Deep Learning for Spatio-temporal Data Mining

Erscheinungsjahr 2026
ISBN: 978-981-9256-04-4
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Big Data Management

Spatio-temporal data generated by traffic sensors, mobile devices, and Internet of Things (IoT) systems exhibits explosive growth in both volume and complexity. Due to its inherent spatio-temporal correlations and dynamic variations, this data poses unique challenges for extracting actionable insights. While traditional data mining methods often struggle to capture the complex spatial dependencies and dynamic temporal patterns embedded in such data, the rapid advancement of deep learning techniques has provided powerful tools for modeling these intricate features.

This book fills a critical gap by systematically integrating spatio-temporal mining with deep learning. The content is organized into three closely integrated parts. The first part establishes a solid framework by introducing classical tasks such as multivariate time series forecasting, traffic volume inference, and outlier detection, utilizing advanced techniques like Dynamic Graph Learning and Mixture of Experts. The second part delves into trajectory representation learning and similarity modeling, covering self-supervised contrastive techniques, semantic alignment, and social relationship inference. The third part explores emerging applications, demonstrating how theoretical models, such as Transformer-based architectures and Diffusion networks, can be translated into real-world solutions for challenges like cross-platform mobility identity linkage and generative modeling of trajectories.

Designed to balance academic rigor with accessibility, this volume features numerous real-world case studies that provide practical, actionable insights. Requiring only basic knowledge of deep learning and programming, this book serves as a valuable resource for researchers, practitioners, and postgraduate students in computer science and data science.

Zielgruppe

Research

Autoren/Hrsg.

Yu, Yanwei

Wang, Yingjie

Zhao, Zhongying

Zheng, Guanjie

Wang, Bin

Dong, Junyu

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

.- Chapter 1: Introduction to Spatio-temporal Data Mining with Deep Learning.- Chapter 2: Human Mobility Prediction and Location Recommendation.- Chapter 3: Traffic Volume Inference and Forecasting.- Chapter 4: Spatio-temporal Anomaly and Outlier Detection.- Chapter 5: Learning Representations from Trajectory Data.- Chapter 6: Trajectory Similarity and Retrieval.-Chapter 7: Inferring Social Relationships from Mobility Data.- Chapter 8: Linking Trajectories to Users.- Chapter 9: Cross-platform Mobility Identity Linkage.- Chapter 10: Generative Modeling of Trajectories.- Chapter 11: Future Trends in Spatio-temporal Deep Learning.

Über Autor(innen)

Yanwei Yu is a professor in the Faculty of Information Science and Engineering of Ocean University of China. He received the B.S. degree from Liaocheng University, China, in 2008 and the Ph.D. degree from University of Science and Technology Beijing, China, in 2014, respectively. From 2012 to 2013, he was a visiting Ph.D. candidate at the Department of Computer Science of Worcester Polytechnic Institute. From 2016 to 2018, he was a postdoc researcher at the College of Information Sciences and Technology of Pennsylvania State University. His research interests include data mining, machine learning, and database systems. He published more than 100 papers in TODS, TKDE, TOIS, KDD, WWW, AAAI, IJCAI, ICDE and other top journals and conferences.

Yingjie Wang, Professor and Ph.D. supervisor, Deputy Director of the Office of Development Planning and Discipline Construction. Her research focuses on crowd sensing networks, service computing, and artificial intelligence. She has led 15 research projects, including those funded by the National Natural Science Foundation of China, and the Special Funding of the China Postdoctoral Science Foundation. She has published over 100 high-quality academic papers, including 5 ESI Highly Cited Papers, 1 ESI Hot Paper, and 3 Best Paper Awards, with nearly 3,000 citations.

Zhongying Zhao received the Ph.D. degree from Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2012. She is currently a Professor with College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China. Her research interests include data mining and recommendation. She has authored or coauthored more than 60 papers in top journals and conferences, like IEEE TKDE, TNNLS, TNSE, TBD, ACM TOMM and KDD.

Guanjie Zheng is an Associate Professor at the John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, whose research focuses on using machine learning and data mining methods to solve spatiotemporal issues, especially on how to make data-driven intelligent decisions from urban spatiotemporal big data. He has published more than 40 papers in top international conferences and journals such as Nature Communications, The Innovation, NeurIPS, and KDD.

Bin Wang received the dual Ph.D. degree from the University of Technology Sydney and Southwest Jiaotong University. He is currently an assistant professor of the College of Information Science and Engineering, Ocean University of China. His research interests include energy forecasting, air traffic flow prediction and AI for healthcare. He has published papers in top-tier conferences and journals such as KDD, IJCAI, TKDE, etc. His research is funded by the National Natural Science Foundation of China.

Junyu Dong received the B.Sc. and M.Sc. degrees from the Department of Applied Mathematics, Ocean University of China, Qingdao, China, in 1993 and 1999, respectively, and the Ph.D. degree in image processing from the Department of Computer Science, Heriot-Watt University, U.K., in 2003. He joined the Ocean University of China in 2004. He is currently a Professor and the Dean of the Faculty of Information Science and Engineering, at Ocean University of China. His research interests include computer vision, underwater image processing, and machine learning, with more than ten research projects supported by the NSFC, MOST, and other funding agencies.