Geography Reference
In-Depth Information
1.
I
NTRODUCTION
Temporal geographic information systems or Space-Time GIS (STGIS)
were designed to process, manage, and analyze spatio-temporal data (Yuan,
1996). In past decades, several spatio-temporal conceptual frameworks such as
space-time path and prism for exploring individual activities (Hägerstrand,
1970; Miller, 1991; Yu and Shaw, 2008), the integration of GIS in space-time
representations (Couclelis, 1999), and key spatio-temporal data models such as
snapshot models (Armstrong, 1988), event-based models (Peuquet and Duan,
1995) and object-oriented data models (Worboys, 1992) have been widely
studied and developed. Goodchild (2013) recently examined seven examples
of distinct STGIS data types (i.e., tracking, temporal sequences of snapshots,
temporal sequences of polygon coverages, cellular automata, agent-based-
models, events and transaction, and multidimensional data) and related scienti-
fic questions.
Despite that humans have keen ability to discover patterns hidden in
small-scale data; they may find it difficult for large-scale data that often vary
over both space and time. Researchers have made great effort on spatial data
mining and spatio-temporal visual analytics to raise the cognitive ceilings
which often prevent the interpretation of large spatio-temporal datasets (Guo et
al., 2006; Shaw, Yu, and Bombom, 2008; Andrienko et al., 2010).
In the Mobile Age, with the widespread use of location-awareness
devices, it is possible to collect large-scale location-awareness datasets, such
as mobile phone call data, GPS-enabled taxi trajectories, and social media
data, to sense complex human movements and human-environment inter-
actions. It would be of great significance to explore and understand how cities
function in short-term temporal scales compared with traditional long-term
strategic planning in the new era of Big Data (Batty, 2013).
For example, although the human movements and activities may vary over
time across different regions, the observed activity hotspots and information
flow might exhibit a pattern of spatial dependence. Also, ignoring the temporal
dimension would not be sufficient to discover underlying urban dynamics.
For instance, urban governors might hope to monitor human movements
by observing the neighboring regions in previous time periods. In such space-
time integration contexts, the spatio-temporal analytics should help to answer
questions such as:
