Computational movement analysis

• Main Author: Laube, Patrick
• Format: eBook Book
• Language: English
• Published: Cham Springer 2014; Springer International Publishing AG; Springer International Publishing
• Edition: 1
• Series: SpringerBriefs in Computer Science
• Subjects: 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
• ISBN: 9783319102672; 3319102672; 9783319102689; 3319102680
• ISSN: 2191-5768; 2191-5776
• DOI: 10.1007/978-3-319-10268-9

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...

Table of Contents:

• 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