Information Technology Reference
In-Depth Information
Chapter 4
Decentralized Movement Analysis
Current technological advancements have lead to an ever increasing integration of
information and communication technologies. This chapter investigates what hap-
pens when movement analysis is no longer constrained to desktop information sys-
tems, but instead migrates into the highly dynamic network built by the mobile phones
of commuters or the board computers of taxis in a fleet management system. The bid-
ding war between Google and Facebook over the navigation app Waze may serve as
an example for the increasing relevance of applications where mobile but networked
devices are used for collecting and integrating spatial information (Dredge 2013 ).
Waze integrates on the fly spatial knowledge about traffic flow and road conditions
through a process of crowd-sourcing from drivers acting as mobile sensors.
This chapter combines Computational Movement Analysis (CMA) with decen-
tralized spatial computing (DeSC), the theoretical underpinning of geosensor net-
works (GSN) and vehicular ad hoc networks (VANETs). As a special flavor of DeSC,
the chapter explores the foundation of decentralized movement analysis. In doing
so this chapter shifts focus as it addresses a rather technology-driven problem area.
Instead of discussing conceptual models or analytical techniques irrespective of any
underlying system architecture, it digs deeper and investigates the interplay between
a specific system architecture and its superimposed data processing procedures.
The chapter not only investigates how movement analysis tasks similar to the ones
discussed in the two previous chapters can still be performed in decentralized spatial
information systems, but also investigates how the movement of tracked objects and
tracking devices can be exploited in a wider sense for information processing in
such systems. Specifically, this chapter investigates the following CMA tasks in a
decentralized way: Movement pattern detection in Laube et al. ( P6 . 2008 ; P12 . 2011 ),
assessing the network load in a transportation network in Both et al. ( P19 . 2013 ),
point clustering in Laube and Duckham ( P8 . 2009 ), and Laube et al. ( P11 . 2010 ), and
privacy-safeguarding in location-based services in Laube et al. ( P11 . 2010 ).
Overarching research objectives. The research summarized in this chapter con-
tributes to the following overarching research objectives of computational movement
analysis.
 
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