Geography Reference
In-Depth Information
The proliferation of location-aware devices has led to detailed accounts of entity-
location interactions. What are the usual paths that an individual commutes from
residence to work? What are common stops along these paths? What are the routine
activities that an individual participates in over a period of time? How have the
routine activities evolved over time? When and what are occasional outings or
routes that one took previously and for what purposes? What are the potential
social interactions among individuals? What kinds of and where are places that
individuals like to hang out and when? These questions seek to identify patterns
of life, socially significant places, and changes in routine activities in space and
time. In time geography, lifelines are comparable to the space-time paths taken
by individuals, stations correspond to locations visited by individuals, bundles
capture locations where multiple individuals visited within space-time proximity,
and domains correspond to the spatial extent in which individuals operate. While
the conceptual mappings work well, time geography offers no specific quantitative
movement analysis of empirical data (Miller and Bridwell
2009
).
20.2
From Time Geography to Trajectory Analysis
Along with the growing use of location-aware devices, there are increasingly
ubiquitous surveillance sensors. With the availability of location data and sensor
data for moving objects, trajectory analysis has quickly gained popularity in
GIScience and related disciplines. The often massive, intensive trajectories recorded
by location-aware devices or geo-sensor networks at fine spatial and temporal reso-
lutions challenge management, query and analysis of trajectory data (Spaccapietra
et al.
2008
). In contrast to time geography, trajectory studies have required the
development of quantitative methods for movement analysis. In time geography,
multiple modes of transportation influence an individual's accessibility in a space-
time aquarium with conceptual elements of lifelines, prisms, bundles, etc. that
represent human activities. Innovative tools are being developed to visualize such
space-time aquaria with functions to query and analyze these conceptual elements
(Shaw and Yu
2009
). Complementarily, trajectory analysis and trajectory mining,
emphasizing the ability to handle massive data, explore effective ways for data
aggregation and generalization (Andrienko and Andrienko
2011
).
While location-aware devices and geo-sensor networks both provide space-time
data on moving objects, the two approaches observe movements in different ways.
Surveillance sensors monitor movements in an area, such as severe storms or
hurricanes in a region, traffic flows on a highway, vehicles in a parking lot, or
patrons at a building entrance. Each object has one trajectory with one activity
(e.g. entering a building), and each trajectory is assumed tangent and continuous
between beginning and ending points. Location-aware devices or personal diaries
(such as geo-tagged tweets or photographs), on the other hand, record the locations
of individuals over space and time. One space-time path taken by an individual may
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