Deep Learning for Spatio-Temporal Data Mining: A Survey (1906.04928v2)

Abstract: 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 spatio-temporal data is critically important to many real world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal datasets increase rapidly, traditional data mining methods, especially statistics based methods for dealing with such data are becoming overwhelmed. Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine learning tasks due to their powerful hierarchical feature learning ability in both spatial and temporal domains, and have been widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, representation learning, anomaly detection and classification. In this paper, we provide a comprehensive survey on recent progress in applying deep learning techniques for STDM. We first categorize the types of spatio-temporal data and briefly introduce the popular deep learning models that are used in STDM. Then a framework is introduced to show a general pipeline of the utilization of deep learning models for STDM. Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions.

• The paper presents a comprehensive survey of deep learning methods applied to spatio-temporal data mining.
• It categorizes various data types, including events, trajectories, point references, raster, and videos.
• It discusses the challenges, practical applications, and future research directions for advanced ST data analysis.

Deep Learning for Spatio-Temporal Data Mining: A Survey

The paper "Deep Learning for Spatio-Temporal Data Mining: A Survey" provides a comprehensive overview of leveraging deep learning techniques to address spatio-temporal data mining (STDM) challenges. Given the rapid increase in the availability and complexity of spatio-temporal datasets, traditional data mining methods have proven insufficient due to their reliance on handcrafted feature engineering and statistical assumptions. This survey aims to consolidate recent advancements in deep learning applications for STDM across various domains.

Key Insights

The authors categorize spatio-temporal data into five types: event data, trajectory data, point reference data, raster data, and videos. For these data types, deep learning models such as CNN, RNN, GraphCNN, LSTM, and more are highlighted. The paper delineates a general framework for implementing deep learning in STDM tasks, emphasizing essential stages like data instance construction, model selection, and the targeted problem domains.

Numerical Results and Strong Claims

The paper showcases a growing trend in employing deep learning methodologies for STDM, illustrated by the increasing number of publications in this area over recent years. It claims that despite existing surveys, there is a lack of focused literature that provides a holistic view of deep learning's breadth in STDM—asserting that this work is among the first to address this gap comprehensively.

Practical and Theoretical Implications

The practical implications span numerous application areas:

• Transportation: Efficiently predicting traffic flows and detecting incidents using CNNs and GraphCNNs.
• On-Demand Services: Accurate forecasting of demand and supply in services like ride-sharing, facilitated through deep models that integrate multiple data sources.
• Climate Science and Weather Forecasting: Improved prediction accuracies of weather patterns, extreme events, and air quality using hybrid models.

Theoretically, the integration of automatic feature learning as opposed to traditional feature engineering marks a shift towards more versatile and adaptable models capable of handling complex relationships inherent in spatio-temporal datasets.

Speculation on Future Developments

The paper outlines several open challenges such as the need for interpretable models in STDM, selecting optimal deep learning architectures for diverse data types, and the fusion of multi-modal spatio-temporal datasets. These areas indicate potential for future research to expand the applicability and effectiveness of deep learning models.

Conclusion

This survey stands as a notable contribution to the field by mapping existing works, addressing diverse applications, and suggesting future research directions. It underscores the transformative potential of deep learning in enhancing our ability to analyze and extract significant insights from spatio-temporal data. Researchers and practitioners are presented with a consolidated resource that can inform both experimental design and strategic adoption of deep learning in their respective domains.