090414 Visualization Challenges of CCIRC - SFU.caMethod 2 18 work and images by Zhe Fang. CCIRC Apr...

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


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


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


Non-Uniform Data

• typical with field data• non-uniform sampling of continuous

domain• leads to uncertainties• makes 3D case rather difficult, and

computationally expensive

6


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


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)

8


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?

9


Human Tooth CT

Transfer Functions (TFs)

α(g)RGB(g)

g

Shading,Compositing…

Simple (usual) case: Map datavalue g to color and opacity

α

11

Gordon Kindlmann


Voxels as TACs

12

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 it13



Distance / similarity metrics (2)

• Simple L1 or L2 metrics:

14



Distance / similarity metrics (3)

• (maximum of) cross-correlation measure:

15



Method 1 -TAC template distance

16



Method 2

17



Method 2

18



Method 3 / MDS

• Distance from each TAC to each other TAC• High-dimensional space• Project into 2D using Multi-dimensional

scaling

19



PET / PET-SORTEO Results

22



PET / PET-SORTEO Results

23



Bernhard demoMaryam demo

25


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


Simulation

field dataSimulation

Anal. 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

090414 Visualization Challenges of CCIRC - SFU.caMethod 2 18 work and images by Zhe Fang. CCIRC Apr...

Documents

Transcript of 090414 Visualization Challenges of CCIRC - SFU.caMethod 2 18 work and images by Zhe Fang. CCIRC Apr...