Database Reference
In-Depth Information
we have obtained an additional data set with spatio-temporal boundaries of traf-
fic jams on motorways. It may, in turn, be considered as contextual data and
used in further analysis. Thus, Figure
8.6
f shows selected trajectories passing
through one of the traffic jams, which have been used as a filter for trajectory
selection. We can closely investigate the movement of the cars affected by this
traffic jam by means of an STC (Figure
8.6
g).
Sections
8.2
-
8.4
show that movement can be analyzed at different levels:
whole trajectories, elements of trajectories (points and segments), and high-
level summaries (densities, flows, etc.). In principle, analyzing movement in
context can also be done at these levels. A comprehensive set of visual analytics
methods addressing all these levels and different types of context items does not
exist yet, which necessitates further research in this direction.
8.6 Conclusions
Movement data link together space, time, and objects positioned in space and
time. They hold valuable and multifaceted information about moving objects
and properties of space and time, as well as events and processes occurring in
space and time. Visual analytics has developed a wide variety of methods and
tools for analysis of movement data, which allow an analyst to look at the data
from different perspectives and perform diverse analytical tasks. Visual displays
and interactive techniques are often combined with computational processing,
which, in particular, allows analysis of larger amounts of data than would be pos-
sible with purely visual methods. Visual analytics leverages methods and tools
developed in other areas related to data analytics, particularly statistics, machine
learning, and geographic information science. The main goal of visual analyt-
ics is to enable human understanding and reasoning. We have demonstrated
by examples how understanding of various aspects of movement is gained by
viewing visual displays and interacting with them, possibly after appropriate
data transformations and/or computational derivation of additional data.
8.7 Bibliographic Notes
Keim et al.
(
2008
) give a general definition of visual analytics and describe
the scope of this research field.
Andrienko et al.
(
2011a
) suggest a conceptual
framework defining the concepts of movement data, trajectories, and events, and
possible relationships between moving objects, locations, and times. It shows
that movement data hold valuable information not only about the moving objects
but also about properties of space and time and about events and processes
occurring in space and time. To uncover various types of information hidden in
movement data, it is necessary to consider the data from different perspectives
and to perform a variety of analytical tasks. The paper defines the possible foci
