Tailoring Trajectories and their Moving Patterns to Contexts

  • Monica WachowiczEmail author
  • Rebecca Ong
  • Chiara Renso
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Nowadays heterogeneous mobile data sources are producing an enormous amount of contextual information that can improve our interpretation of discovered mobility patterns. Because both an entity and the data sources can be mobile, what context is and how it can be used to interpret mobility patters may vary anyplace at anytime. This chapter describes an approach for tailoring mobility patterns based on the synergy of trajectory and mobility pattern annotation techniques, where contexts are represented as dynamic semantic views. These views are obtained after the classification of context variables that are selected based on the classification criteria previously proposed for a taxonomy of collective phenomena. An experiment is used to illustrate the proposed approach for tailoring moving flock patterns to contexts of visitors in a recreational area.


Context Variable Mobility Pattern Semantic Annotation Data Mining Algorithm Semantic View 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.University of New BrunswickNew BrunswickCanada
  2. 2.CNR—KDD LabPisaItaly

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