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2014 | Book

Introduction Read chapter Read first chapter

Computational Movement Analysis

Author: Patrick Laube

Publisher: Springer International Publishing

Book Series : SpringerBriefs in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

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.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
This book has a thesis, it makes the case for Computational Movement Analysis (CMA), as an interdisciplinary umbrella for contributions from a wide range of fields aiming for a better understanding of movement processes. This first chapter explains why this inclusive umbrella is a contribution, what it involves, and which fields it borrows methods and concepts from.
Patrick Laube
Chapter 2. Movement Spaces and Movement Traces
Abstract
The analysis of the observed movement by means of computers requires abstraction, conceptual modeling, and formalization of the moving entities and the spaces embedding that movement. This preliminary but crucial stage of Computational Movement Analysis (CMA) requires modeling choices but is also constrained by the data sources at hand. This chapter investigates how movement can be modeled from the various data sources contributing to CMA, and discusses implications of the characteristics of models and sources on how movement can be captured and characterized, structured and analyzed.
Patrick Laube
Chapter 3. Movement Mining
Abstract
With ever increasing volumes and complexity of spatio-temporal information, knowledge discovery in databases and its best known step data mining, have rapidly gained importance within Geography and GIScience. Analyzing spatio-temporal data first of all means structuring data, then extracting relevant spatial patterns and rules and providing decision makers with enriched information and condensed knowledge rather than flooding them with raw data. Movement patterns, for example, represent such sought-for high-level process knowledge derived from low-level trajectory data. This second chapter introducing the research field of Computational Movement Analysis (CMA) reviews research on several aspects of mining movement data, including the conceptualization and formalization of movement patterns and the development of algorithms for their detection, the computing of trajectory similarity, and methods for visualization-based exploratory analysis of movement data
Patrick Laube
Chapter 4. Decentralized Movement Analysis
Abstract
This chapter investigates the implications of decentralized spatial computing for Computational Movement Analysis (CMA). As more and more moving objects are permanently connected to some communication network and to each other, movement analysis is no longer limited to desktop computers collecting movement data first and then analyzing it. By contrast, networked and communicating agents start analyzing information about their movement in a decentralized but collaborative way. This chapter illustrates decentralized spatial analysis concepts for the CMA tasks of monitoring network flow in transportation systems, movement pattern mining, point clustering, and privacy-aware location-based services.
Patrick Laube
Chapter 5. Grand Challenges in Computational Movement Analysis
Abstract
This final chapter addresses the prospect of Computational Movement Analysis (CMA) as a relatively young research field. The first decade of CMA was shaped by significant technological developments resulting in much increased availability of fine-grained movement data, an innocent and somewhat naïve enthusiasm over moving points resulting in a wide but fragmented variety of methods for movement analysis, and finally due to this lack of a unifying theory of CMA only moderate success in overcoming GIS’ and GIScience’ legacy of static cartography. The final chapter concludes this book by proposing a set of grand challenges of CMA.
Patrick Laube
Metadata
Title
Computational Movement Analysis
Author
Patrick Laube
Copyright Year
2014
Electronic ISBN
978-3-319-10268-9
Print ISBN
978-3-319-10267-2
DOI
https://doi.org/10.1007/978-3-319-10268-9

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