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 spatio-temporal data is critically important to many real-world applica...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 34; no. 8; p. 1
Main Authors: Wang, Senzhang, Cao, Jiannong, Yu, Philip
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Anomalies
Artificial neural networks
Crime
Data mining
Data models
Deep learning
Demand analysis
Domains
Electronic devices
Environmental management
Location based services
Machine learning
Meteorology
Neural networks
Public safety
Recurrent neural networks
Remote sensing
Social networks
Spatio-temporal data
Spatiotemporal data
Statistical methods
Task analysis
Trajectory
Transportation
Urban planning
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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 data increase rapidly, traditional data mining methods, especially statistics based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.3025580