Computing with Spatial Trajectories
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Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals.
Computing with Spatial Trajectories introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work.
Computing with Spatial Trajectories is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.
Location B? We will discuss the space-time prisms and their implications to trajectories uncertainty in more detail in Section 3.4. While the interest of time geographers is on the uncertainty of location of mobile agents as the time evolves, a speciﬁc type of handling imprecision was considered by the GIS (Geographic Information Systems) researchers, focusing on the spatial properties of the basic primitives. Namely, in a vector GIS, the representation is based on the type of an inﬁnitely small.
Locations. LBS pose new challenges to traditional data privacy-preserving techniques due to two main reasons . (1) These techniques preserve data privacy, but not the location-based queries issued by mobile users. (2) They ensure desired privacy guarantees for a snapshot of the database. In LBS, queries and data are continuously updated at high rates. Such highly dynamic behaviors need continuous maintenance of anonymized user and object sets. Privacy-preserving techniques for LBS can be.
Are said to be co-localized with respect to d, if the Euclidean distance between each pair of points in Tp and Tq at time t ∈ [t1 ,tn ] is less than or equal to d. An anonymity set of k trajectories is deﬁned as a set of at least k colocalized trajectories. The cluster of k co-localized trajectories is then transformed into an aggregate trajectory where each of its location points is computed by the 132 Chi-Yin Chow and Mohemad F. Mokbel WLPH 7UDMHFWRU\ 9ROXPHRI7T UDGLXV G 7UDMHFWRU\.
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Based on this ﬂock concept, two interesting variants, meet  and leadership , are deﬁned: Meet (m, r, k) A meet pattern occurs if at least m objects stay together in a stationary disc with 5 Trajectory Pattern Mining 159 radius r for at least k consecutive time points. Unlike the deﬁnition of ﬂock, the disc speciﬁed in meet has a ﬁxed location. Thus, the concept of meet resembles a past variant of encounter. Figure 5.8 illustrates the concepts of ﬂock and meet. In Figure 5.8(a), a ﬂock.