Metro Transit-Centric Visualization for City Tour Planning Pio Claudio and Sung-Eui Yoon.

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Presentation transcript:

Metro Transit-Centric Visualization for City Tour Planning Pio Claudio and Sung-Eui Yoon

2 Motivation Tourist Destinations Map Metro 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

Outline Motivation Related Work Our Approach Results 4

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 Map Warping – Image Warping: Böttger et al – Helmert Transform: Jenny et al

Combining Different Maps 6 Related Work Böttger et al, 2008Reilly et al., 2004

Our Approach INPUT: Metro MapINPUT: Tourist DestinationsOctilinear LayoutMap WarpingDestinations Summary 7

Framework 8 POI Data Run-time Map Hierarchical Clustering Map Warping Octilinear Layout Visual Worth Trip Websites

Determining Popular Regions Focus + Context Wider spaces to popular regions Graphical Fisheye Views of Graphs. Sarkar et al. Which are popular regions? 9

10 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 Worth high low

Framework 11 POI Data Run-time Map Hierarchical Clustering Map Warping Octilinear Layout Trip Websites Visual Worth

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 12 Input Variable Apply variable edge lengths according to visual worth 12 Uniform Octilinear

Framework 13 POI Data Run-time Map Hierarchical Clustering Map Warping Octilinear Layout Trip Websites Visual Worth

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 14 Input Warped Map

Framework 15 POI Data Run-time Map Hierarchical Clustering Map Warping Octilinear Layout Trip Websites Visual Worth

Displaying all tourist destinations will clutter the map Display only relevant destinations at a given view configuration (visual worth) Hierarchical Clustering 16 Run a hierarchical clustering algorithm [Goldberger2008]

Runtime Map Determine a graph cut which displays largest clusters fitting the view window Display top N rated clusters (visual worth) 17

Results Default zoom levelZoomed-in view 18

20 Results: User Study

Summary Holistic visualization technique – Combines tourist destinations map – transportation (metro) map Octilinear Layout for effective navigation Identify and highlight popular tourist areas 21

Connecting paths to POIs Adaptive map layout for different display sizes 22 Future work

NRF-2013R1A1A DAPA/ADD (UD110006MD) MEST/NRF ( ) IT R&D program of MOTIE/KEIT [ ] 23 Acknowledgements

Thank you for listening! Paper, videos, source code(coming soon)! Visit our project homepage: sglab.kaist.ac.kr/MetroVis 24