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Trusted Smart Statistics:
Statistical information
production in a datafied
society and economy
European Big Data Value Forum
Helsinki, 16 October 2019
Eduardo Barredo Capelot
Statistical
Office
European Union
Directorate General
European Commission
Eurostat
Central
Institution European Statistical
System
The mission of the ESS
To provide all the citizens of the European Union with
independent, high-quality information
on the economy and society
on European, national and regional levels and
make the information available to everyone for
decision-making purposes, research and democratic
debate.
Producing high-quality statistics is not enough
In a world of fast-food information and data deluge
we should be trustworthy and our statistics should be visible and
attractive
4
London Transport workers sorting 4 million used #London Underground tickets to identify most and least popular routes in 1939.
Photograph by Gerry Cranham/Fox Photos/Hulton Archive/Getty Images
Experience sharing
Policy
Legislation
Quality
Skills
Methodology
IT Infrastructure
Ethics / Communication
P I L O T S
Big Data for European Statistics
Scheveningen Memorandum 2013
Trusted Smart Statistics
Bucharest Memorandum 2018
Average monthly electricity consumption of private persons, January 2014
kW/h
0 – 99 (638)
100 – 199 (841)
200 – 299 (1165)
300 – 499 (1380)
500 – 999 (574)
1000 < (109)
Not just new data, but a new world
Analog World
• Scarce data: costly to collect
• Designed data: easy to interpret
• Slow change: time to consolidate
methodologies
Digital & Datafied World
• Abundant data: as by-product
• Complex data: difficult to interpret
• Fast change: pressure on
methodological developments
Not just new data, but a new world
Analog World
• Official Statistics as the only “data
monopolist”
• User expectations: annual stats for
whole country delivered after 6
months is o.k.
Digital & Datafied World
• Official Statistics as one of many data
players in a competitive “data
ecosystem”
• User expectations: daily stats for
each district delivered the next day.
Different conceptual approaches
HYPE
DENIAL
CRITICAL
THINKING
Only use new data
Trusted Smart Statistics
Ignore new data
Big Data in official
statistics
Scheveningen
Memorandum
2013
Bucharest
Memorandum
2018
New data requires a new paradigm
Fuel Engine Product
Legacy
New
Design principles
Current Official Statistics
(1st engine)
Trusted Smart Statistics
(2nd engine)
Copy data in
Manual interventions possible
Confidentiality by Statistical
Disclosure Control (SDC)
Open methodology manuals
Efforts in improving data collection
Sources of error & uncertainty for
data known
Share computation out
Statistical production fully automated
Confidentiality by SDC
AND Privacy-Enhancing Tech.
Open source code
Efforts in improving data processing
Sources of error & uncertainty to be
discovered and learned
SHARING
COMPUTATION
PUSHING
COMPUTATION OUT
Statistical System
SHARING DATA
PULLING DATA IN
Statistical System
TRADITIONAL
STATISTICS
PRODUCTION
TRUSTED
SMART
STATISTICS
Confidentiality on output and on input
SDC: Statistical Disclosure Control
SMC: Secure Multi-Party Computation
§
Data transfer
SMC
SDC
To be SMART, you must be TRUSTED.
To be TRUSTED, you must be SMART.
More trust
Deeper data Higher risks
Stronger safeguards
Challenges
Current Official Statistics
(1st engine)
Trusted Smart Statistics
(2nd engine)
Apply existing methodological and quality
framework
Legal basis for data collection (surveys)
and data access (admin) is here
Citizens involved at data collection
(surveys) as respondents
Develop new methodological and quality
frameworks
Legal basis for sustainable access to privately-
held data not yet here
Citizens engaged in entire statistical cycle
(“citizen statistics”)
Webscraping
business’ websites
Webscraping
online job
adverstisements
Data from ship
tracking fro
maritime
transport and
trade
Data from
smart
electricity
meters
Mobile phone
network data
for
population,
tourism,
labour force
Road traffic data as an
indicator of GDP growth
Satellite images for crop
identification, land use,
environmental statistics
From (new modes of) data collection…
Administrative
registers
…To new (models of) statistical production
Multi-
source
products
Statistical
domains
Multi-
purpose
data
Data
sources
§
Surveys
New data
TourismDemography Regional Business Labour Transport
TourismDemography Regional Business Labour Transport
Mobile Network Data
Multi-
Purpose
data
Multi-
source
statistical
products
New business process, new functions
New data sources need
new methodological frameworks:
layered approach & hourglass model
input-agnostic
output-agnostic
components and
definitions
output-specific
processing components
depending on statistical
domain, use-case
input-specific
processing components
depending on data type,
generative technology,
infrastructure details, etc.
