Information Technology Reference
In-Depth Information
Chapter 1
Introduction
In the last decade, advances in tracking technologies resulted in geographic
information representing the movement of individual objects at previously unseen
spatial and temporal granularities. 1 This novel, inherently spatio-temporal kind
of geographic information offers new insights into dynamic geographic processes
but also challenges the traditional very static spatial analysis toolbox ( P17 .
Gudmundsson et al. 2012 ).
Consequently, movement analysis has emerged as a major new research focus of
Geographical Information Science (GIScience). I argue that movement is the first
truly spatio-temporal phenomenon on geographic scales that is traceable beyond
the snapshot. Since movement data is furthermore easily accessible and seemingly
simple in structure, its analysis has received increasing attention from the GIScience
and wider community.
Work has appeared addressing modeling, storing, indexing, and querying move-
ment, mapping and visualizing movement, movement patterns, trajectory similarity
and clustering, trajectory segmentation, semantically annotating and enriching tra-
jectories, as well as simulating movement in the context of many mobile applications,
for instance for location-based services (LBS), vehicular ad hoc networks (VANETs),
or geosensor networks).
This topic 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.
 
Search WWH ::




Custom Search