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t
5
(a)
G
j
t
2
t
4
t
3
ϕ
t
1
t
4
B
i
r
B
i
t
5
(b)
t
4
G
j
t
4
t
2
t
3
t
1
B
i
(c)
B
i
SN
B
i
SN
G
j
G
j
t
1
t
2
t
3
t
4
t
5
approach
t
delay
follow
separate
Fig. 3.7
Pursuit and escape pattern (
P16
. Merki and Laube
2012
). An actor (
black dot
) approaches
a reactor (
grey dot
), they follow each other (front region
ϕ
), and finally they separate again.
a
Lagrangian perspective,
b
Eulerian perspective,
c
Eulerian perspective for network bound move-
ment (Republished fromMerki,M. andLaube, P., Detecting reactionmovement patterns in trajectory
data, In Gensel, J., Josselin, D., and Vandenbroucke, D. (eds.),
Bridging the Geographic Information
Sciences
, International AGILE2012 Conference, Avignon (France), April, 24-27, Lecture Notes in
Geoinformation and Cartography, Springer, Berlin, Germany, ISBN 978-3-642-29063-3, Copyright
© 2012)
spatio-temporal constellations found in various publications in themovement ecology
and animation industry bodies of literature. These sources provide precise descrip-
tions of what it means to “initiate the collective movement of a coordinated group”
of grazing heifers or “leading a pack of wolves”. Similarly, Merki and Laube (
P16
.
2012
) developed their formal notion of interaction patterns based on work on preda-
tor/prey behavior and territorial interaction found in the ecology literature as well as
studies on suspicious behavior of pedestrians in video surveillance.
Early work on movement patterns (Laube et al.
2005
) was rather tools-driven,
where pattern definitions were heavily influenced by the algorithms at hand for
the subsequent data mining process. Whereas these papers were important for pro-
moting the idea of movement patterns in the first place, gained insights about the
limitations of such definitions lead to more problem-driven definitions. For example,
Laube et al. (
P12
.
2011a
) revealed the limitation of a disc-shaped notion of prox-
imity for the pattern flock. For a group of grazing cows, group coherence is better