Trusted Smart Statistics
Initiatives
Community of experts
Web
Intelligence
Online job
vacancies
Wikipedia
Enterprise
websites
Internet
platforms
…
Trusted
Smart
Surveys
Time use
Household
budget
…
Mobile
Network
Operator
Data
Methodological
Framework
Human
presence and
movements
…
Transport
and
Logistics
Vessel traffic
Air traffic
Railway traffic
…
Smart
Systems
Smart energy
Smart farming
Smart devices
IoT for smart
cities
Smart traffic
…
Earth
Obser-
vation
Agriculture,
Land cover,
Environment,
SDGs
…
Eurostat initiatives on Trusted Smart Statistics
 A bundle of capabilities to support the collection, processing, reuse and
analysis of web data ressource (web pages, APIs …) for producing statistics
o Online job vacancies advertisement
Skills, job vacancies
o Enterprise websites
Business registers, jobs, information society
o Wikipedia / EDGAR / ESEF
EuroGroups Register
Web Intelligence Hub
Web Intelligence Hub – Expected benefits
 Complementary statistical products
 Improved statistical outputs
 Increased spatial granularity
 Flexible and interactive dashboarding
 Shared solutions
 …
Trusted Smart Surveys, Citizen Statistics
interactive
data
responses to queries
passive data
collected by sensors
(e.g. position tracks,
activity traces)
Statistical
System
Secure
Private
Computing
Give back
Close the loop
It seems you are travelling along
the Belgian coast today.
Is this trip related to your work? AI
Mobile Network Operator (MNO) Data
Develop a methodological framework and robust
methodologies for selected use-cases
Build expert knowledge about mobile network technologies.
Pilot applications of Privacy-Enhancing Technologies
Pilot multi-MNO deployments
Initial focus on population and tourism statistics
Average monthly electricity consumption of private persons, January 2014
kW/h
0 – 99 (638)
100 – 199 (841)
200 – 299 (1165)
300 – 499 (1380)
500 – 999 (574)
1000 < (109)
Smart Systems: Electricity Meters
Household Consumption per Commune
kW/h
0 – 99 (835)
100 – 199 (2264)
200 – 299 (1203)
300 – 499 (354)
500 – 999 (45)
1000 < (6)
Average monthly electricity consumption of private persons, July 2014
January July
Transport and logistics
Use of tracking data to provide long-distance
transportation and logistics.
Initial focus on
Ship position data
Extension to air and
railway traffic data
Flash estimates of economic indicators
Earth Observation
Conclusions
• New fuel needs a new engine
• Exploiting new “big data” for Official Statistics requires a new paradigm:
Trusted Smart Statistics
• Multiple dimensions must be addressed in parallel
• New methodological frameworks
• New production models
• Sustainable use of externally-collected data
• Participatory statistics and “citizen statistics”
• Privacy by default
• Use of internet sources to complement statistical information
Trusted Smart Statistics
Community of experts
Web
Intelligence
Online job
vacancies
Wikipedia
Enterprise
websites
Internet
platforms
…
Trusted
Smart
Surveys
Time use
Household
budget
…
Mobile
Network
Operator
Data
Methodological
Framework
Human
presence and
movements
…
Transport
and
Logistics
Vessel traffic
Air traffic
Railway traffic
…
Smart
Systems
Smart farming
Smart devices
IoT for smart
cities
Smart traffic
…
Earth
Obser-
vation
Agriculture,
Land cover,
Environment,
SDGs
…
Eurostat initiatives on Trusted Smart Statistics
Thanks for your attention

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Trusted Smart Statistics

  • 1. Trusted Smart Statistics: Statistical information production in a datafied society and economy European Big Data Value Forum Helsinki, 16 October 2019 Eduardo Barredo Capelot
  • 2. Statistical Office European Union Directorate General European Commission Eurostat Central Institution European Statistical System
  • 3. The mission of the ESS To provide all the citizens of the European Union with independent, high-quality information on the economy and society on European, national and regional levels and make the information available to everyone for decision-making purposes, research and democratic debate.
  • 4. Producing high-quality statistics is not enough In a world of fast-food information and data deluge we should be trustworthy and our statistics should be visible and attractive 4
  • 5. London Transport workers sorting 4 million used #London Underground tickets to identify most and least popular routes in 1939. Photograph by Gerry Cranham/Fox Photos/Hulton Archive/Getty Images
  • 6. Experience sharing Policy Legislation Quality Skills Methodology IT Infrastructure Ethics / Communication P I L O T S Big Data for European Statistics Scheveningen Memorandum 2013 Trusted Smart Statistics Bucharest Memorandum 2018 Average monthly electricity consumption of private persons, January 2014 kW/h 0 – 99 (638) 100 – 199 (841) 200 – 299 (1165) 300 – 499 (1380) 500 – 999 (574) 1000 < (109)
  • 7. Not just new data, but a new world Analog World • Scarce data: costly to collect • Designed data: easy to interpret • Slow change: time to consolidate methodologies Digital & Datafied World • Abundant data: as by-product • Complex data: difficult to interpret • Fast change: pressure on methodological developments
  • 8. Not just new data, but a new world Analog World • Official Statistics as the only “data monopolist” • User expectations: annual stats for whole country delivered after 6 months is o.k. Digital & Datafied World • Official Statistics as one of many data players in a competitive “data ecosystem” • User expectations: daily stats for each district delivered the next day.
