View
223
Download
2
Category
Preview:
Citation preview
Visual Analytics for Spatio-Temporal Data
Andrea Unger
Section 1.5 Geoinformatics
14th November 2012
How to ..?
1. .. compare observation data and simulation data to validate a geoscientific simulation model?
2. .. identify prominent spatio-temporal states in large time series of spatial data?
3. .. find relevant patterns on multiples scales in time series data?
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Time
Our approach: Visual Analytics
• Combine methods from statistics and interactive visualization
• It is not about showing results of analysis in an image
• It is about using visual analytics as part of the analytical process to gain new insights
• Close cooperation with geoscientists – Understand geoscientific challenge
– Identify requirements for analysis
– Develop visual analytics concepts iteratively
Andrea Unger: Visual Analytics for Spatio-Temporal Data
EXAMPLE 1
How to compare observation data and simulation data to validate a geoscientific simulation model?
Andrea Unger: Visual Analytics for Spatio-Temporal Data
With Volker Klemann, Sven Schulte, Doris Dransch
in IEEE Transactions on Visualization and Computer Graphics, December 2012
Example 1: Validation of a geoscientific simulation model
• Goodness of fit – Agreement between observation data and simulation data
• Challenges
– Precise values of model parameters unknown
– Variations in space and time
– Unevenly distributed and imprecise terrestrial data
Evaluate goodness of fit with respect to challenges
Andrea Unger: Visual Analytics for Spatio-Temporal Data
valu
e
time
Example 1: Our approach
• Bring together all relevant data – Observation data
– Model parameterizations and simulation data for each model parameterization
– Goodness of fit values from automatic comparison
• Integrate visualization components that target each challenge
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Example 1: Benefit for analysis
Before
• Analysis of small regions due to varying data quality
• Tedious comparison of multiple parameterizations
Now
• Assessment of data quality – meaningul aggregations of
goodness of fit
– consideration of all available data
• Simplified comparison of parametrizations – find suitable parameterization
• Communication with other scientists
Andrea Unger: Visual Analytics for Spatio-Temporal Data
EXAMPLE 2
How to identify prominent spatio-temporal states in large time series of spatial data?
Time
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Patrick Köthur, Mike Sips, Julian Kuhlmann, Doris Dransch
Example 2: Long Geospatial Time Series
Goal: Find relevant spatio-temporal patterns
Challenge:
How many relevant patterns?
Time
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Example 2: Our approach
• User identifies prominent spatio-temporal patterns by interactive exploration of clustering hierarchy
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Example 2: Benefit for analysis
• Fast and intuitive way to approach geospatial time series
• Detailed assessment of specific patterns
• Supports hypothesis generation
• Challenge: Analysis of large geospatial time series talk by Matthias Sax at 2 pm
Andrea Unger: Visual Analytics for Spatio-Temporal Data
EXAMPLE 3
How to find relevant patterns on multiples scales in time series data?
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Mike Sips, Patrick Köthur, Christian Hege
in IEEE Transactions on Visualization and Computer Graphics, December 2012
Example 3: Patterns on multiple scales in time series data
• Talk by Mike Sips at 2.15 pm
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Conclusion
• Visual analytics combines analytical methods and interactive visualization
• Valuable complement to range of analysis methods
• Benefits – Intuitive access to data and statistical methods
• See what is in the data
• Understand relations among heterogeneous data
• Understand analytical methods
– Generate new hypotheses
– Communication with other scientists
Andrea Unger: Visual Analytics for Spatio-Temporal Data
Recommended