Piet daas big_data_official_statistics_target_groningen

Big Data as a
data source for
official statistics

Piet Daas, Marco Puts, Bart Buelens and Paul van den Hurk
Statistics Netherlands

Big Data Target Conference, April 4, Groningen

Overview

• Data sources and statistics
• More & more data becomes available
• Effect on statistics production
• How we study Big Data: 2 examples
• Traffic loop detection data
• Social media messages

Big Data Target Conference, April 4, Groningen 1

Introduction

“Statistics Netherlands has produced
about 5000 official publications and
tables in 2012”
For this we need DATA


Data sources for official statistics

Primary data Secondary data

Data from ‘others’
Our own surveys - Administrative sources
- ‘New’ data sources


Statistics Netherlands law

• “Statistics Netherlands aims to reduce the
administrative burden for companies and the
public as much as possible”
• By (re-)using existing administrative registrations of both
government and government-funded organizations.
• And study potential new sources of information


• Data, data everywhere!

X


Statistics Netherlands and Data
• Data is generated in increasing amounts and at increasing frequencies:
• From ‘Data scarcity’ (sample survey) to ‘Data abundance’ (administrative
& Big)
• Ever increasing amounts of data need to be checked, processed and
analyzed
• More sources of information become available
• Opportunities to produce statistics faster (‘real-time statistics’)
• Need for new methods and tools
1. Methods to quickly uncover information from massive amounts of data
available, such as visualisation methods and data-, text- and stream-
mining techniques (‘making Big Data small’), High Performance Comp.
2. Methods capable of integrating the information in the statistical process,
e.g. linking at massive scale, macro/meso-integration, estimation methods
suited for large datasets


2 Big Data case studies

Research findings on the study of Big Data sources
from a statistics point of view

1. Traffic loop detection data
80 million records/day, studied 90 days so far,
number of vehicles detected each minute

2. Dutch social media messages
1~2 million public messages/day, studied up to 2 billion
records, content and sentiment


1. Traffic loop detection data

• Traffic ‘loops’
• Every minute (24/7) the number of passing
vehicles is counted by >10,000 road sensors
& camera’s in the Netherlands
• Total vehicles and in different length classes

• Interesting source to produce traffic and
transport statistics (and more)
• Huge amounts of data, about 100 million
records a day
Locations


Number of detected vehicles on a single day

By all loops Total = ~ 295 million


Traffic loop detection activity (only first 10 min.)


Correct for missing data
• ‘Corrected’ data (for blocks of 5 min)

Before After

Total = ~ 295 million Total = ~ 330 million (+ 12%)


Total vehicles during the day (snapshots)


For different vehicle lengths
1 categorie 3 categoriën 5 categoriën

Totaal Totaal Totaal
<= 5.6m > 1.85 & <= 2.4m
> 5.6 & <= 12.2m > 2.4 & <= 5.6m
> 12.2m > 5.6 & <= 11.5m
> 11.5 & <= 12.2m
> 12.2m

Small vehicles <= 5.6 m
Medium sized vehicles > 5.6 m & <= 12.2 m
Large vehicles > 12.2 m


Small vehicles

~75% of total


Small & medium vehicles


Small, medium & large vehicles


Volatile behaviour at the micro-level


2. Social media messages

• Dutch are very active on social media platforms
• Bijna altijd bij zich en staat vrijwel altijd aan
• Steeds meer mensen hebben een smartphone!

• Mogelijke informatiebron voor:
• Welke onderwerpen zijn actueel:
• Aantal berichten en sentiment hierover

• Als meetinstrument te gebruiken voor:
• .
Map by Eric Fischer (via Fast Company)


2. Social media messages
• Dutch are very active on social media platforms
• Potential information source for:
• Topics discussed and sentiment over these topics (quickly
available!) and probably more?
• Investigate it to obtain an answer on potential use

2a. Content:
- Collected Dutch Twitter messages for study: ‘selection’ of 12 million

2b. Sentiment
- Sentiment in Dutch social media messages: ‘all’ ~2 billion


Social media: Dutch Twitter topics

(3%)

(7%)
(3%)

(10%)
(7%)
(3%)
(5%)
(46%)

12 million messages


Sentiment in Social media
• Access to Coosto database
• > 2 billion publicly available messages
• Twitter, Facebook, Hyves, Webfora, Blogs etc.
• Sentiment of each message
• Positive, negative or neutral
• Interesting finding
• Determine so-called ‘Mood of the nation’ compared
to Consumer confidence of Statistics Netherlands


Consumer confidence, survey data

Sentiment towards the economic climate

(pos – neg) as % of total

~1000 respondents/month


Final remarks: Big Data and statistics
• Preparing Big data for statistics is time consuming
• Exploration phase takes a lot of time
• Try to reduce amount of data without losing information (‘making big data
small’, noise reduction)
• Risk: ‘garbage in’ ‘garbage statistics out’
• Traditional approach does not suffice
• Big data sources are definitely not ‘large’ sample surveys or admin data
• Often a selective but a large part of the ‘population’ is included
• Events are registered, not units!
• Careful with using ‘traditional’ statistical analysis (everything is significant!)
• More need for:
• Visualisation methods (to rapidly gain insight)
• Methods & models specific for large dataset (fast and ‘robust’)
• Learn from ‘computational statistics’ & (try to) use dedicated hardware
• Beware of privacy issues!


Big Data Target Conference, April 4, Groningen The future of Stat Neth?

Piet daas big_data_official_statistics_target_groningen

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