By Wang-Chien Lee, John Krumm (auth.), Yu Zheng, Xiaofang Zhou (eds.)
Spatial trajectories were bringing the remarkable wealth to a number of examine groups. A spatial trajectory files the trails of a number of relocating gadgets, corresponding to those who log their go back and forth routes with GPS trajectories. the sphere of relocating gadgets similar learn has turn into super energetic in the previous couple of years, in particular with all significant database and knowledge mining meetings and journals.
Computing with Spatial Trajectories introduces the algorithms, applied sciences, and platforms used to approach, deal with and comprehend latest spatial trajectories for various functions. This ebook additionally provides an outline on either basics and the state of the art examine encouraged via spatial trajectory info, in addition to a unique concentrate on trajectory trend mining, spatio-temporal facts mining and location-based social networks. each one bankruptcy offers readers with a tutorial-style advent to 1 vital point of place trajectory computing, case reports and lots of helpful references to different appropriate examine paintings.
Computing with Spatial Trajectories is designed as a reference or secondary textual content publication for advanced-level scholars and researchers generally inquisitive about machine technology and geography. pros engaged on spatial trajectory computing also will locate this ebook very useful.
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Extra info for Computing with Spatial Trajectories
Sample text
E. their geographicaltemporal traces, are often treated as first-class citizens in MODs. Due to the need to acquire and preprocess trajectory data before loading them into the MODs, in this book chapter, we review a number of issues and techniques for trajectory preprocessing, including trajectory data generation, filtering and reduction. Based on Wikipedia, a trajectory is the path that a moving object follows through space as a function of time. To capture the accurate and complete trajectory of a moving object, however, is very difficult and expensive due to the inherent limitations of data acquisition and storage mechanisms.
By analyzing the derived speeds at subsequent segments of a trajectory, they propose to use the speed difference of two subsequent segments as a criteria to decide whether the location point between the two segments should be retained in the approximated trajectory. Accordingly, a new class of spatio-temporal algorithms are obtained by integrating the speed difference threshold and the time synchronized Euclidean distance with the top-down algorithms and open window algorithms. Finally, Hung and Peng propose a model-driven data acquisition technique that reports the speeds of a moving object [17].
To address this issue, Meratina and de S S S S S S 3 S S 3 S S S S S S S S S D 6WHS S S S S S S S 3 3 S S S S S S E 6WHS Fig. 5 The Douglas-Peucker algorithm. Line segments are used to approximate the original trajectory. The original trajectory is split into two subtrajectories by selecting the location point contributing the most errors as the split point. In Step (1), p9 is selected as the split point. In Step (2), p3 is selected as the split point.
