By Wang-Chien Lee, John Krumm (auth.), Yu Zheng, Xiaofang Zhou (eds.)
Spatial trajectories were bringing the remarkable wealth to quite a few study groups. A spatial trajectory documents the trails of a number of relocating items, similar to those that log their shuttle routes with GPS trajectories. the sector of relocating items comparable learn has develop into super energetic in the previous few years, specifically with all significant database and knowledge mining meetings and journals.
Computing with Spatial Trajectories introduces the algorithms, applied sciences, and platforms used to technique, deal with and comprehend latest spatial trajectories for various functions. This e-book additionally provides an outline on either basics and the cutting-edge study encouraged via spatial trajectory facts, in addition to a different specialize in trajectory trend mining, spatio-temporal facts mining and location-based social networks. every one bankruptcy presents readers with a tutorial-style creation to 1 vital point of position trajectory computing, case experiences and plenty of precious references to different correct study paintings.
Computing with Spatial Trajectories is designed as a reference or secondary textual content e-book for advanced-level scholars and researchers normally interested by laptop technology and geography. pros engaged on spatial trajectory computing also will locate this ebook very useful.
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Extra resources for Computing with Spatial Trajectories
Finally, Hung and Peng propose a model-driven data acquisition technique that reports the speeds of a moving object . They develop a kernel regression algorithm and derive a set of kernel functions to model a time series of speeds readings. 7 Trajectory Filtering Spatial trajectories are never perfectly accurate, due to sensor noise and other factors. Sometimes the error is acceptable, such as when using GPS to identify which city a person is in. In other situations, we can apply various ﬁltering techniques to the trajectory to smooth the noise and potentially decrease the error in the measurements.
Based on the speciﬁed speed and direction tolerance thresholds, Potamias et. al propose threshold-guided sampling algorithms to reduce redundant data points in trajectories . The basic idea is to use a safe area derived from the last two locations and a given thresholds to efﬁciently determine whether a newly acquired location point contains important information. If the new data point, as predicted, is located within the safe area, then this location point is considered as redundant and thus can be discarded.
Even though object movement is continuous, the representation of object trajectories is inevitably in a discrete form due to the nature of sampling-based data acquisition approach. Thus, an intuitive strategy to reduce the volumes of trajectory data is to reduce the sampling rate of data acquisition or to reduce the number of sample points in the trajectory representation. However, the question is whether we are able to discard some sample points without sacriﬁcing the quality of trajectory data required for supporting the targeted applications.