Welcome to the InfoSci Platform
Spatio-Temporal Outlier Detection: Methods, Applications, and Research Directions

Spatio-Temporal Outlier Detection: Methods, Applications, and Research Directions

Eman O. Eldawy, Mohammed Abdalla, Abdeltawab Hendawi, Hoda M. O. Mokhtar
Copyright: © 2023 |Pages: 17
ISBN13: 9781668473191|ISBN10: 1668473194|ISBN13 Softcover: 9781668473207|EISBN13: 9781668473214
DOI: 10.4018/978-1-6684-7319-1.ch003
Cite Chapter Cite Chapter

MLA

Eldawy, Eman O., et al. "Spatio-Temporal Outlier Detection: Methods, Applications, and Research Directions." Emerging Trends, Techniques, and Applications in Geospatial Data Science, edited by Loveleen Gaur and P.K. Garg, IGI Global, 2023, pp. 63-79. https://doi.org/10.4018/978-1-6684-7319-1.ch003

APA

Eldawy, E. O., Abdalla, M., Hendawi, A., & Mokhtar, H. M. (2023). Spatio-Temporal Outlier Detection: Methods, Applications, and Research Directions. In L. Gaur & P. Garg (Eds.), Emerging Trends, Techniques, and Applications in Geospatial Data Science (pp. 63-79). IGI Global. https://doi.org/10.4018/978-1-6684-7319-1.ch003

Chicago

Eldawy, Eman O., et al. "Spatio-Temporal Outlier Detection: Methods, Applications, and Research Directions." In Emerging Trends, Techniques, and Applications in Geospatial Data Science, edited by Loveleen Gaur and P.K. Garg, 63-79. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7319-1.ch003

Export Reference

Mendeley
Favorite

Abstract

Nowadays, GPS-enabled devices play a vital role in location-based services and daily basis activities such as: locating where your vehicle is at any given time, recovering a stolen vehicle, monitoring your children's location, and traffic or weather alerts. Indeed, precise locations generated from these GPS devices are a must to ensure that the location-based services work accurately. For this purpose, the discovery of abnormal patterns in spatio-temporal data is a significant topic for many kinds of researchers. In this paper, the methods of spatio-temporal outlier detection are categorized into four types: distance and density-based outlier detection, pattern outlier detection, supervised and semi-supervised learning, and statistical and probabilistic techniques. This chapter describes the datasets used in the approaches of spatio-temporal outlier detection and explores its popular applications. The main contribution of this study is to guide researchers to define research gaps in trajectories outlier detection and choose the proper techniques that cope with their research problems.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.