Spatio-Temporal Outlier Detection

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.