| 书目名称 | Computational Movement Analysis | | 编辑 | Patrick Laube | | 视频video | | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 描述 | 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. | | 出版日期 | Book 2014 | | 关键词 | Data mining; Decentralized spatial computing; Geographic information science; Movement data; Movement pa | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-10268-9 | | isbn_softcover | 978-3-319-10267-2 | | isbn_ebook | 978-3-319-10268-9Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author(s) 2014 |
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发表于 2025-3-21 16:28:36
