Space-Time Analytics of Tracks for the Understanding of Patterns of Life - Space-Time Integration in Geography and GIScience - page 369

By segmenting a trajectory into trajectory segments, movement parameters, such as
duration and speed (Dodge et al. 2012 ) and semantic behavior, such as shopping
and work (Popa et al. 2012 ), can be associated with these features along trajectories
for analysis. This approach enables us to extract local behavioral patterns of mobile
objects rather than global patterns for the entire trajectories. Several algorithms are
designed for trajectory segmentation, such as density-based clustering TRACLUS
(Lee et al. 2007 ), Geometry-based Representativeness (Panagiotakis et al. 2012 ),
and Velocity-based Trajectory Structure (Yan et al. 2012 ).
In contrast to segmentation, aggregation and connectivity among trajectory seg-
ments provide insights into the global trajectory patterns. Andrienko and Andrienko
( 2011 ) introduced a trajectory aggregation technique by partitioning the space
into compartments, transforming raw trajectory data into moves between the
compartments, and aggregating the transformed moves with common origins and
common destinations. Guo et al. ( 2010 ) proposed a graph-based partition method
incorporating the use of trajectory topological relationships to find spatial structures
and general patterns of trajectories that were visualized in 2D trajectory density
maps at several temporal snapshots. These spatial structures or clusters often lead to
interesting semantic implications.
Semantic enrichment contextualizes trajectory segments with behavioral char-
acteristics. Eagle and Pentland ( 2006 , 2009 ) applied the principle components of
movement data, termed eigen-behaviors, to capture the structure of behavioral
contexts of individuals, such as staying at home, work, or elsewhere. Availability of
a context-rich dataset is a critical factor for success for empirically based research of
semantic tracks (Giannotti and Pedreschi 2007 ). Using bus routes and bus stops data
and manually labeled modes of transportation (e.g. foot, bus, or car) to trajectory
segments, Patterson et al. ( 2003 ) obtained 84 % accuracy in predicting modes of
transportation with GPS data sampled at 2-10 s intervals over 3 months during
outside activities of the individuals being studied.
The combination of geometric and semantic trajectory analyses deepens the
level of behavioral knowledge that can be discovered from trajectories, such as
trajectory clusters (grouping), trajectory categories (typing), trajectory sequences
(transitioning) and trajectory aggregates (flocking). Laube et al. ( 2007 ) introduced
a methodology for lifeline context operators and standardizations, and explored the
spatio-temporal behaviors of homing pigeons using the sinuosity, rate of change
of trajectory sinuosity, navigational displacement, relation between distance to loft
and flight sinuosity. Dodge et al. ( 2009 ) identified local motion descriptors (i.e.,
motorcycle, car, bicycle, pedestrian) and global and local motion descriptors (e.g.,
velocity, acceleration, turning angle, straightness index) as movement signatures to
differentiate trajectories from different types of mobile objects. Similarly, Willems
et al. ( 2009 ) applied Kernel Density Estimation (KDE) to visualize movement
patterns of seafaring vessels. The density based visualization has shown to be useful
in identifying movement types (e.g. walking or driving; by vessels or speed boats)
and gathering places.
Besides mining trajectories from mobile collectives, attention to episodes along
individual trajectories offers new insights into behavioral changes in mobile objects,