  • 9. Different conceptual approaches HYPE DENIAL CRITICAL THINKING Only use new data Trusted Smart Statistics Ignore new data Big Data in official statistics Scheveningen Memorandum 2013 Bucharest Memorandum 2018
  • 10. New data requires a new paradigm Fuel Engine Product Legacy New
  • 11. Design principles Current Official Statistics (1st engine) Trusted Smart Statistics (2nd engine) Copy data in Manual interventions possible Confidentiality by Statistical Disclosure Control (SDC) Open methodology manuals Efforts in improving data collection Sources of error & uncertainty for data known Share computation out Statistical production fully automated Confidentiality by SDC AND Privacy-Enhancing Tech. Open source code Efforts in improving data processing Sources of error & uncertainty to be discovered and learned
  • 12. SHARING COMPUTATION PUSHING COMPUTATION OUT Statistical System SHARING DATA PULLING DATA IN Statistical System TRADITIONAL STATISTICS PRODUCTION TRUSTED SMART STATISTICS
  • 13. Confidentiality on output and on input SDC: Statistical Disclosure Control SMC: Secure Multi-Party Computation § Data transfer SMC SDC
  • 14. To be SMART, you must be TRUSTED. To be TRUSTED, you must be SMART. More trust Deeper data Higher risks Stronger safeguards
  • 15. Challenges Current Official Statistics (1st engine) Trusted Smart Statistics (2nd engine) Apply existing methodological and quality framework Legal basis for data collection (surveys) and data access (admin) is here Citizens involved at data collection (surveys) as respondents Develop new methodological and quality frameworks Legal basis for sustainable access to privately- held data not yet here Citizens engaged in entire statistical cycle (“citizen statistics”)
  • 16. Webscraping business’ websites Webscraping online job adverstisements Data from ship tracking fro maritime transport and trade Data from smart electricity meters Mobile phone network data for population, tourism, labour force Road traffic data as an indicator of GDP growth Satellite images for crop identification, land use, environmental statistics From (new modes of) data collection…
  • 17. Administrative registers …To new (models of) statistical production Multi- source products Statistical domains Multi- purpose data Data sources § Surveys New data TourismDemography Regional Business Labour Transport
  • 18. TourismDemography Regional Business Labour Transport Mobile Network Data Multi- Purpose data Multi- source statistical products New business process, new functions
  • 19. New data sources need new methodological frameworks: layered approach & hourglass model input-agnostic output-agnostic components and definitions output-specific processing components depending on statistical domain, use-case input-specific processing components depending on data type, generative technology, infrastructure details, etc.
  • 20. Trusted Smart Statistics Initiatives Community of experts Web Intelligence Online job vacancies Wikipedia Enterprise websites Internet platforms … Trusted Smart Surveys Time use Household budget … Mobile Network Operator Data Methodological Framework Human presence and movements … Transport and Logistics Vessel traffic Air traffic Railway traffic … Smart Systems Smart energy Smart farming Smart devices IoT for smart cities Smart traffic … Earth Obser- vation Agriculture, Land cover, Environment, SDGs … Eurostat initiatives on Trusted Smart Statistics
  • 21.  A bundle of capabilities to support the collection, processing, reuse and analysis of web data ressource (web pages, APIs …) for producing statistics o Online job vacancies advertisement Skills, job vacancies o Enterprise websites Business registers, jobs, information society o Wikipedia / EDGAR / ESEF EuroGroups Register Web Intelligence Hub
  • 22. Web Intelligence Hub – Expected benefits  Complementary statistical products  Improved statistical outputs  Increased spatial granularity  Flexible and interactive dashboarding  Shared solutions  …
  • 23. Trusted Smart Surveys, Citizen Statistics interactive data responses to queries passive data collected by sensors (e.g. position tracks, activity traces) Statistical System Secure Private Computing Give back Close the loop It seems you are travelling along the Belgian coast today. Is this trip related to your work? AI
  • 24. Mobile Network Operator (MNO) Data Develop a methodological framework and robust methodologies for selected use-cases Build expert knowledge about mobile network technologies. Pilot applications of Privacy-Enhancing Technologies Pilot multi-MNO deployments Initial focus on population and tourism statistics
  • 25. Average monthly electricity consumption of private persons, January 2014 kW/h 0 – 99 (638) 100 – 199 (841) 200 – 299 (1165) 300 – 499 (1380) 500 – 999 (574) 1000 < (109) Smart Systems: Electricity Meters Household Consumption per Commune kW/h 0 – 99 (835) 100 – 199 (2264) 200 – 299 (1203) 300 – 499 (354) 500 – 999 (45) 1000 < (6) Average monthly electricity consumption of private persons, July 2014 January July
  • 26. Transport and logistics Use of tracking data to provide long-distance transportation and logistics. Initial focus on Ship position data Extension to air and railway traffic data Flash estimates of economic indicators
