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© 2009 IBM Corporation1
Space, Time, and Antony
Space, Time and Antony
Visualizing Then and Now,
Here and There
© 2009 IBM Corporation2
Space, Time, and Antony
Who is Antony Unwin?
• There is no ‘H’ in ‘Antony’
• Otherwise you might find …
© 2009 IBM Corporation3
Space, Time, and Antony
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Hmmm…
• Without a statistical background, we might just pick the most likely results:– “ANTHONY”– Bimodal distribution of ages– Born in 1908, 1978– Happy 32nd or 102nd birthday!
• Does “Anthony Unwin” have a web page?
© 2009 IBM Corporation5
Space, Time, and Antony
Anthonyunwin.com
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Space, Time, and Antony
Fortunately, we are statisticians
• WWAD [What would Antony Do]?1. Get good data2. Look at the data3. Validate the data4. Build models5. Look at the model results6. Validate the model results7. Don’t trust a model more than the data
© 2009 IBM Corporation7
Space, Time, and Antony
Some Data on Antony
• Sample Papers from 1991 onwards
• Look at the data
© 2009 IBM Corporation8
Space, Time, and Antony
All Y
ears
© 2009 IBM Corporation9
Space, Time, and Antony
1991
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Space, Time, and Antony
1999
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Space, Time, and Antony
2001
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Space, Time, and Antony
2007
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Space, Time, and Antony
All Y
ears
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Space, Time, and Antony
Space and Time
• Base data shown is TEXT x TIME• This relates back to early work I did with
Antony on DIAMOND FAST, exploring time series data.
• The “eyeballing time series” paper exhibits several features later made popular by Shneiderman …
© 2009 IBM Corporation15
Space, Time, and Antony
Visual Information-Seeking Mantra
Overview first
Zoom and filter
Then details-on-demand
Shneiderman, 1996
© 2009 IBM Corporation16
Space, Time, and Antony
Unwin, 1988
• Eyeballing Time Series
“Explore, Interrogate and Manipulate Data Series
Graphically”
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Space, Time, and Antony
Overview First
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Space, Time, and Antony
Overview First
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Zoom and Filter• “Changing time scaling … can be done dramatically by a
factor of 10 with a single click or more precisely by dragging a scaling bar. Here are two views of monthly Canadian unemployment data. The graph on the left of Figure 2 shows all 20 years of the series (240 points) but has been crushed into a very short time-scale. The overall trend and range of seasonality are clearly shown.
• The graph on the right of Figure 2 shows only some of the series, just under two years, at a scale 100 times bigger. The points have been marked to emphasise the effect”
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Details on Demand
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Space, Time, and Antony
+ Modeling
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Space and Time
• Statistical Graphics are a SPACIAL layout of glyphs based on a mapping from data.
• Studying geographic visualization informs on statistical visualization.
• A second project followed Diamond Fast: REGARD
© 2009 IBM Corporation24
Space, Time, and Antony
What did REGARD introduce?
• Linked views:– “Details-on-demand” on steroids– Mix Statistical Graphics and Domain-Specific
Graphics– New ways of displaying data subsets
• Spatial Data– Layers: Multiple elements for different data
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Model Spatial Correlation
• Domain views: variogram cloud, map view
• Link to map view with edge element
• Show selected subset in linked views
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Linking
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Districts of the city of Dublin showing areas with high levels of average income.
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Showing Selections
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REGARD
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Plus ça change, plus c'est la même chose•Wills, G. (2010) Visualizing Time Springer, 2010
•Wills, G., and Wilkinson, L. (2010) Autovis: Automatic Visualization Information Visualization, December 18, 2008•Wilkinson, L and Wills, G. (2008) Scagnostics Distributions Journal of Computational and Graphical Statistics, Volume 17; pp.473-491
•Graham J. Wills (2008) Linked Data Views Handbook of Data Visualization; Chen, Haerdle and Unwin•Wills, Graham J. (2002) Visualization Handbook of Data Mining and Knowledge Discovery
•Wills, Graham (2009) Visualizing Network Data Encyclopedia of Database Systems, Wills, Graham (2009) Visualizing Hierarchies Encyclopedia of Database Systems, •Graham Wills (2006) Networks Graphics of Large DataSets; Unwin, Theus and Hofmann (eds)•Wills, Graham J. and Keim, D. (2002) Data Visualization For Domain Exploration: Interactive Statistical Graphics Handbook of Data Mining and Knowledge Discovery.
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Visualizing Time; Chapter 7
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Space and Time;Overviews
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Visualizing Time; Chapter 6
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Aligned Views;Overviews
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Space, Time, and Antony
Visualizing Time; Chapter 10
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Details on demand
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Visualizing Time; Chapter 9
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Don’t trust the model more
than the data
© 2009 IBM Corporation35
Space, Time, and Antony
Final Lessons
Antony is also close to data; stay there also
Look at the data many ways with appropriate tools and plots
© 2009 IBM Corporation36
Space, Time, and Antony
Final Lessons
Variables + Models = INFORMATION
Statistics, analysis and procedures must be close to representations and must be investigated for features, errors and ideas
© 2009 IBM Corporation37
Space, Time, and Antony
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