Abstract

Spatial and spatiotemporal data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the data collected over time and space. However, explosive growth in the spatial and spatiotemporal data, and the emergence of social media and location sensing technologies, emphasizes the need for developing new and computationally efficient methods tailored for analyzing big data. In this chapter, we study approaches to handle big spatial and spatiotemporal data by closely looking at the computational and I/O requirements of several analysis algorithms for such data. We also study applications of such methods in domains where data is encountered at a massive scale.

BibTex

@article{Chandola2015,
 author="Varun Chandola and Raju Vatsavai and Devashish Kumar and Auroop Ganguly",
 year="2015",
 journal="Handbook of Statistics",
 title="Analyzing Big Spatial and Big Spatiotemporal Data: A Case Study of Methods and Applications",
}