Statbel is the national statistical institute of Belgium and a member of the European Statistical System. Big data presents new opportunities and challenges for official statistics. Statbel has established a Big Data Team and conducted projects using mobile phone data, web scraping, and satellite imagery. Key challenges include gaining access to proprietary data sources and developing statistical methodology for large, unstructured datasets. Smart statistics that integrate multiple data sources in real time could provide detailed monitoring systems for issues like air quality and population estimates. Statbel aims to further develop use cases for big data and may need to collaborate with data scientists or hire them internally to fully leverage big data.

1. http://statbel.fgov.be
Marc Debusschere
Coordinator Administrative & Big Data
Statbel and big data
Data Science Meetup Leuven, 27 June 2018

2. http://statbel.fgov.be
Overview
❑ Context
Statbel, big data and the third data revolution in statistics
❑ Big data and Statbel
Projects, accomplishments and problems
❑ European collaboration
❑ Big data and official statistics
Provisional conclusions and way forward
❑ Big data, Statbel and data science

3. http://statbel.fgov.be
Statbel =
❑ Statistics Belgium
❑ The institute formerly known as Nationaal Instituut voor de
Statistiek (NIS) / Institut national de Statistique (INS)
❑ Administratively part of the FPS (‘ministry’) Economy
❑ Member of the European Statistical System (ESS) =
Eurostat + 32 EU & EFTA national statistical institutes + associated statistics producers

4. http://statbel.fgov.be
Big data
❑ = data impossible to process in a ‘normal’ way
‘normal’ is relative …
❑ 3 v’s: volume, velocity, variety
❑ Result of societal and technological changes
Satellites, cameras and sensors, internet and e-mail, social media, mobile
phones and tablets, e-business, e-government, machine-to-machine
(internet of things, IoT)
❑ Result: data explosion, data deluge

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Big data and statistics
❑ Big data = ‘digital footprint’
❑ Containing valuable information, statistically exploitable
(but also commercially …)
❑ Resulting in the third data revolution in statistics
After surveys (>1846) and administrative data (>2000), now: big data!
❑ Possible data sources – list far from exhaustive!
❖ Scanner data, electronical payments, credit card data
❖ Webscraping for job vacancies, enterprise characteristics
❖ Traffic cameras and detection loops
❖ Smart meters (electricity, gas, water)
❖ Last but not least: mobile phone data!

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The future of statistics …
… big data!
Instant statistics based on big data, complemented and/or
validated by administrative data and small and specific ad hoc
surveys.
Also known as: smart statistics …

7. http://statbel.fgov.be
Big data and Statbel: Big Data Team
❑ Start at the end of 2015
❑ Restricted group, operating informally and ad hoc
❑ Focus on mobile phone data, webscraping job vacancies
❑ Tasks:
❖Reflection on strategy, priorities
❖External contacts concerning big data, with data owners, potential users,
federal and regional authorities, academia, EU, international
organisations, …
❖Analysing big datasets and connecting them to statistical ones – see
below

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Big data and Statbel: in production
❑ For consumer price index (CPI)
❖Scanner data supermarkets and retail chains
❖Webscraping prices (e.g. airplane tickets, webshops)

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Big data and Statbel: not planned (at present)
❑ Social media, internet search results, text analytics, …
=‘high-hanging fruits’, access and interpretation very problematic!
❑ Smart meters
Political decision of regions (2012) not to deploy
=> no data (about to change in Flanders)
❑ Traffic cameras, traffic loops
Regional competence and data

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Big data and Statbel: projects
❑ Mobile phone data
❖Project Statbel-Proximus-Eurostat
❖Border Region Data Collection (BRDC)
❖City data from LFS and Big Data
❑ Webscraping
❖Job vacancies
❑ Satellite data and aerial photography
❖Deep Solaris

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Big data in production
❑ Scanner data for CPI
❖ Based on agreement with data owners, facilitated by political pressure
❖ Legal basis (HICP regulation) but cooperative model
❖ Being expanded gradually with new supermarket and retail chains
❖ Extremely smooth and cost-efficient after initial set-up
❑ Webscraping prices for CPI
❖ Collecting prices on webshops (e.g. airplane tickets)
❖ For efficiency but also out of necessity: e-commerce fast expanding!
❖ Legal issues possible

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Big data almost in production
❑ Webscraping job vacancies: about to go in production …
❖ Methodological and practical issues
❖ Stand-alone results not sufficient, need to combine with existing Job
vacancy survey (JVS) and ‘administrative’ data from regional
employment agencies (VDAB, FOREM, Actiris)
❖ Linked to European project (ESSnet Big Data, see below): https://
webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/WP1_Webscraping_job_vacancies