  • 28. Conclusions • New fuel needs a new engine • Exploiting new “big data” for Official Statistics requires a new paradigm: Trusted Smart Statistics • Multiple dimensions must be addressed in parallel • New methodological frameworks • New production models • Sustainable use of externally-collected data • Participatory statistics and “citizen statistics” • Privacy by default • Use of internet sources to complement statistical information
  • 29. Trusted Smart Statistics Community of experts Web Intelligence Online job vacancies Wikipedia Enterprise websites Internet platforms … Trusted Smart Surveys Time use Household budget … Mobile Network Operator Data Methodological Framework Human presence and movements … Transport and Logistics Vessel traffic Air traffic Railway traffic … Smart Systems Smart farming Smart devices IoT for smart cities Smart traffic … Earth Obser- vation Agriculture, Land cover, Environment, SDGs … Eurostat initiatives on Trusted Smart Statistics
  • 30. Thanks for your attention

Editor's Notes

  1. The mission of the ESS it to provide all the citizens of the European Union with independent, high-quality information on the economy and society on European, national and regional levels and make the information available to everyone for decision-making purposes, research and democratic debate. At the heart of the ESS is the ESSC composed by Eurostat and representatives of MS – NSAs. Observers EFTA, ECB and OECD. It is chaired by Eurostat Director-General and its task is « … to provide professional guidance to the ESS for developing, producing and disseminating European statistics,… »
  2. The way we see ourselves is essentailly that in a world of fast-food information and data deluge we should be trustworthy (borrowing the words of Professor Onora O’Neil, meaning= honest, competent and reliable) and our statistics should be visible and attractive. How quality is assured? At the higher level it is assured by the European Code of Practice that sets principles concerning the institutional environment, statistical processes and statistical outputs. The Code aims to ensure that statistics produced within the European Statistical System (ESS) are relevant, timely and accurate, and that they comply with the principles of professional independence, impartiality and objectivity. This is in a nutshell Eurostat and the ESS.
  3. Analysing data is not a new subject. This is big data in 1939 trying to solve essentially an optimisation problem. Data has always been data and statisticians are aware analysing and using data. Operations research is not new neither. Using data for problem-solving and decision making. Big data is « data » nothing new about that neither.
  4. Big data pilots on: Webscraping job advertisements for skills and job vacancies Webscraping of enterprise websites for different purposes: Business register, ICT usage statistics) Smart Electricity Meters for energy consumption statistics and analysis of consumption patterns, e.g. to determine occupation of buildings Automatic vessel identification system for transport and environmental statistics Mobile phone data for population, labour force Flash estimates for short term enterprise statistics and GDP Combination of multiple data sources for population, tourism, agricultural statistics Guidelines on quality, methodology 2018 Implementation of pilots: web scraping, smart meters, AIS New pilots: financial transactions, Earth observation, tourism Exploratory: Smart Systems Methodological framework for mobile network data Pilots produce input for horizontal topic work package on quality, methodology and IT infrastructures 5 conferences on Big data 2016-2019 2 hackathons 14 ESTP courses https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/Main_Page
  5. Big data pilots on: Webscraping job vacancies; collaboration with CEDEFOP Webscraping of enterprise websites for different purposes: Business register, ICT usage statistics) Smart Electricity Meters for energy consumption statistics and analysis of consumption patterns, e.g. to determine occupation of buildings Automatic vessel identification system for transport and environmental statistics Flash estimates for short term enterprise statistics Combination of multiple data sources for population, tourism, agricultural statistics Pilots should produce input for horizontal topic work package on quality, methodology and IT infrastructures A new round of pilots is organised for 2018 (see business case on next slide) https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/Main_Page
  6. The “data revolution” does not only bring “more and bigger data”, but data that are different from legacy ones along multiple key dimensions, requiring fundamental new “system-level” approach as to how “data” is accessed and transformed into “information”. Furthermore, “data revolution” is more than just “new data”: it changes the way things are done, e.g. how people live, how services are delivered, changing goods into services. These other developments contribute to the need of changing the statistical system in terms of “what” is measured and “how” it is measured.
