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Movement Mining

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Computational Movement Analysis

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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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

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Notes

  1. 1.

    Whereas this chapter discusses movement mining in conventional omniscient and centralized information systems or databases, the following Chap. 4 discusses the rather peculiar case where data mining is performed in decentralized systems such as geosensor networks. Even though most of the work summarized in Chap. 4 nominally also proposes data mining techniques, its theoretical underpinning in decentralized spatial computing justifies a separate chapter focusing on decentralized movement analysis alone.

  2. 2.

    Note, the research on flocking featured in this book combines data mining concepts with decentralized spatial computing principles. This chapter focuses on the data general data mining aspects, Chap. 4 on the specifics of mining movement patterns in a decentralized setting.

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  1. Institute of Natural Resource Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland

    Patrick Laube

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Laube, P. (2014). Movement Mining. In: Computational Movement Analysis. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-10268-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-10268-9_3

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