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Visualization Challenges for CCIRC
Workshop on Visualization and Communication of
Climate Change Risk
Maryam Booshehrian, Bernhard Finkbeiner, Torsten Möller
Torsten MöllerCCIRC Apr 2009 2
Overview
• Data Sources• Dimensions - spatial vs. high-D• Non-Uniform Data• Time-Series• What is hard? / Use for a Vis technician
Torsten MöllerCCIRC Apr 2009
Data Sources
• field data• analytical models• simulation data
3
Torsten MöllerCCIRC Apr 2009
Dimensions - spatial
• consider continuous domain, e.g.– 1D - flow down a river– 2D - geospatial– 3D - earth layers or ocean layers
• source - field data or analytical models
4
Torsten MöllerCCIRC Apr 2009
Dimensions - spatial
• almost all have 2D problems– working with maps - GIS
• 1D, 3D less frequent– less clear how to look at it– especially 3D - expensive to render/interact
• multi-resolution data• non-uniform interpolation• time-series!
5
Torsten MöllerCCIRC Apr 2009
Non-Uniform Data
• typical with field data• non-uniform sampling of continuous
domain• leads to uncertainties• makes 3D case rather difficult, and
computationally expensive
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Torsten MöllerCCIRC Apr 2009
Time-Series
• that’s the real big challenge• common problem to most researchers• often 100,000’s of time steps• multi-scale (years vs. seconds)• what to do?
7
Torsten MöllerCCIRC Apr 2009
The purpose of time series
• find patterns of similar behaviour:– locations of similar illness levels– locations of similar forest fire behaviour– locations of similar ground water levels– etc.
• time-series is a means to an end• the end: segmenting the space (1D/2D/3D)
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Torsten MöllerCCIRC Apr 2009
Dimensions - high-D
• based on simulations• lots of data• lots of compute time• how can we analyze the data?• how to do a sensitivity analysis?• can we find correlations, so we can reduce
the dimensionality?
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Torsten MöllerCCIRC Apr 2009 10
Torsten MöllerCCIRC Apr 2009
Human Tooth CT
Transfer Functions (TFs)
α(g)RGB(g)
g
Shading,Compositing…
Simple (usual) case: Map datavalue g to color and opacity
α
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Gordon Kindlmann
Torsten MöllerCCIRC Apr 2009
Voxels as TACs
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Distance / similarity metrics
• Old approach:– Select a “feature” (iso-surface)– Visualize this iso-surface and some (scalar)
voxels “in the neighborhood”• New approach:
– Define a distance or similarity metric among TACs
– Select a “feature” (iso-TAC)– Visualize this iso-TAC and some (TAC) voxels
“similar” to it13
work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Distance / similarity metrics (2)
• Simple L1 or L2 metrics:
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Distance / similarity metrics (3)
• (maximum of) cross-correlation measure:
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Method 1 -TAC template distance
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Method 2
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Method 2
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
Method 3 / MDS
• Distance from each TAC to each other TAC• High-dimensional space• Project into 2D using Multi-dimensional
scaling
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009 20
work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009 21
work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
PET / PET-SORTEO Results
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009
PET / PET-SORTEO Results
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work and images by Zhe Fang
Torsten MöllerCCIRC Apr 2009 24
Torsten MöllerCCIRC Apr 2009
Bernhard demoMaryam demo
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Torsten MöllerCCIRC Apr 2009
What is hard?
• can’t get access to data (proprietary)• CPU cycles (computational challenge)• too many packages - non-trivial integration• reducing the complexity / find correlation
among variables• communicate probabilities to lay person
26
Diana Allen
Gwen Flowers
Randall Petermann
Duncan Knowler
Karen Kohfeld
Frank Gobas
Peter Anderson
Ken Lertzman Tim Takaro Ryan Allen corrections
Data Source
Time
Dimension
Non-Uniform
Sources of Uncertainty
Vis Needs
Tech help
field dataAnal. Model
Simulation
field dataAnal. Model
Simulation
field dataSimulation
Anal. Model
field dataAnal. Model
Simulation
field dataSimulation
field data field data field data field dataSimulation
yes
20,000 ... 100,000
time steps; diff levels of detail
not so much
yes - analytical
20,000 ... 100,000
time steps; diff levels of detail
20,000 ... 100,000
time steps;yes multiple
time scalesyes yes
mainly 2D+3D;
a little high-D
mainly 2D+3D;
a little high-D
high-D 1D 2D, some 3D
high-D 2D 2D 2D 2D
yes; 3D rendering
issues
yes; 3D rendering
issuesno no
yes; 3D rendering
issuesno yes yes yes yes
compare to field data
compare to field data; sensitivity analysis
sensitivity analysis
understand own data + comm. to decision makers
understand own data
uncertainty + comm. to
policy makers
comm. probability
to lay person
reduce # parameters
comm. risk
find patterns - analysis
+vis
comm. of extreme
scenarios
relationship between
space+time
better workflow /
data handling; rendering
Linux help; rendering;
CPU cyclesCPU cycles CPU cycles
tools for gathering data; GIS
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