Computing with Spatial Trajectories by Yu Zheng, Xiaofang Zhou

By Yu Zheng, Xiaofang Zhou

Spatial trajectories were bringing the exceptional wealth to various learn groups. A spatial trajectory files the trails of a number of relocating gadgets, reminiscent of those that log their commute routes with GPS trajectories. the sphere of relocating gadgets comparable learn 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 current spatial trajectories for various functions. This publication additionally provides an summary on either basics and the state of the art examine encouraged by way of spatial trajectory information, in addition to a different specialise in trajectory development mining, spatio-temporal info mining and location-based social networks. each one bankruptcy presents readers with a tutorial-style creation to at least one vital point of situation trajectory computing, case reports and lots of necessary 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 often desirous about laptop technology and geography. execs engaged on spatial trajectory computing also will locate this booklet very useful.

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Supposedly the more sample points are acquired in a trajectory, the more accurate the trajectory is. However, adopting high sampling rates in acquiring the location points of moving objects to generate the trajectories may result in a massive amount of data leading to enormous overheads in data storage, communications and processing. 3 The system tracks the trajectories of about 4000 buses daily, covering 287 bus routes in the greater Taipei metropolitan area. The locations of buses tracked in the system are transmitted to the system server every 15-25 seconds, generating millions of sampled data points daily.

The mean and median filters are both simple and effective at smoothing a trajectory. They both suffer from lag. More importantly, they are not designed to help estimate higher order variables like speed. In the next two sections, we discuss the Kalman filter and the particle filter, two more advanced techniques that reduce lag and can be designed to estimate more than just location. 9 Kalman Filter The mean and median filters use no model of the trajectory. 1)) and the dynamics of the trajectory. For the Kalman filter, a simple example is smoothing trajectory measurements from something arcing through the air affected only by gravity, such as a soccer ball.

10(a) shows the result of the mean filter with n = 10. The resulting curve is smoother. 3) is a so-called “causal” filter, because it only depends on values in the past to compute the estimate xˆ i . In fact, all the filters discussed in this chapter are causal, meaning they can be sensibly applied to real time data as it arrives. For post-processing, one could use a non-causal mean filter whose sliding window takes into account both past and future values to compute xˆ i . One disadvantage of the mean filter is that it introduces lag.

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