Visualization Challenges for
CCIRC
Workshop on Visualization and
Communication of
Climate Change Risk
Maryam Booshehrian, Bernhard Finkbeiner,
Torsten Möller
Overview
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•
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Data Sources
Dimensions - spatial vs. high-D
Non-Uniform Data
Time-Series
What is hard? / Use for a Vis technician
CCIRC Apr 2009
Torsten Möller
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Data Sources
• field data
• analytical models
• simulation data
CCIRC Apr 2009
Torsten Möller
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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
CCIRC Apr 2009
Torsten Möller
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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!
CCIRC Apr 2009
Torsten Möller
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Non-Uniform Data
• typical with field data
• non-uniform sampling of continuous
domain
• leads to uncertainties
• makes 3D case rather difficult, and
computationally expensive
CCIRC Apr 2009
Torsten Möller
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Time-Series
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•
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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?
CCIRC Apr 2009
Torsten Möller
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The purpose of time series
• find patterns of similar behaviour:
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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)
CCIRC Apr 2009
Torsten Möller
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Dimensions - high-D
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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?
CCIRC Apr 2009
Torsten Möller
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CCIRC Apr 2009
Torsten Möller
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Gordon Kindlmann
Transfer Functions (TFs)
α
Simple (usual) case: Map data
value g to color and opacity
g
RGB(g) α(g)
Shading,
Compositing…
Human Tooth CT
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
Voxels as TACs
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
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 it
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
Distance / similarity metrics (2)
• Simple L1 or L2 metrics:
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
Distance / similarity metrics (3)
• (maximum of) cross-correlation measure:
CCIRC Apr 2009
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Method 1 -TAC template
distance
work and images by Zhe Fang
CCIRC Apr 2009
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work and images by Zhe Fang
Method 2
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
Method 2
CCIRC Apr 2009
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work and images by Zhe Fang
Method 3 / MDS
• Distance from each TAC to each other TAC
• High-dimensional space
• Project into 2D using Multi-dimensional
scaling
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
PET / PET-SORTEO Results
CCIRC Apr 2009
Torsten Möller
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work and images by Zhe Fang
PET / PET-SORTEO Results
CCIRC Apr 2009
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CCIRC Apr 2009
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Bernhard demo
Maryam demo
CCIRC Apr 2009
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What is hard?
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•
•
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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
CCIRC Apr 2009
Torsten Möller
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Diana
Allen
Gwen
Flowers
Randall
Petermann
Duncan
Knowler
Karen
Kohfeld
Frank
Gobas
Peter
Anderson
Ken
Lertzman
Tim Takaro
Ryan Allen
field data
Anal.
Model
Simulation
field data
Anal.
Model
Simulation
field data
Simulation
Anal.
Model
field data
Anal.
Model
Simulation
field data
Simulation
field data
field data
field data
field data
Simulation
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 scales
yes
yes
mainly 2D
+3D;
mainly 2D
+3D;
2D, some
3D
high-D
2D
2D
2D
2D
a little
high-D
1D
a little
high-D
high-D
NonUniform
yes; 3D
rendering
issues
yes; 3D
rendering
issues
no
no
yes; 3D
rendering
issues
no
yes
yes
yes
yes
Sources of
Uncertainty
compare to
field data
compare to
field data;
sensitivity
analysis
sensitivity
analysis
Vis Needs
understand
own data +
comm. to
decision
makers
understand
own data
uncertainty
+ comm. to
policy
makers
comm.
probability
to lay
person
comm. risk
find
patterns analysis
+vis
comm. of
extreme
scenarios
relationship
between
space+time
Tech help
better
workflow /
data
handling;
rendering
Linux help;
rendering;
CPU cycles
CPU cycles
CPU cycles
Data
Source
Time
Dimension
reduce #
parameters
tools for
gathering
data; GIS
corrections