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Metro Transit-Centric Visualization for City Tour Planning
Pio Claudio and Sung-Eui Yoon
2
Motivation
Tourist Destinations MapMetro Map
Switching between maps forces users to manually map points between different coordinate spaces
Display a holistic combined view of a transportation map and a tourist map
4
Outline
• Motivation
• Related Work
• Our Approach
• Results
5
Related Work
• Automatic Generation of Octilinear Metro Layouts– Mass-Spring[Hong et al. 06], Hill
Climbing[Stott et al. 11]
– Mixed Integer Programming: Nöllenburg et al. 2011
• Map Warping– Image Warping: Böttger et al. 2008
– Helmert Transform: Jenny et al. 2011
http://davis.wpi.edu/~matt/courses/morph/2d.htm
http://designmuseum.org/design/london-transport
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• Combining Different Maps
Related Work
Böttger et al, 2008Reilly et al., 2004
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Our Approach
INPUT: Metro MapINPUT: Tourist DestinationsOctilinear LayoutMap WarpingDestinations Summary
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Framework
POI Data
Run-time Map
Hierarchical Clustering
Map WarpingOctilinear Layout
Visual Worth
Trip Websites
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Determining Popular Regions
Focus +
Context
Wider spaces to popular regions
Graphical Fisheye Views of Graphs. Sarkar et al.
Which are popular regions?
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Determining Popular Regions: Visual Worth
• Kernel Density Estimation
1. Tourist destinations (Points-of-Interest POI)
2. Highly ranked tourist destinations (rank r)
3. Nearby metro-stations (proximity ρ): POI
Visual Worthhighlow
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Framework
POI Data
Run-time Map
Hierarchical Clustering
Map WarpingOctilinear Layout
Trip Websites
Visual Worth
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Octilinear Layout Computation
• Why Octilinear Layout?– Clean and readable schematic representation
• Mixed-Integer Programming [Nöllenburg et al. 2011]
– A set of design constraints are satisfied to find a global solution to layout optimization
Input Variable
Apply variable edge lengths according to visual worth
Uniform Octilinear
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Framework
POI Data
Run-time Map
Hierarchical Clustering
Map WarpingOctilinear Layout
Trip Websites
Visual Worth
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Map Warping
• Put tourist map elements in a single map space• Map warping
– Use the metro stations as control points– Solve for affine transformation parameters– Apply transformation and interpolation to original map points
Input Warped Map
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Framework
POI Data
Run-time Map
Hierarchical Clustering
Map WarpingOctilinear Layout
Trip Websites
Visual Worth
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• Displaying all tourist destinations will clutter the map• Display only relevant destinations at a given view
configuration (visual worth)
Hierarchical Clustering
• Run a hierarchical clustering algorithm [Goldberger2008]
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Runtime Map
• Determine a graph cut which displays largest clusters fitting the view window
• Display top N rated clusters (visual worth)
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Results
Default zoom level Zoomed-in view
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Results: User Study
0%
20%
40%
60%
80%
100%
36% 37%
24%
46%39%
34%
64% 63%
76%
54%61%
66%
Other Ours
Pre
fere
nce
Pe
rce
ntag
e
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Summary
• Holistic visualization technique
– Combines tourist destinations map
– transportation (metro) map
• Octilinear Layout for effective navigation
• Identify and highlight popular tourist areas
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• Connecting paths to POIs
• Adaptive map layout for different display sizes
Future work
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• NRF-2013R1A1A2058052
• DAPA/ADD (UD110006MD)
• MEST/NRF (2013-067321)
• IT R&D program of MOTIE/KEIT [10044970]
Acknowledgements
24
Thank you for listening!
Paper, videos,
source code(coming soon)!
Visit our project homepage:
sglab.kaist.ac.kr/MetroVis