  7. … and besides the new features of data, we must also consider the new “data context” out there: expectations by our users data are changing, because the relationship between “people” and “data” are changing. People uses every day apps and online services (delivere by private companies) that deliver “data” in quasi-real time at very high level of spatial and temporal granularity (e.g., traffic condition). Official Statistics have in their mission to deliver “timely” statistics, but what “timely” used to mean is not the same as what “timely” means today. Think for instance of an “express parcel delivery service”: delivering a parcel after only a few weeks could be considered “express service” in the XIX century, given the transport technology available at that time. In the late XX century, “express delivery” meant “in a few days”. And now, it means “wihtin 24 hours”. In this example, the “mission” is formulated always in the same way (“express delivery” of a parcel, “timely publication” of statistics), but points to different degrees of user expectations (weeks, days, hours) that are tuned according to the available technology. The same concept applies to other quality dimensions: relevance, granularity etc. In order to “remain timely” when the definition of “timely” is changing, we must shorten the production delay. In order to remain “relevant”, when the definition of “relevant” is changing, we must improve the spatial and temporal granularity of our statistics, and define new statistics.
  8. Big data pilots on: Webscraping job vacancies; collaboration with CEDEFOP Webscraping of enterprise websites for different purposes: Business register, ICT usage statistics) Smart Electricity Meters for energy consumption statistics and analysis of consumption patterns, e.g. to determine occupation of buildings Automatic vessel identification system for transport and environmental statistics Flash estimates for short term enterprise statistics Combination of multiple data sources for population, tourism, agricultural statistics Pilots should produce input for horizontal topic work package on quality, methodology and IT infrastructures A new round of pilots is organised for 2018 (see business case on next slide) https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/Main_Page
  9. Say that with the Bucharest Memorandum we are marking the entry into the “trusted smart statistics” paradigm (third box) (Use new data in new ways along with traditional data), reckoning that using new data calls for “new ways” of making statistics. Getting past the “big data in official statistics” (second box) (use new data in the same ways as traditional data) was a non-obvious achievement, that took 4+ years of work (Ref. the past work in the ESS Task Force on Big Data, 1st ESSnet on Big Data, etc.). We should also distinguish our position from the “hype” that see big data completely replacing/subverting official statistics. Actually the values and the high level missions of official statistics should remain, and traditional data will be complemented by, not replace traditional data.
  10. Think of data as the fuel … survey & administrative data  traditional fuel new digital data  new fuel and the statistical system as an engine made of legal, methodological, organisational components, skills, processes … Today we need TWO engines 1st engine for survey and admin data  legacy collection & production system (with incremental innovations) 2nd engine for new digital data  Trusted Smart Statistics (must be designed on different principles)
  11. Errors considerations for survey data are mostly related to sampling methods; moving to admin data already required new approaches to errors and uncertainty. With new data sources that are tightly connected with technology-heavy generation processes, there is even more to learn by statisticians about sources of errors and uncertainty. Process data at source, share computation out Statistical production must be fully automated Confidentiality by SDC AND Privacy-Enhancing Tech. Open source code Big efforts in improving processing methods Statisticians must discover and learn the sources of error & uncertainty for new data
  12. In other words, while the previous model is based on “sharing data”, the second one is based on “sharing computation” First, it can be naturally combined with technologies for Secure PRivate Computing (aka privacy-preserving computation). These are cryptographic technologies have evolved recently in the last years, and some of them are now mature for commercial applications and adoption in production settings. Thinking of the computation process as a distillation process, that from grapes (i.e., raw input data) distills the desired information (i.e., grappa), these technologies allow us (the statistical system) to obtain the grappa without necessarily “seeing” or having access to the grapes. In other words, data sources can let us distill the grappa without having to disclose their grapes. This reassuers data subjects that their data cannot be possibly used for anything different purpose other than what was agreed, reported and regulated. Furthermore, sharing computation with the data source implies that we are sharing control over  the production process with them, which again reinforces trust and at the same time strengthens their participation and engagement, an aspect that is particulalry important when datas sources are individual citizens, as will become clear in the next slide. And last but not least, we must combine the new model with maximal transparency as to how we access and proces the data, moving from publishing manuals and cookbooks towards publishng the source code of all computation instances that we run on external data. And to allow, even promote actively public scrutiny over these algorithms and source code, yet another element that increases the level of public trust in how we use new data. All these elements are fundamental ingredients of the Trusted Smart Statistics moel that we are elaborating within the ESS.
  13. The new data ecosystem poses additional confidentiality challenges, also on the input side in addition to SDC on the output Modern Secure Private Computing technologies provide new means and re-define the meaning of “access”: “using data without sharing them” In addition to the problem of Statistical Disclosure Control on the output side, we now must care also of protecting confidentiality of the data on the input side, in the framework of “pushing computation out”. This is a new challenge that adds (not replace) to the SDC on the output side. Fortunately, new modern technologies exist today in the field of Secure Private Computing (also called “Privacy-Enhancing Technologies”), such as for example SMC, that can be used in combination with others and are fully consistent with the “pushing computation out” paradigm. We must build capacity, learn to use such novel technologies, and understand how to combine them with more traditional SDC means.
