1. Neighborhood Visualization
Challenges and Approaches from a Geovisualization Perspective
Matthias Trapp
Hasso-Plattner-Institut, Universität Potsdam, Germany

2. Motivation – 2D Geovisualization
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3. Motivation – 2D Geovisualization
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4. Motivation – 3D Geovisualization
[www.refina3d.de]
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5. Motivation – Virtual 3D City Models
 Central component of information
infrastructures
 Basic information models for…
 Simulation
 Analysis
 Monitoring
 Architectural Design
 Possible applications:
 Location marketing
 Urban development and planning
 Web 2.0
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6. Geovisualization & Dynamic Networks
Data
Infra-
structure Users
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7. Geodata is complex
 Characteristics:
 Data is heterogeneous
 Update frequency increases (partially real-time)
 Challenges:
 Decentralized data storage & interfaces (INSPIRE,…)
 Access
 Digital Rights Management
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8. Users
 Potentials:
 Crowd-Sourcing (e.g., Open Street Map, Public Earth)
 Multi-cultural
 Collaborative environments
 Challenges:
 Ensure data quality
 Data security (privacy)
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9. Visualization Infrastructures
Classic approach: complex, monolithic systems
[döllner et al., 2006]
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10. Visualization Infrastructures
Service-Oriented Architectures (SOA)
 Interoperable Services enables:
Abstraction, Autonomy, Composability,
Discoverability, Formal contract,
Loose coupling, Reusability, Statelessness
[klimke, 2012]
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11. Neighborhood Visualization
 Neighborhood ~ #Geolocations
 Visualization requires context
 depict various information w.r.t. geolocations
 support insights for users
 Waldo Toblers 1st law of geography:
“Everything is related to everything else, but near things
are more related than distant things.”
 Visualization categories:
 Spatial visualization only
 Spatio-Temporal Visualization
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12. SECTION
Spatial Visualization

13. Challenges in 3D Geovisualization
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14. Multi-Perspective Views
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15. Multi-Perspective Views
[lorenz et al., 2008]
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16. Multi-Perspective Views
[lorenz et al., 2006]
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17. Logarithmic Projections
[böttger et al., 2008]
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18. Clutter Reduction
[semmo et al., 2012]
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19. Clutter Reduction
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20. Reduction of Visual Clutter
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21. Clutter Reduction
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22. Symbolization
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23. Symbolization – Coherence ?
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24. SECTION
Data Mapping

25. Style Transfer Functions
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26. Style Transfer Functions
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27. Mapping of Raster Data
Data Layer:
Building category data
Data Layer:
Color Layer:
Color Layer:
Traffic frequency data
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28. Section
Spatio-Temporal Visualization
[buschmann, 2012]

29. Challenges
 Typical questions for spatio-temporal data [Langran, 1989]:
 What changes have occurred with respect to a feature/object?
 What changes in spatial distribution have occurred?
 What is the temporal relationship among multiple phenomena?
 Questions for general temporal data [MacEachren, 1995]:
 Existence of an element of data
 Temporal location and/or cyclic behavior
 Temporal limits (interval) of a data element
 Frequency (temporal texture) of an element
 Rate of change of a data element
 Sequential order of the data elements
 Synchronization (if any) of the data elements
 Visualization categories:
 Dynamic (animations)
 Static (stills)
[nienhaus, 2005]
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30. Colonia3D
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31. Temporal Neighborhood in C3D
[trapp et al., 2010]
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32. Temporal Neighborhood in C3D
[trapp et al., 2010]
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33. 3D Flight Trajectories
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34. 2D Density Maps
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35. 3D Density Volumes
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36. Summary
 (Geo)Visualization is still a developing field
 Real-time / Interaction is a demand
 High research potential: spatio-temporal
visualization
 Service-oriented architectures for implementation
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37. Thanks / Questions?
Contact:
matthias.trapp@hpi.uni-potsdam.de
Special thanks to my collegues:
Sebastian Pasewaldt
Amir Semmo
Stefan Buschmann
Workgroup:
Prof.-Dr. Jürgen Döllner
Computer Graphics Systems Group
Hasso-Plattner-Institut, University of Potsdam
www.hpi3d.de
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