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 understand...
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cham
Springer
2014
Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Series | SpringerBriefs in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319102672 3319102672 9783319102689 3319102680 |
| ISSN | 2191-5768 2191-5776 |
| DOI | 10.1007/978-3-319-10268-9 |
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| Abstract | 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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| AbstractList | This book 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 addresses the subject as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fields including GIScience, spatiotemporal databases and data mining. Challenges 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. -- 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. |
| Author | Laube, Patrick |
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| BackLink | https://cir.nii.ac.jp/crid/1130000796321690880$$DView record in CiNii |
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| Copyright | The Author(s) 2014 |
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| DOI | 10.1007/978-3-319-10268-9 |
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| Notes | Including references and index |
| OCLC | 890696428 |
| PQID | EBC1966997 |
| PageCount | 98 |
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| RelatedPersons | Furht, Borko Ikeuchi, Katsushi Shen, Xuemin (Sherman) Jain, Lakhmi C. Siciliano, Bruno Lee, Newton Jajodia, Sushil Padua, David Wu, Xindong Zdonik, Stan Shekhar, Shashi Subrahmanian, V.S. Hebert, Martial Katz, Jonathan |
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| Snippet | This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of... This book discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of computational... |
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| SubjectTerms | Artificial intelligence Computer Science Data Mining and Knowledge Discovery Geographic information systems Geographical Information Systems/Cartography Geospatial data Mobile geographic information systems Regional/Spatial Science Transportation |
| SubjectTermsDisplay | Electronic books. Geographic information systems. Geospatial data. |
| TableOfContents | Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- 1 Introduction -- 1.1 Motivation -- 1.2 Introducing Computational Movement Analysis -- 1.3 Structure of this Book -- References -- 2 Movement Spaces and Movement Traces -- 2.1 Data -- 2.2 Conceptual Models for Movement and Movement Spaces -- 2.2.1 Lagrangian Versus Eulerian Movement -- 2.2.2 Constraints to Movement -- 2.2.3 Continuous Versus Discrete Movement Spaces -- 2.3 Computing Movement Descriptors -- 2.3.1 Trajectory Operators -- 2.3.2 Scale -- 2.3.3 Uncertainty and Data Quality -- 2.4 Related Work -- 2.5 Concluding Remarks -- References -- 3 Movement Mining -- 3.1 Data Mining for CMA -- 3.1.1 Defining Movement Mining -- 3.1.2 What is Special About Movement Data? -- 3.2 Movement Mining Tasks -- 3.2.1 Segmentation and Filtering -- 3.2.2 Similarity and Clustering -- 3.2.3 Movement Patterns -- 3.2.4 Exploratory Analysis and Visualization -- 3.3 Evaluation -- 3.3.1 Validation -- 3.3.2 Credibility -- 3.3.3 Efficiency -- 3.4 Related Work -- 3.5 Concluding Remarks -- References -- 4 Decentralized Movement Analysis -- 4.1 Foundations -- 4.2 Movement in Decentralized Spatial Information Systems -- 4.2.1 Static Nodes Monitor Mobile Objects -- 4.2.2 Mobile Agents Monitor Their Collective Movement -- 4.3 Decentralized Movement Analysis Principles -- 4.3.1 Challenges -- 4.3.2 Specific Decentralized Movement Analysis Principles -- 4.3.3 Revisiting General DeSC Principles -- 4.4 Related Work -- 4.5 Concluding Remarks -- References -- 5 Grand Challenges in Computational Movement Analysis -- 5.1 Coping with Big Movement Data -- 5.2 Bridging the Semantic Gap -- 5.3 Contributing to Ambient Spatial Intelligence -- 5.4 Balancing Benefits and Privacy -- 5.5 Improving Recognition -- 5.6 Towards a Unifying Theory of CMA -- References |
| Title | Computational movement analysis |
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