  14. Being “Smart” and being “Trusted” reinforce each other. As we aim to use much more pervasive and invasive data by citizens (“deeper data”) than was done in the past, we are going to face much higher risks (to individuals and to the whole society), and to prevent them we must put in place “stronger safeguards”, and particularly “hard” technological means in addition to legal protections (misuse of confidential data outside the scope of what was agreed and communicated should not only be “legally forbidden” but also “technically impossible”!), and much stronger transparency practices (e.g., publish source code, not only methodological manuals). And if such safeguards are put in place and correctly communicated (to the citizens and to the data holders) we achieve a higher level of “trust” (by the citizens and by the data holders) that enables us to ask for even more and deeper data ….
  15. These are 3 “grand challenges” that the ESS must face: developing new methodological frameworks (and methodologies), achieving sustainable access (probably by new legislation) and developing new interaction models with the external stakeholders (citizens and expert users) Each one of these “grand challenges” involves a number of sub-issues. In the next 3 slides we focus on the first challenge: methodological
  16. By their nature, new data sources can provide multi-faceted information serving multiple statistical domains. Thus, rather than pursuing domain-specific data collection, processing and analysis, new data sources would be (pre-)processed by data-aware and transformed into purpose-agnostic intermediate data that can be then used (possibly in combination with others) to derive final statistical indicators serving different application domains. To effectively use these new data sources and the huge investments associated with their exploitation, their development should target multi-purpose by design to foster multiple uses and ensure (horizontal) scalability of approaches. Novel statistical data and indicators shall be developed based on the fusion of multiple data sources, including combinations of traditional (survey data, administrative data) and new data sources;
  17. Methodological frameworks for multi-purpose statistics will necessarily look like “hourglass”, with a lower processing layer that is “input specific and output agnostic”, and an upper layer that is conversely “output specific and input agnostic”. The middle box represents a parsimonious representation of intermediate data, both input-agnostic and output-agnostic
  18. Layered organisation of data processing work flows Considering that new data are often generated as a by-product of technology-intensive processes, the development of new methodological approaches requires contribution by experts from disciplines that are outside the traditional knowledge field of official statistics (e.g. engineers, image processing experts). Generally speaking, in most cases the whole data processing flow (from raw input data to output statistics) can be split into two distinct segments, or macro layers. In the lower layer, the raw data are transformed into intermediate data with a clear structure, easy to be interpreted by statisticians. If the raw input data are unstructured (e.g. text or images), the lower processing layer includes algorithms to transform them into structured data (e.g. text classification and object recognition modules). All technology-specific and source-specific processing functions requiring highly specialised technical (non-statistics) knowledge should be confined into the lower segment. The upper processing layer takes as input the transformed intermediate data to compute statistical indicators serving various application domains (statistical domains). Engineering and technology-specific knowledge is needed exclusively in the lower processing layer, while professional statisticians can focus on the methodological development in the upper layer. Benefits of Layering Decouples the complexity & heterogeneity of the two domains Hides complexity & heterogeneity of MNO data to statisticians Hides complexity & heterogeneity of statistical concepts to MNO engineers Decoupling allows for independent development, adoption & evolution at each domain By their nature, new data sources can provide multi-faceted information serving multiple statistical domains. Thus, rather than pursuing domain-specific data collection, processing and analysis, new data sources would be (pre-)processed by data-aware and transformed into purpose-agnostic intermediate data that can be then used (possibly in combination with others) to derive final statistical indicators serving different application domains. To effectively use these new data sources and the huge investments associated with their exploitation, their development should target multi-purpose by design to foster multiple uses and ensure (horizontal) scalability of approaches. Novel statistical data and indicators shall be developed based on the fusion of multiple data sources, including combinations of traditional (survey data, administrative data) and new data sources;
  19. Activities in this area will profit from developments of current work related to the use of online job vacancies informing labour market and skills statistics, enterprise websites for information society and business statistics, and web information supporting tourism statistics. The aim is to build capabilities at ESS level for harvesting information from the web including acquisition channels, such as web scraping and the use of APIs. Data acquisition will also include conditions and agreements with trans-national players (owners/administrators of web platforms, web aggregators, etc.) and provide models and templates to support NSI in establishing national agreements with local players. Following the definition of a methodological framework, we aim at developing, implementing and maintaining a portfolio of text processing and analytic services at various levels (e.g. text parsing, mining, classification, interpretation). Initial focus will be put on the analysis of online job vacancies/skills and the EuroGroupsRegister, but the functionalities and architectures will be developed in a highly modular and flexible fashion, so as to prepare for future extensions to further application domains and use cases. Concepts should be based on state of the art architectures e.g. micro service and software-defined architecture (independence of technical infrastructure) ensuring evolvability and portability following the principles of the methodological framework. Application architectures should be highly flexible allowing joint processing of data as wells as use of source data and intermediate products at any stage of a possible processing chain at national or European levels. For the online job vacancies there is an opportunity for building a joint ESS system combining developments from the ESSnet Big Data II and the CEDEFOP agency that created a system for collecting data and extracting skills information for feeding policy demands of the European Commission in the area of the new skills agenda for the European Commission.
