By Wang-Chien Lee, John Krumm (auth.), Yu Zheng, Xiaofang Zhou (eds.)
Spatial trajectories were bringing the exceptional wealth to quite a few learn groups. A spatial trajectory documents the trails of a number of relocating gadgets, corresponding to those who log their commute routes with GPS trajectories. the sphere of relocating gadgets comparable examine has turn into tremendous energetic in the previous few years, in particular with all significant database and information 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 purposes. This publication additionally provides an summary on either basics and the cutting-edge examine encouraged via spatial trajectory info, in addition to a different specialize in trajectory development mining, spatio-temporal info mining and location-based social networks. each one bankruptcy offers readers with a tutorial-style creation to at least one very important element of situation trajectory computing, case stories 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 desirous about desktop technology and geography. execs engaged on spatial trajectory computing also will locate this e-book very useful.
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Extra info for Computing with Spatial Trajectories
The uniform sampling approach is very efﬁcient computationally, but it may not be useful for certain applications that require better capture of some special trajectory details. Notice that every location point in the original trajectory may contain different amount of information required to represent the trajectory and that some neighboring location points may contain redundant information, the location points in an approximated trajectory can be selected based on other criteria instead of uniform sampling.
If the replacement does not meet the speciﬁed error requirement, it recursively partitions the original problem into two subproblems by selecting the location point contributing the most errors as the split point. This process continues until the error between the approximated trajectory and the original trajectory is below the speciﬁed error threshold. The DP algorithm aims to preserve directional trends in the approximated trajectory using the perpendicular Euclidean distance as the error measure.
As the positioning technology and processing power of data acquisition mechanisms continue to advance rapidly, the problem of data explosion gets only worse. Hence, it’s a mandate to employ the data reduction techniques in trajectory preprocessing. In addition to data reduction, trajectories can beneﬁt from ﬁltering to reduce noise and estimate higher-level properties like speed and direction. Since trajectories are normally measured by a sensor, they inevitably have some error, including occasional outliers.