Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging Challenges

In recent years, spatiotemporal data has continued to proliferate with the development of data collecting technologies such as the Global Positioning System (GPS), the Internet of Things (IoT), advanced sensors, cameras, loop detectors, and various mobile applications, including social media. Efficient and effective analysis of spatiotemporal data can help extract crucial information in diversified areas such as transportation, climate and weather, the environment, human mobility, public safety, neuroscience, and epidemiology. However, with both spatial and temporal attributes, spatiotemporal data is more complex in nature, making it unique from other types of data. Consequently, additional challenges arise when working with this special data type. Nevertheless, in this era of Artificial Intelligence (AI), researchers have been relentlessly working on developing improved methods that are successful in solving various problems that require unveiling spatiotemporal patterns in the data. In this chapter, we have attempted to provide a comprehensive discussion on spatiotemporal data. We explore both traditional machine learning techniques and the currently preferred deep learning methods that are well-suited for specific problems associated with distinct types, instances, and formats of spatiotemporal data. In addition, we explore various domains where spatiotemporal data is regularly collected, stored, and analyzed. Besides, we also present a case study related to spatiotemporal track association of marine vessels using deep learning algorithms. Finally, we conclude the chapter by identifying the existing challenges associated with spatiotemporal data analysis and providing the direction to tackle these challenges in future research.