Paper Info
Title
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.
Year
DOI
Venue
2020
10.1007/s11390-020-9349-0
Journal of Computer Science and Technology
Keywords
DocType
Volume
data-driven modeling, spatio-temporal data, prediction, change pattern detection, outlier detection, hotspot detection, partitioning/summarization, (tele-)coupling, visual analytics
Journal
35
Issue
ISSN
Citations 
3
1000-9000
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Monidipa Das1219.31
Soumya Kanti Ghosh234539.91