Increasing amounts of spatial-temporal data is becoming available from various kinds
of sensors, surveys, and many other sources for researchers due to the advances in
technologies like GPS, RFID, and wireless communication devices. Mining or analyzing
these kinds of data can shed light into some very interesting applications such as
extracting population's mobility pattern from mobile data, detecting anomalies from
vehicle GPS data, monitoring traffic and quickly responding to events by mining historical
trajectory data, and analyzing peoples' spatio-temporal behaviors with social
and commercial values from their social check-in data. The hidden spatial-temporal
patterns in these data can convey useful knowledge which contributes to the decision
making and problem solving and have high social and commercial values. However,
analysis of a tremendous amount of spatial-temporal data is a very challenging task.
These data are usually noisy, sparse or incomplete, high dimensional, and contain both
spatial and temporal attributes. Thus a fast and intuitive way to understand and compare
these characteristics is indispensable. How to clearly summary and explore these
complex features in data becomes an important problem. It is essential to present the
data features in its original structure to prevent information loss and facilitate the analysis
and at the same time provide users with some approaches to characterize unique
patterns, compare different combinations of features, and quickly search anomalies.
Visual analytics has emerged as a very active research field and can be of great value
for multiple dimensions, spatio-temporal attributes, and heterogeneous structures and
also provide rich interactions, allowing users to explore the data and improve mining
processes and results with their domain knowledge. It turns complex and abstract data such as mobile data, GPS data, historical trajectory, and social check-ins into visual
representations in intuitive ways with rich context over multiple dimensions, and then
users can exploit interactive computer graphics techniques and human visual capabilities
to gain insight into the data. It is essential to keep human in the analysis loop
to exploit the tremendous pattern-recognition capability of the human visual system.
And the analyst's sense of the space and time, the knowledge of space/time's inherent
properties and relationships, and space/time-related experiences, are hard to convey
to machines. Targeting on the visual summarization and analysis in spatial-temporal
patterns in big data, in this thesis, we propose a set of visual summarization approaches
on multi-dimensional spatio-temporal data, which cover different aspects of data visual
analysis issues.
1) We propose a visual analytic approach, which integrated many well-established visualization
techniques such as parallel coordinates and pixel-based representations
to characterize data's mobility-related features and summarize user groups inferred
from the results.
2) We develop a novel visualization method, Voronoi-diagram-based visual design to
reveal the unique features related to flow in the data. This visualization method
can better reveal the direction information when comparing two adjacent flows of
time-series data in a graph.
3) We propose a new visual aided mining approach, Visual Fingerprinting (VF) for
extremely large-scale spatio-temporal feature extraction and analysis. The approach
integrated important statistical and historical information and can be conveniently
embedded into urban maps. The sophisticated design of the visualization can better
reveal frequent or periodic patterns for temporal attributes.
4) We develop an interactive visual analytics system, T-Watcher, for monitoring and
analyzing complex traffic situations in big cities via taxi trajectory data. Several
new integrated traffic fingerprinting designs have been elaborated. We also designed
a novel visual structure called cell-glyph to compare instantaneous situations with
statistical information. The system consists of three major modules (the region fingerprint, the road fingerprint, and the vehicle fingerprint) and users are able to
utilize the carefully designed visual structures to monitor and inspect data interactively
from three levels (region, road and vehicle views) for traffic patterns analysis
and abnormal behaviors detection.
5) We present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to
uncover people's spatio-temporal behaviors by facilitating the analysis and visualization
of social check-in data. We focus on three major components of the check-in
data: location, activity, and user. Visual fingerprints for region, activity, and user
are designed to intuitively represent high-dimensional, spatio-temporal attributes
related to these components. Some well established visualizations like WorldMapper
and Voronoi Treemap are integrated into our glyph-like designs. The visual fingerprint designs allow easy comparison of different check-in locations, different
activities and user groups, which means they can be conveniently overlaid into maps
and embedded into graphs and charts.
6) Finally, we demonstrate the effectiveness and usability of our methods by conducting
case studies on real datasets including mobile phone data, taxi GPS data, and
social check-in data. Some interesting findings have been obtained.
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