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Project Statbel, Proximus, Eurostat
❑ Start December 2015, first results April 2016
❑ Step by step approach:
❖ First: actual present population
❖ Basis for: resident population (via place of residence), workplace,
‘usual environment’, tourism, labour migration, migration, time use, …
❑ Innovative:
❖ First collaboration NSI/operator in EU => ‘real’ data
❖ No ‘call detail records’ but network signals: 10 x more frequent!
❖ Combining mobile phone data with statistical datasets
❑ Via geo-coupling of aggregates: no privacy issues!

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Project Statbel, Proximus, Eurostat
Results and next steps
❑ Results
❖ 10 publications - see De Meersman e.a., Debusschere e.a., Demunter e.a., Reis e.a., Seynaeve e.a.,
Wirthmann e.a., all at https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/WP5_Documentation
❑ Our objectives:
❖ Further exploration for statistical ánd commercial applications
❖ Concrete use cases with a view to statistical production lines
❑ Unfortunately …
❖ March 2017: data blocked for everyone
❖ In the meantime: new initiatives via MIT/Univ. Newcastle, Eurostat

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Some results
Population density per km²:
Mobile phone data (left) versus Census 2011 (right)

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Some results, continued
Weekday cells identified as ‘residential’, ‘commuting’ or ‘work’, with geographical
representation

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Some results, continued
‘Residential’, ‘work’ and ‘commuting’-cells in the Brussels-Leuven area

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Other projects
❑ Border Region Data Collection (BRDC)
❖Grant EC DG Regio, 1 year, July 2017 - July 2018
❖Cross-border living place-workplace mobility through
combining Labour force survey (LFS), administrative data and
mobile phone data
❖With CBS Netherlands (lead), Destatis Germany, Insee
France, GUS Poland, SURS Slovenia

19. http://statbel.fgov.be
Other projects, continued
❑ Deep Solaris
❖Grant Eurostat, 1 year, kickoff 19 Febr. 2018
❖Detecting solar pannels on the basis of satellite data
and aerial photography
❖Via machine learning
❖With CBS Netherlands (lead), Destatis Germany,
IT.NRW (Düsseldorf, DE) and BISS (Heerlen, NL)

20. http://statbel.fgov.be
Other projects, continued
❑ City data from LFS and Big Data
❖Grant EC DG Regio, 1 year, Jan. – Dec. 2018
❖Mapping metropolitan areas on the basis of Labour force
survey (LFS) and mobile phone data
❖With CBS Netherlands (lead), Destatis Germany, Insee
France, Statistics Austria

21. http://statbel.fgov.be
From exploration to exploitation
Developing use cases
❑ Scanner data and webscraping for CPI
❑ Webscraping job vacancies
❑ Validation living place and workplace Census
❑ Matrix living place-workplace
❑ … (population, migration, tourism, mobility, transport, time use,
environment, agriculture, …)

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Big data in the ESS (European Statistical System)
Initiatives and projects

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Big data and official statistics
Provisional conclusions
❑ Size and complexity of datasets
❖ Less problematic than anticipated (because of pre-processing, at
least some structure)
❖ Focus consequently less on IT infrastructure and software

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Big data and official statistics
Provisional conclusions, continued
❑ Biggest obstacle: access!
❖ Data owned by private enterprises: profit-oriented
❖ Fail to see any advantage in collaborating, on the contrary
(mistakenly!)
❖ Imposing legal obligation seems unavoidable …
❑ Link to privacy issues: fear of reputational damage

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Big data and official statistics
Provisional conclusions, continued
❑ Additional challenge: methodology
❖ All ancient headaches are still there …
… with a lot of new ones added!

26. http://statbel.fgov.be
Big data and official statistics
The next stage: smart statistics
❑ Monitoring systems which are:
❖ integrated
❖ flexible
❖ multi-source
❖ real-time and highly detailed
❑ Some examples:
❖ continuous tracking of air quality
❖ highly granular actual present population (time, location, characteristics)
❖ smart farming statistics

27. http://statbel.fgov.be
For discussion:
big data, Statbel and data science
❑ Statbel owns numerous geocoded datasets (population,
employment, income, lodgings, …)
❑ and might gain access to big data sources …
❑ … but lacks data science, capability to analyse big data
❑ Two possible solutions:
❖ collaboration with academia, researchers
❖ hiring …

28. http://statbel.fgov.be
Questions?
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