This document discusses using big data as a source for official statistics. It provides an overview of big data research at Statistics Netherlands and why visualization is used as an analysis tool. Some key challenges discussed include dealing with noisy and dirty data, addressing selectivity issues in big data sources, going beyond simple correlation, and addressing privacy and security concerns. Examples are provided of visualizing census and social security register data. The future potential of big data for statistics is acknowledged, though fundamental methodological, legal and technical issues still need resolution.

1. Big Data as a source for Official Statistics
Edwin de Jonge and Piet Daas
November 12, London

2. Overview
• Big Data
• Research ‘theme’ at Stat. Netherlands
• Data driven approach
• Visualization as a tool
•Why?
•Examples in our office
• Issues & challenges
• From an official statistical perspective
• Focus on methodological and legal ones
2

3. Why Visualization?
October 1st 2013, Statistics Netherlands

4. Effective Display!
(see Tor Norretranders, “Band width of our senses)

5. Anscombes quartet…
DS1 x
y
DS2 x
y
DS3
x
y
DS4
x
y
10
8.04
10 9.14
10 7.46
8
6.58
8
6.95
8 8.14
8 6.77
8
5.76
13
7.58
13 8.74
13 12.74
8
7.71
9
8.81
9 8.77
9 7.11
8
8.84
11
8.33
11 9.26
11 7.81
8
8.47
14
9.96
14 8.1
14 8.84
8
7.04
6
7.24
6 6.13
6 6.08
8
5.25
4
4.26
4 3.1
4 5.39
19
12.5
12
10.84
12 9.13
12 8.15
8
5.56
7
4.82
7 7.26
7 6.42
8
7.91
5
5.68
5 4.74
5 5.73
8
6.89
5

6. Anscombe’s quartet
Property
Value
Mean of x1, x2, x3, x4
All equal: 9
Variance of x1, x2, x3, x4
All equal: 11
Mean of y1, y2, y3, y4
All equal: 7.50
Variance of y1, y2, y3, y4
All equal: 4.1
Correlation for ds1, ds2, ds3, ds4
All equal 0.816
Linear regression for ds1, ds2, ds3,
ds4
All equal: y = 3.00 + 0.500x
Looks the same, right?

7. Lets plot!

8. Assumptions…
8

9. Why visualization?
Tool for data analysis
–Effective display of information
–Summary of data
–Show outliers / patterns
–Helps exploring data
–Helps checking assumptions

10. Often Maps
Many visualizations are maps
–Positive:
‐ Is familiar
‐ Attractive
But: only makes sense:
‐ When data geographically distributed
‐ When locality is meaningful
‐ When data is correctly normalized

11. Huh, Normalized?,
11

13. Many maps just population maps!
A better map:
‐ Takes population size into account (e.g.
by making figures relative)
‐ May plot difference w.r.t. an expected
value.
13

14. Visualization is not easy
– Creating good visualizations is hard
– “Easy Reading” is not “Easy Writing”
Visualization must be:
– Faithful
– Objective
Thus not introduce perceptial bias

15. Visualization
– Use appropriate chart
– Use approprate scales
‐ x,y, color, time
– Use appropriate granularity
Research: What works for which data?

16. Example:
Census
16

17. Example Virtual Census
‐ Every 10 years a Census needs to be conducted
‐ No longer with surveys in the Netherlands
• Last traditional census was in 1971
‐ Now by (re‐)using existing information
• Linking administrative sources and available sample
survey data at a large scale
• Check result
• How?
• With a visualisation method: the Tableplot
11

18. Making the Tableplot
1.
2.
Load file
Sort record according to
key variable
• Age in this example
3. Combine records
each)
• Numeric variables
•
•
100 groups (170,000 records
Calculate average (avg. age)
Categorical variables
•
4.
17 million records
17 million records
Ratio between categories present (male vs. female)
Plot figure
•
Colours used are important
of select number of variables
up to 12
12

20. October 1st 2013, Statistics Netherlands tableplot of the census test file

21. Tableplot: Monitor data quality
– All data in Office passes stages:
‐ Raw data (collected)
‐ Preproccesed (technically correct)
‐ Edited (completed data)
‐ Final (removal of outliers etc.)
21

22. Processing of data
Raw (unedited) data
Edited data
Final data

23. Example 2 : Social Security Register
15

24. – Contains all financial data on jobs, benefits and
pensions in the Netherlands
‐ Collected by the Dutch Tax office
‐ A total of 20 million records each month
‐ How to obtain insight into so much data?
• With a visualisation method: a heat map
24

