Workshop talk at 3DVis@IEEEVIS2014 - Does 3D really make sense for Data Visualization?

1. 2D and 3D Presentation of Spatial Data:
A Systematic Review
Workshop 3DVis@IEEEVIS2014
Steve Dübel, Martin Röhlig, Heidrun Schumann, Matthias Trapp
Paris, November 09, 2014
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2. Motivation
Human problem solving and decision making
performance varies enormously (100:1) with different
representations
(P. Hanrahan, 2011)
Which representation should we use?
Does 3D really make sense for Data Visualization?
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3. Problem Statement
2D vs. 3D decision is a challenging task
• Diversity of influencing factors
– Display technology
– The given task
– User
– Application context
– …
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• Diversity of data aspects:
– Attribute space
– Reference space
– Data complexity
– …

4. Our Goal
Categorization of current approaches
– to support problem understanding and
– to ease the choice of visualizations
with regard to:
– data classification (Andrienko, Andrienko 2006)
Attributes (data values) & Reference (spatial frame of reference)
– presentation:
2D & 3D
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5. Systematization
Presentation of the Attribute Space (What)
Presentation of the Reference Space (Where)
in 2D or 3D leads to four categories:
•
•
•
•
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6. Systematization
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7. Attribute Space in 2D
Reference Space in 2D
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• [STKD12] Interactive visualiz. of generalized virtual 3D city models
• [AMST11] Visualization of time oriented data
• [GCML06] A visualization system for space-time and
multivariate patterns
• [YQWZ06] Natural textures for weather data visualization
• [SRDJ05] Temporal visualization of planning polygons for
efficient partitioning of geo-spatial data
• [AnAn04] Interactive visual tools to explore spatio-temporal data
• …
• [WaPl13] Designing a better weather display
• [AnAn11] Spatial generalization and aggregation of
massive movement data
• [KrOr10] Cartography: visualization of geospatial data
• [FuSc04] Visualizing Abstract Data on Maps

8. 8
• [WPL06] Glyphs for visualizing uncertainty in vector fields.
• [KaWr05] Geotime information visualization
• [KeDo02] Interactive 3D visualization of vector data in GIS
• …
• [SHE12] Concepts and techniques of an online 3D atlas
• [Blei11] Evaluating the appropriateness of visually combining
quantitative data representations with 3D desktop virtual
environments using mixed methods
• [BrWh08] Multilayer hybrid visualizations to support 3D
• [FuSc04] Visualizing Abstract Data on Maps
Attribute Space in 2D
Reference Space in 3D

9. 9
Challenges:
• Reference Space:
• Occlusion
• Perspective distortion
Attribute Space in 2D
Reference Space in 3D

10. 10
• [BDAC14] A review of temporal data visualizations based on
space-time cube operations
• [ToSc12] The great wall of space-time
• [HTSS10] Visualization of attributed hierarchical structures in a
spatiotemporal context
• [JRWP01] Acquisition and display of real-time atmospheric data
• [Hae70] What about people in regional science?
• …
• [TSAA12] Stacking-based visualization of trajectory attribute data
• HTSS10] Visualization of attributed hierarchical structures in a
spatiotemporal context
• [TKB07] Designing a visual environment for exploration of
time series of remote sensing data: In search for convective clouds
• [NELJ13] Learning Benefits of Using 2D Versus 3D Maps:
Evidence from a Randomized Controlled Experiment
Attribute Space in 3D
Reference Space in 2D

11. 11
Challenges:
• Attribute Space:
• Occlusion
• Perspective distortion
Attribute Space in 3D
Reference Space in 2D

12. 12
• [CDBK07] Exploratory spatio-temporal data mining and
visualization
• [WAPW06] Visualizing the underwater behavior of
humpback whales
• [ZCSZ06] A 3D visualization system for hurricane
storm-surge flooding
• …
• [SHE12] Concepts and techniques of an online 3D atlas
• [TDN11] Information Visualization in Climate Research
• [BHAB00] Preliminary results from polar-orbiting satellite data
assimilation into laps with application to mesoscale modeling
• [Tre99] Task-specific visualization design
Attribute Space in 3D
Reference Space in 3D

13. 13
Challenges:
• Increased occlusion between and within
Attribute and Reference Space
• Perspective distortion
→ Increased Visual clutter
Attribute Space in 3D
Reference Space in 3D

14. Characteristics
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15. Further aspects
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2D or 3D vs. 2D and 3D
• Transitions
• Multiperpective Views
Transition between 2D & 3D of the
spatial reference (Lorenz et al. 2008)
Transition between 2D & 3D of buildings
on a map (Pasewaldt et al. 2014)

16. Further aspects
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• Static & dynamic
• NPR & PR
Animated trajectory presentation
(Buschmann et al. 2014)
Different Level of Abstraction through
NPR methodes (Semmo et al. 2012)

17. Summary
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2D vs 3D decision is challenging
The question is:
• whether we should decide this once for the whole presentation or
• whether a further differentiation would be helpful.
We distinguish between Attribute and Reference Space
• to categorize different approaches and
• to identify fundamental characteristics of each category.
This systematization is a first step.

18. Research questions
There are many powerful approaches, but we need:
• Evaluation studies
– that differentiate between and
– that quantify specified characteristics of a particular category
– that test comparability of categories
→ towards applicability
• Extensions with respect to
– Presentation aspects
(PR/NPR, dynamic/static)
– output devices
(Stereoscopic 3D Displays, Holografic displays, etc.)
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19. Thank you for your attention!
Your questions?
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20. Evaluations
• Examplary Study: (J.-Y. Vion-Dury and M. Santana, 1994)
Relation of screen-space and number of items to display
• Evaluation, using the ratio of the number of objects perceived and the total
number of objects o
• Existence of a boundary value at which 3D presentations exhibit higher context
perception than 2D presentations
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21. Evaluations
• Other Evaluations (A. Cockburn, 2002, 2004) concludes:
• No significant different effects on spatial memory using 2D
and 3D presentations
• Results of experiments, conducted by (Tory et al, 2006) show
that 3D presentations can be very effective for approximate
navigation and relative positioning, but 2D is more suitable for
precise measurement and interpretation
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22. Evaluations
• Tim Dwyer. Two-and-a-Half-Dimensional
Visualisation of Relational Networks, 2005
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