  20. The term “smart surveys” refers to surveys based on smart personal devices, equipped with sensors and mobile applications. Smart surveys involve (continuous) interaction with the respondent and with her personal device(s). They combine data collection modes based on active inputs from the data subject (such as responses to queries, shared images) together with data collected passively by the device sensors (e.g. accelerometer, GPS). The term “trusted smart surveys” refers to an augmentation of smart surveys by technological solutions that collectively increase their degree of trustworthiness and therefore acceptance by the citizens as well as the user. Constituent elements of a trusted smart survey are the strong protection of personal data based on privacy-preserving computation solutions, full transparency and auditability of queries and processing methods. Furthermore, such technological elements will be combined with a coherent strategy for public communication and individualised incentives based on a broad range of incentivizing approaches, including but not limited to personalised feedback, gamification, public rewarding, (pseudo)financial compensation, and others. Special focus will be given to Time Use Surveys and Household Budget Surveys and the functionalities will be developed in a fashion that facilitates extension to further use cases in other application domains and use cases. Note that the concept of smart surveys goes well beyond the mere use of web-based (online) data collection that essentially transforms the paper questionnaire into an electronic version. Among the capabilities developed under this priority area are the development of Secure Private Computing services to process highly detailed behavioural data (“deep data”) by respondents, and a set of incentive tools that can be used to compose survey-specific incentive strategies to foster participation by citizens.
  21. Electricity consumption / identifying energy consumption patterns Develop procedures, technical solutions Collection, processing and analysis Develop functional production prototypes Outputs Develop methodology for geo-spatial linking and define common quality measurements Develop standard visual outputs for electricity data. Validate the methods selected during the ESSNet big data I define requirements for classifying households as vacant or seasonally vacant. The main advantage of using the smart meter data are: possibility to link it with other data sources and gain new knowledge, the data source can be used to validate or improve current survey based statistics and the data source could improve the speed of producing statistics and also increase the quality of regional statistics. Aim: “To demonstrate by concrete estimates whether buildings equipped with smart meters can be used to produce energy statistics but can also be relevant as a supplement for other statistics, e.g. census housing statistics, household costs, impact on environment, statistics about energy production.” Objective Data from smart electricity meters are used to complement and replace data collection on consumption of electricity by households and by businesses. As part of the ESSnet Big Data activities, Statistics Estonia did a study on the feasibility of using smart meter data for producing information on electricity consumption. The roll-out plans of smart meters in Estonia expect full coverage in Estonia by 2017. Electricity consumption is one of the most important parts of energy data to create energy balance sheets and statistical data. Access to this data provides an opportunity to reduce the reporting burden on businesses. The Estonian transmission system operator Elering AS manages the Estonian electricity system in real time. For this purpose, they built a data hub. Data for statistical purposes is acquired from the data hub. Output is final energy consumption by economic activity, by region and monthly, quarterly and annual aggregation for businesses and final energy consumption by household characteristics as they are contained in household registers (size of dwelling, number of rooms and persons, …) by region and monthly, quarterly and annual aggregation. Another goal is to identify vacant dwellings and to verify real places of residence of households and related persons. From survey data, it is estimated that 20% of residents do not live in places where they are registered.   Results achieved The production of electricity consumption statistics of households and businesses by various characteristics on a monthly basis and spatially disaggregated is feasible. Key factor for success is linking the smart meters to dwellings, households and businesses from the respective registers. Empty dwellings could be identified and the results of the survey could be replicated. However, the granularity in terms of time and spatial disaggregation can be considerably improved. The main advantage of using the smart meter data are: - possibility to link it with other data sources and gain new knowledge, - validate or improve current survey based statistics and - improve considerably the production speed, temporal granularity and the spatial disaggregation of regional statistics.   Success factors/lessons learnt The project was conducted and delivered successful results because access to the data was possible. It has been based on current legislation. The data has to come with adequate information on the data source and the data. Metadata is crucial for understanding and correctly analysing the data. Additional information is necessary to identify the metering point recording the final consumption. Some metering points only transfers electricity. However, this issue could also lead to new knowledge on the structure of the electricity network and the trading of electricity. Matching of smart meter data with register data is crucial. Issues of identifying the actual consumers instead of the contract owner were recognised. Address information should be standardized or at least harmonised. Modelling is necessary, e.g. to assign consumption from dwelling level to single apartments, machine learning for identifying unoccupied households. Constant relationships with data providers are required and IT systems have to be designed to receive regular data flow. Analysing the potential of a new data source triggers improving existing data sources and enhances their quality. In addition, new applications are detected in the process, e.g. correlate patterns of electricity consumption with economic activity of businesses, use patterns for forecasting of major economic indicators, e.g. national accounting, infer from consumption pattern to type of energy usage, e.g. heating.
