Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (3): 665-696.doi: 10.1007/s11390-020-9349-0

Special Issue: Surveys; Data Management and Data Mining

• Survey • Previous Articles     Next Articles

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

Monidipa Das1, Member, IEEE, Soumya K. Ghosh2, Senior Member, IEEE   

  1. 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;
    2 Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
  • Received:2019-01-15 Revised:2019-11-19 Online:2020-05-28 Published:2020-01-08
  • About author:Monidipa Das is currently working as a postdoctoral research fellow in the Computational Intelligence Laboratory, in the School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore. She received her Ph.D. degree in computer science and engineering from the Indian Institute of Technology (IIT) Kharagpur, Kharagpur, in 2018, and her M.E. degree in computer science and engineering from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur, in 2013. Her research interests include spatial informatics, spatio-temporal data mining, soft computing, and machine learning. Dr. Das is a member of IEEE.

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

Key words: data-driven modeling; spatio-temporal data; prediction; change pattern detection; outlier detection; hotspot detection; partitioning/summarization; (tele-)coupling; visual analytics;

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