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Spatial-temporal reasoning is the ability to visualize spatial patterns and
mentally manipulate them over a time-ordered sequence of spatial
transformations.
This ability is important for generating and conceptualizing solutions to
multi-step problems that arise in areas such as architecture, engineering,
science, mathematics, art, games, and everyday life.
A profile of an individual with higher acuity to spatial-temporal reasoning is
otherwise referred to as having an exceptional spatial awareness.
While some visual thinkers (who account for around 60% of the general
population)[1] also have good spatial-temporal reasoning this does not make
spatial-temporal reasoning exclusive to those who "think in pictures".
Spatial-temporal reasoning can have as much or more to do with one of the other
5 main modes of thought: the logical (mathematical/systems) style of thought
[1].
Kinesthetic learners (physical learners who learn through body mapping and
physical patterning) are highly developed in spatial awareness[2] and may also
visualize spatial patterns and movement direction without being predominantly
those who 'think in pictures'[1]. The same is true of logical thinkers
(mathematical/systems thinking) who think in patterns and relationships and may
work diagrammatically[1] without this being pictorially and, as such, may have
excellent spatial-temporal reasoning yet not necessarily be strong visual
thinkers at all.
Spatial-temporal reasoning is also studied in computer science. It aims at
describing the common-sense background knowledge on which our human perspective
on the physical reality is based. Methodologically, qualitative constraint
calculi restrict the vocabulary of rich mathematical theories dealing with
temporal or spatial entities such that specific aspects of these theories can
be treated within decidable fragments with simple qualitative (non-metric)
languages. Contrary to mathematical or physical theories about space and time,
qualitative constraint calculi allow for rather inexpensive reasoning about
entities located in space and time. For this reason, the limited expressiveness
of qualitative representation formalism calculi is a benefit if such reasoning
tasks need to be integrated in applications. For example, some of these calculi
may be implemented for handling spatial GIS queries efficiently and some may be
used for navigating, and communicating with, a mobile robot.
Examples of temporal calculi include the so-called point algebra, Allen's
Interval Algebra, and Villain's point-interval calculus. The most prominent
spatial calculi are mereotopological calculi, Frank's cardinal direction
calculus, Freksa's double cross calculus, Egenhofer and Franzosa's 4- and
9-intersection calculi, Ligozat's flip-flop calculus, and various region
connection calculi (RCC). Recently, spatio-temporal calculi have been designed.
For example, the Spatio-temporal Constraint Calculus (STCC) by Gerevini and
Nebel combines Allen's interval algebra with RCC-8. Moreover, the Qualitative
Trajectory Calculus (QTC) allows for reasoning about moving objects.
Most of these calculi can be formalized as abstract relation algebras, such
that reasoning can be carried out at a symbolic level. For computing solutions
of a constraint network, the path-consistency algorithm is an important tool.