Computational Movement Analysis

1. Front Matter

   Pages i-xiii

   PDF

2. Introduction

   • Patrick Laube

   Pages 1-7

3. Movement Spaces and Movement Traces

   • Patrick Laube

   Pages 9-27

4. Movement Mining

   • Patrick Laube

   Pages 29-58

5. Decentralized Movement Analysis

   • Patrick Laube

   Pages 59-80

6. Grand Challenges in Computational Movement Analysis

   • Patrick Laube

   Pages 81-87

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.

• Data mining
• Decentralized spatial computing
• Geographic information science
• Movement data
• Movement patterns
• Trajectories

• Institute of Natural Resource Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland

  Patrick Laube

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