Visualisation of large data sets

From design to analysis and back

C. Versino

European Commission

Joint Research Centre

cristina.versino@jrc.ec.europa.eu

2016 Visualisation Workshop

Valencia, 17-18 May 2016

Outline

• Issues in data retrieval & analysis over large data sets

• The data visualisation process & the possible interventions

• Example visualisations

• Conclusive remarks

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Invisible ‘Big Data’

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Issue #1

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Data access – (Traditional)

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Issue #2

question answer

data

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Precision vs. Accuracy of information

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“Even if the amount of knowledge in the world is increasing, the

gap between what we know and what we think we know

may be widening. This syndrome is often associated with very

precise-seeming predictions that are not at all accurate. (…)

not accurate not precise

accurate not precise

not accurate precise

accurate precise

Issue #3

Reference Nate Silver

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Data access – (By visualisation)

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question answer

data

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What is data visualisation?

Data visualisation is the process of

transforming, composing and

encoding data by graphical attributes,

for analysis and communication,

with the goal of producing insight and evidence.

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References • Alberto Cairo • Stephen Few • Edward Tufte

(fast) Visual perception

Reasoning (slow)

Analyst + IT tools = Intelligence Augmentation (<> Artificial Intelligence)

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The visualisation process (4 levels of interventions)

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Data composition & visual encoding

Data gathering

Explore Question Understand

Make a point Findings Report ...

Analytical interactions: adding / removing dimensions, sorting, filtering, highlighting, aggregating / disaggregating, drilling, grouping, zooming/panning, re-visualising, re-expressing, re-scaling ...

Data preparation (raw data + meta-data)

Reference Stephen Few 2016 Visualisation Workshop

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2

4

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Raw data – Data integrity – Data sushi

Why raw data?

• Overview of the data (data integrity, accuracy) and

detailed views as required.

• Result data views generated on demand

on dimensions of interest to the analyst.

• ‘Validate the author’ of data views.

• Facilitate blending with other data sources.

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Data sushi:

“A visualisation which is beautiful on the outside

and has raw data on the inside” Reference Jock Mackinlay

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Example #1 Export control policy

Data visualisation – For communication

1 2 4 3

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S. Blunsden, C. Versino VideoZoom storyboard

Example #2

Data visualisation – Exploration tool

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Video reviews

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S. Blunsden, C. Versino VideoZoom zooming interface

Example #2 Video reviews

Data visualisation – Exploration tool

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Data visualisation – Promote awareness of existence of data sources

Example #3 Strategic Trade Atlas for World Customs Organisation

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Data visualisation – Raw data + meta-data

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Example #4

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Nuclear Trade Database

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Data visualisation – Data composition by drag & drop

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1 2 4 3

Example #4 Nuclear Trade Database

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Data visualisation – Overview first … Dashboards bring together individual data views.

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Example #4

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Data visualisation – … details on demand Interactivity: exploration based on data distribution (avoid empty results).

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Example #4

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Outlook: Faceted browsing of visualisation recommendations

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1 2 4 3

K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, Bi. Howe, J. Heer. Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations

IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2016

Conclusive remarks (1)

• Issues in data retrieval and analysis arise when:

• The data are ‘invisible’

• Data access starts by questions and not by data presentation

• Retrieval and analysis systems strive more for results’ precision

than accuracy.

• Data visualisation approaches can mitigate these issues in that

priority is given to data presentation.

• This encourages data exploration by the analyst,

enabling more accurate results and higher data integrity.

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Conclusive remarks (2)

• “Overview first, details on demand”.

• Visual data summarisation is useful in the analysis process.

• Interactive: use the data distribution to avoid zero-result queries

(faceted search approach).

• Added valued in data preparation:

relate generic data to the analyst’s specific domain of interest.

• Analyst & tool designer: can be the same person…

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Acknowledgements

The work presented is funded by the European Commission, Joint Research Centre, in projects:

Strategic Trade Analysis for Non Proliferation and VideoZoom.

[1] CAIRO A. The Functional Art: An Introduction to Information Graphics and Visualization. Peachpit Press, ISBN-10: 0-

321-83473-9, 2012.

[2] FEW S. Now You See it. Analytics Press, ISBN 0970601980, 2009.

[3] TUFTE E. The Visual Display of Quantitative Information. Graphics Press, ISBN 0961392142, 2001.

[4] SILVER N. The Signal and the Noise: Why Most Predictions Fail but Some Don’t. ISBN 978-1-101 59595-4, 2012.

[5] MACKINLAY J. Jock’s Dream of Data Sushi. Presentation at Tapestry, 2014.

https://www.youtube.com/watch?v=EsyMkuMM8HU

[6] VERSINO C. Pattern Recognition by Humans and Machines. Symposium on International Safeguards: Linking Strategy,

Implementation and People. IAEA, Vienna, 20-24 October 2014.

[7] VERSINO C, HEINE P. Strategic Trade Atlas. Country-Based Views. EUR 27739 EN. Print: ISBN 978-92-79-55003-4

ISSN 1018-5593 doi:10.2789/104834. Online: ISBN 978-92-79-55002-7 ISSN 1831-9424 doi: 10.2789/98360.

References