Visual analysis of floating car data

The pervasive usage of location positioning and communication technologies in moving vehicles is generating tremendous floating car data (FCD). These big FCD bring new opportunities for understanding urban dynamics. However, analyzing such complex and big movement data is very challenging. This chapter is dedicated to visual analytical approaches for analyzing massive FCD. We firstly introduce the state of the art of FCD visual analysis and identify two abstract levels, namely, point-based and trajectory-based levels, for the analysis. Then we propose several visualization methods and develop interactive visual environments to explore multivariate points and trajectories. Extensive experiments are conducted using a large amount of real-world taxi FCD collected from May 10 to June 30, 2010 in Shanghai. The visual mining results demonstrate that the visual analytical approaches can effectively support users in gaining insight into the data, for instance, to understand distinctive taxi driving behaviors.