Georg Albrecht
CMPS 261
10/19/2013
Project Proposal
Proposed Title:
Visualization of Airport Identification and Classification Using Knowledge Discovery and
Information Retrieval Techniques
Abstract:
In this paper we present a method for identifying and classifying airports using data mining and
information retrieval on air traffic data. In addition we show how to visualize these results. First
we set out to see if unsupervised clustering algorithms would be capable of distinguishing
between various types of air traffic patterns. Visualization techniques will be required that enable
quick inspection and validation of the various clusters. Once this is show to be feasible, we then
perform further analysis in an effort to classify the general type of aircraft that formed the
groupings of flight paths. In addition to this, both machine learning and information retrieval
techniques are used to identify the likely locations of airports. We can then combine the gathered
information about potential airport locations with the generalized classification of the air traffic
which utilizes these airports to classify the airport. Here we will also present visualization
methods that can be used to present a user with informative graphical representations of the
presence of various airport types.
Motivation:
This research has several motivations, both general and specific. The work done by applying
machine learning to the air traffic may aid in the potential development of future automation of
the National Air Space (NAS). Work in this area could be helpful for automated air traffic
monitoring to help classify air traffic patterns and detection of anomalous flight patterns.
Furthermore, the work done on airport identification and classification may have potential uses
when attempting to locate and classify potential airports in non-cooperative situations. For the
purposes of this class, I would like to develop visualization techniques that will allow for
informative presentation of results from using such methods.
Related Work:
The following is a list of papers and posters that are, with varying degrees, in the direction of
what I am trying to accomplish. I have not read all of them yet, and so do not know which will
be most of use. However, based on initial viewings those papers marked with a ‘*’ will likely
contain useful information.
2013 VAST:
*Visualizing Locations of Interest in 2D GPS Movement Data by Shrey Gupta, Michael J.
McGuffin, Thomas Kapler
Visualization of Passenger Flows on Metro by Masahiko Itoh, Daisaku Yokoyama, Masashi
Toyoda, Yoshimitsu Tomita, Satoshi Kawamura, Masaru Kitsuregawa
2013 InfoVis:
Using Sparklines to Reveal Trends in Parallel Coordinates by Tomasz Opach, Jimmy Johansson,
Jan Ketil Rød
2013 SciVis:
Urban Transport Energy Consumption Explored Through 3D Arc Maps by Stephanie
Schweitzer, Ariane Middel, Wenwen Zhang
2012 InfoVis:
Stacking-Based Visualization of Trajectory Attribute Data by Christian Tominski, Heidrun
Schumann, Gennady Andrienko, Natalia Andrienko
2012 VAST:
*Visual Analytics Methods for Categoric Spatio-Temporal Data by Tatiana von Landesberger,
Sebastian Bremm, Natalia Andrienko, Gennady Andrienko, Maria Tekusova
2011 InfoVis:
*Composite Density Maps for Multivariate Trajectories by Roeland Scheepens, Niels Willems,
Huub van de Wetering, Gennady Andrienko, Natalia Andrienko, Jarke J. van Wijk
Flow Map Layout via Spiral Trees by Kevin Verbeek, Kevin Buchin, Bettina Speckmann
*From Movement Tracks through Events to Places: Extracting and Characterizing Significant
Places from Mobility Data by Gennady Andrienko, Natalia Andrienko, Christophe Hurter,
Salvatore Rinzivillo, Stefan Wrobel
Proposed Research Directions:
1. Creating improved visualizations which show the results of clustering algorithms on the air
traffic data. Specifically I would like to be able to show not only the altitude and location or the
flight path, but also the direction (so that one can tell if the groups are also separated according
to direction of flight and not just the area traversed/path taken), and speed (so that the aircraft can
be easily generalized as commercial, private, military).
2. A visualization technique for showing where airports could be located based on the start and
end points of the flight paths. This would be more along the lines of the methods discussed in the
"Using K-Means Clustering for a Spatial Analysis of Multivariate and Time-Varying
Microclimate Data" by Häb, Middel, and Hagen you showed me. It would be more of an
offshoot of the work I did in data mining, but still somewhat related. This would be more along
the lines of what I have been considering for a thesis, though I'm sure the results from the first
idea could still be applied to this.
I am intent to experiment with information retrieval techniques from TIM 260 to identify the
locations of potential airports. This will serve as an alternative method, and I believe it will yield
better results than the machine learning algorithms.