25. Income (euro)
Heat map: Age vs. ‘Income’
Age
October 1st 2013, Statistics Netherlands
16

26. After ‘
d
ata re
d
uction
’
amount
amount
age
October 1st 2013, Statistics Netherlands
age
17

27. Visualization helps with volume of data
–
–
–
–
–
–
Summarize by “binning”
Tableplot
Histogram
Heatmap (2D histogram)
Smoothing?
Detect unexpected patterns
We use it as a tool to check, explore and communicate
data
27

28. Big Data: Issues and challenges

29. Big Data: issues & challenges
During our exploratory studies we identified
a number of issues & challenges.
Focussing on the methodological and legal ones,
we found that there is a need to:
1) deal with noisy and dirty data
2) deal with selectivity
3) go beyond correlation
4) cope with privacy and security issues
We have only solved some of them (partially)
29

30. 1) Deal with noisy and dirty data
– Big Data is often
‐ noisy
‐ dirty
‐ redundant
‐ unstructured
• e.g. texts, images
– How to extract information
from Big data?
‐ In the best/most efficient way
30

31. Noisy and dirty data
Social media sentiment
Traffic loop data
Aggregate, apply filters (Poisson/Kalman), try to exclude noisy records, models
(capture structure), ‘Google approach’ (80/20 rule)
Preferably do NOT use samples !
31

32. Noise reduction
Social media: daily sentiment in Dutch messages
32

33. Noise reduction
Social media, daily sentiment in Dutch messages
Social media: daily & weekly sentiment in Dutch messages
33

34. Noise reduction
Social media, daily sentiment in Dutch messages
Social media: daily, weekly & monthly sentiment in Dutch messages
34

35. Noise reduction
Social media, daily sentiment in Dutch messages
Social media: monthly sentiment in Dutch messages
35

36. Social media sentiment & Consumer confidence
Social media: monthly sentiment in Dutch messages &
Social media, daily sentiment in Dutch messages
Consumer confidence
Corr: 0.88
36

37. Dirty data
Total number of vehicles detected by traffic loops during the day
37
Time (hour)

38. Loop active varies during the day
38
(first 10 min)

39. Correct for dirty data
Use data from same location from previous/next minute (5 min. window)
Before
Total = ~ 295 million vehicles
39
After
Total = ~ 330 million vehicles (+ 12%)

40. 2) Deal with selectivity
–
Big data sources are selective (they do NOT cover
the entire population considered)
‐
–
AND: all Big Data sources studied so far contain events!
‐
‐
–
Some probably more then others
E.g. social media messages created, calls made and vehicles detected
Events are probably the reason why these sources are so Big
When there is a need to correct for selectivity
1)
Convert events to units
How to identify units?
2) Correct for selectivity of units included
How to cope with units that are truly absent and part of the
population under study?
40

41. Units / events
– Big Data contains events
‐ Social media messages are generated by usernames
‐ Traffic loops count vehicles (Dutch roads are units)
‐ Call detail records of mobile phone ID’s
‐ Convert events to units
• By profiling
41

42. Profiling of Big data
42

43. Travel behaviour of active mobile phones
Mobility of very active mobile
phone users
- during a 14-day period
Based on:
- Call- and text-activity
multiples times a day
- Location based on phone masts
Clearly selective:
- North and South-west
of the country hardly included
43
__

44. 3) Go beyond correlation
–
You will very likely use correlation to check Big Data
findings with those in other (survey) data
–
When correlation is high:
1) try falsifying it first (is it coincidental/spurious?)
correlation ≠ causation
2) If this fails, you may have found something
interesting!
3) Perform additional analysis (look for causality)
cointegration, structural time‐series approach
44
Use common sense!

45. An illustrative example
Official unemployment percentage
Number of social media messages
including the word “unemployment”
X
Corr: 0.90 ?
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46. 4) Privacy and security issues
– The Dutch privacy and security law allows the study of privacy
sensitive data for scientific and statistical research
– Still appropriate measures need to be taken
• Prior to new research studies, check privacy sensitivity of data
• In case of privacy sensitive data:
• Try to anonymize micro data or use aggregates
• Use a secure environment
– Legal issues that enable the use of Big Data for official statistics
production is currently being looked at
‐ No problems for Big Data that can be considered ‘Administrative data’: i.e.
Big Data that is managed by a (semi‐)governmentally funded organisation
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47. Conclusions
– Big data is a very interesting data source
‐ Also for official statistics
– Visualisation is a great way of getting/creating insight
‐ Not only for data exploration
– A number of fundamental issues need to be resolved
‐ Methodological
‐ Legal
‐ Technical (not discussed here)
– We expect great things in the near future!
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48. The future of statistics?