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Movement Mining

  • Patrick LaubeEmail author
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

With ever increasing volumes and complexity of spatio-temporal information, knowledge discovery in databases and its best known step data mining, have rapidly gained importance within Geography and GIScience. Analyzing spatio-temporal data first of all means structuring data, then extracting relevant spatial patterns and rules and providing decision makers with enriched information and condensed knowledge rather than flooding them with raw data. Movement patterns, for example, represent such sought-for high-level process knowledge derived from low-level trajectory data. This second chapter introducing the research field of Computational Movement Analysis (CMA) reviews research on several aspects of mining movement data, including the conceptualization and formalization of movement patterns and the development of algorithms for their detection, the computing of trajectory similarity, and methods for visualization-based exploratory analysis of movement data

Keywords

Data Mining Movement Pattern Movement Data Data Mining Technique Movement Mining 
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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© The Author(s) 2014

Authors and Affiliations

  1. 1.Institute of Natural Resource SciencesZurich University of Applied SciencesWädenswilSwitzerland

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