  22. Activities within this priority area establish a provision of information processed at EU level related to long-distance transportation and logistics, specifically prepared to serve official statistics purposes. The focus here is on non-personal data that can be centralised without causing privacy risk concentration issues. The information will be acquired from the relevant owner/partner organisation(s) at EU level. Rather than acquiring raw data the focus will be laid on pre-processed and/or partially aggregated data (e.g. from event-based data to trip-based data). The pre-processing methods and functions that are run at the source premises shall be designed by, co-developed with or at least reviewed by the dedicated expert groups from the ESS. Use of data, information, derived products and services will follow the same principles described above for the Web Intelligence priority area. Initial focus will be given to ship tracking data (Automatic Identification System, AIS) data as currently gathered by EMSA (European Maritime Safety Authority). Data will be used to produce statistics in the domain of transport and fisheries. At a later stage, activities could extend to other domains such as air traffic data (passengers and cargo), railway traffic and possibly other modes of transports for goods and people. Transport data could contribute to producing flash estimates of economic indicators and be used in the context of tourism statistics.
  23. The crucial expectation from the earth observation project is identification and analysis of EO data sources for multiple statistical themes product and development of an adequate Reference Methodological Framework for processing data. As a physical deliverables the developer's and user's guidelines at European and national level will be prepared. Additionally the collaboration with the scientific community will be established in order to strengthen the substantive side of the project. The project is divided into thematic tasks: Agriculture Build-up area (SDG/Sustainable Cities and Communities) Land cover Settlements, Enumeration Areas and Forestry The main purpose of the “Agriculture - Crop recognition, mapping and monitoring” case study is to use Sentinel‑1 and Sentinel-2 satellites for agricultural crops mapping and area estimates in Northern Europe conditions. – Monitoring of the off-season vegetation cover The general objective of this case study is to monitor the off-season vegetation cover of agricultural soils in high-latitude agricultural systems. This gives important information on nutrients losses from fields to water bodies. The statistical product would be related to the UN's Sustainable Development Goals (SDGs), Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture. This method would provide grounds for establishing an indicator on sustainable agriculture as land management practices closely relate to sustainability. – Implementing SDG indicator 11.7.1 The SDG indicator 11.7.1 proves a very suitable case study to achieve these goals. (Indicator 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities). Indicator 11.7.1 has several interesting concepts that required global consultations and consensus. These include; built-up area, cities, open spaces for public use, etc. As a custodian agency, UN-Habitat has worked on these concepts along with several other partners. Finally, UN-habitat has just released, in July 2018, a methodology that might be helpful for the implementation of this indicator. This methodology mostly relies on satellite imagery. Urban Sprawl This case study will be focused on the characterization of urban sprawl across Urban areas in Europe. Urban sprawl was only recently officially acknowledge as an issue in Europe (EEA, 2016) and numerous attempts at characterizing urban sprawl have been made in recent years however a consensus remains to be reached. This study will attempt to characterize urban sprawl across urban areas at a pan European scale by means of data-driven machine learning methods. Further, the study will investigate the possibility of providing temporal continuity on the basis of multiple datasets provided by satellites such as MODIS and SENTINEL 2 Combination of administrative and Earth Observation data to determine the quality of housing Land cover maps at very detailed scale The aim of this case study is to realize a land cover map at various scale by four bands aerial and satellite (Sentinel and LANDSAT) images based on 1st LUCAS (Land Use/Cover Area frame Survey) level legend. Machine learning algorithms will be used (i.e a segmentation algorithm based on CNN and Unet in order to recognize built-up artificial areas). Update the INSPIRE Theme Statistical Units dataset and preventing forest fire The main goal within this case study is to explore the Copernicus data in order to: update the INSPIRE Theme Statistical Units dataset, namely the Settlements and Enumeration Areas. The process will contribute to build the geospatial framework to support 2021 Census. explore the possibility of studying the forest and the eucalyptus plantation and its impact in preventing forest fire.
  24. We should go beyond the dichotomy between “applying established methods” (= the job for statistical office) and “inventing new ideas” (= the job of researchers). When changing the paradigm, you need to “invent and apply new methods inspired by new ideas”  this is a JOINT work for statistical offices AND research organisations. Building the Trusted Smart Statistics is a matter of paradigmatic change, not incremental evolution not just the ability to provide new solutions to known problems, but to (re)formulate the problems and anticipate needs …