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Computational Movement Analysis [electronic resource] / by Patrick Laube.

By: Laube, Patrick [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XIII, 87 p. 22 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319102689.Subject(s): Computer science | Transportation | Data mining | Geographical information systems | Regional economics | Spatial economics | Computer Science | Data Mining and Knowledge Discovery | Geographical Information Systems/Cartography | Regional/Spatial Science | TransportationAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
Introduction -- Movement spaces and movement traces -- Movement mining -- Decentralized movement analysis -- Grand challenges in Computational Movement Analysis.
In: Springer eBooksSummary: This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of Computational Movement Analysis: Conceptual modeling of movement and movement spaces, spatiotemporal analysis methods aiming at a better understanding of movement processes (with a focus on data mining for movement patterns), and using decentralized spatial computing methods in movement analysis. The author presents Computational Movement Analysis as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fields including GIScience, spatiotemporal databases and data mining. Key challenges in Computational Movement Analysis include bridging the semantic gap, privacy issues when movement data involves people, incorporating big and open data, and opportunities for decentralized movement analysis arising from the internet of things. The interdisciplinary concepts of Computational Movement Analysis make this an important book for professionals and students in computer science, geographic information science and its application areas, especially movement ecology and transportation research.
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Introduction -- Movement spaces and movement traces -- Movement mining -- Decentralized movement analysis -- Grand challenges in Computational Movement Analysis.

This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of Computational Movement Analysis: Conceptual modeling of movement and movement spaces, spatiotemporal analysis methods aiming at a better understanding of movement processes (with a focus on data mining for movement patterns), and using decentralized spatial computing methods in movement analysis. The author presents Computational Movement Analysis as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fields including GIScience, spatiotemporal databases and data mining. Key challenges in Computational Movement Analysis include bridging the semantic gap, privacy issues when movement data involves people, incorporating big and open data, and opportunities for decentralized movement analysis arising from the internet of things. The interdisciplinary concepts of Computational Movement Analysis make this an important book for professionals and students in computer science, geographic information science and its application areas, especially movement ecology and transportation research.

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