StatMine – prototype
0.2
Edwin de Jonge, Jan van der Laan & Jessica Solcer
Statistics Netherlands (CBS)
NTTS 2013, March 6 2013

StatMine
Goal: Improve use figures Statistics Netherlands
How: Add Analysis layer to OutputDB (StatLine)
Working approach:
•
•
•
•
Formulate improvement
Develop software prototype
Test prototype on (real) users
Evaluate
But why?
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Mission SN
“The mission of Statistics Netherlands is to publish
reliable and coherent statistical information that
meets the needs of society” (source: www.cbs.nl)
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Mission SN
“The mission of Statistics Netherlands is to publish
reliable and coherent statistical information that
meets the needs of society” (source: www.cbs.nl)
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Evidence-based
policy
5

What is the state of the Netherlands?
StatLine contains over
1.000.000.000 figures!
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Problem 1
Figures ≠ Information
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1. Figures ≠ Information
We know (from user study):
• Some important user don’t get the most out of
StatLine:
• Data journalists
• Policy makers
• They don’t find and see interesting
information, because of tabular presention (data =
table)
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Solution 1
Visualize
data!
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Problem 2.
Fragmented information
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2. Fragmented information
For policy makers and journalist most information in
OutputDB is fragmented:
• Users need to combine fragments from different
statistics
• Diabetes (insuline usage, hospital admissions,
mortality, visits to doctor, obesity)
• Energy consumption vs economic growth
• Income vs economic growth
• (Perceived) public safety vs registered crimes
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2. Solution:
Let users
combine
tables
(even if we
wouldn’t …)
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Prototype StatMine 0.2
Implements:
• Visual interactive data browsing
• Combining fragments of different tables
Tested on:
• 40 SN employees (++)
• 40 policy makers (++)
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Line chart
Bar chart
- Show development
- Compare
Bubble/scatter chart
Mosaic chart
- Show correlation
- Show structure
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Small multiples
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Technical
HTML5
JSON
R
JavaScript
CSS
SVG
• Runs on desktop
• makkelijk over te zetten naar webserver
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Currently (2013)
• All Official Statistics have confidence interval.
• StatMine 0.3 will test if showing uncertainty
improves/changes understanding of (quality of)
figures.
• May lead to publishing interval estimates (in stead
of point estimates).
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Conclusion
• Visual data browsing is promising for
• Our own statisticians (quality control)
• External policy makers and journalists
• Using real end users for testing is very helpful:
• Lots of suggestions for improvement from users
• Users feel involved in innovation process of NSI
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