By Wang-Chien Lee, John Krumm (auth.), Yu Zheng, Xiaofang Zhou (eds.)
Spatial trajectories were bringing the unparalleled wealth to numerous study groups. A spatial trajectory documents the trails of a number of relocating items, akin to those who log their go back and forth routes with GPS trajectories. the sector of relocating gadgets comparable study has develop into tremendous energetic in the previous couple of years, specifically 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 present spatial trajectories for various functions. This e-book additionally provides an outline on either basics and the cutting-edge study encouraged via spatial trajectory info, in addition to a distinct concentrate on trajectory development mining, spatio-temporal facts mining and location-based social networks. each one bankruptcy presents readers with a tutorial-style creation to 1 very important point of situation trajectory computing, case reviews and lots of useful references to different appropriate study paintings.
Computing with Spatial Trajectories is designed as a reference or secondary textual content booklet for advanced-level scholars and researchers normally interested in computing device 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
The following are the main performance metrics often used to evaluate the efﬁciency and effectiveness of the trajectory data reduction techniques: • Processing time: the execution time spent to run a trajectory data reduction algorithm; • Compression rate: the ratio in the size of an approximate trajectory vs. the size of its original trajectory; • Error measure: the deviation of an approximate trajectory from its original trajectory. Among them, the processing time assesses how efﬁciently a trajectory data reduction technique processing a given trajectory data set.
It is important to note that ﬁltering is not always necessary. In fact, we rarely use it for GPS data. Filtering is important in those situations where the trajectory data is particularly noisy, or when one wants to derive other quantities from it, like speed or direction. 9. The GPS logger recorded 1075 points at a rate of one per second during a short walk around the Microsoft campus in Redmond, Washington USA. For plotting, we converted the latitude/longitude points to (x, y) in meters. While the walk itself followed a casual, smooth path, the recorded trajectory shows many small spikes due to measurement noise.
18 Wang-Chien Lee and John Krumm S S S S S S S S S 3 S 3 S S S S S Fig. 8 The construction of the safe area and the data reduction strategy in a threshold-guided sampling algorithm. Only the location points fallen out of the projected safe areas are reported to the location server. 8 Therefore, a decision to include a particular location point or not needs to take into account changes in speed and direction. g. driving on a highway, moving objects usually do not make dramatic speed and direction changes.