care, and environmental management depend on spatio-temporal data mining. Traditional data mining methods, especially statistics-based methods, are becoming overwhelmed as spatio-temporal data volume, resolution, and number increase rapidly. Due to their powerful automatic feature representation learning, deep learning models like recurrent neural network (RNN) and convolutional neural network (CNN) have been successful in many domains and are widely used in spatio-temporal data mining (STDM) tasks like predictive learning, anomaly detection, and classification. This paper reviews recent STDM deep learning advances. We categorize spatio-temporal data into five types and briefly introduce STDM’s deep learning models. Next, we classify existing literature by types of spatio-temporal data, data mining tasks, and deep learning models, then apply deep learning for STDM in transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we discuss research limitations and future directions.
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