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

Abstract

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.

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Das, M., Ghosh, S.K. Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts. J. Comput. Sci. Technol. 35, 665–696 (2020). https://doi.org/10.1007/s11390-020-9349-0

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Keywords

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