1. WP7 MULTI DOMAINS

2. WP7 Multi domains WP7 Multi domains WP7 Multi domains

3. 1. Population

4. 2. Tourism/border crossing

5. 3. Agriculture

6. Country leaders of each domain
WP7 TEAM
Janusz Dygaszewicz
Project Manager
of Polish work
Jacek Maślankowski
Coordinator of methodology
Anna Nowicka
Leader
cooperation
PARTNERS
Piet Daas
John Sheridan
Nigel Swier
Regional statistical office in Poznań
Regional statistical office in Bydgoszcz
Population
Regional statistical office in Rzeszów
Department of Social Research
Tourism/
border crossing
Department of Agriculture
Regional statistical office in Olsztyn
AGRICULTURE
Coordinator of domain area (SGA-1)
Cooperation on domain area
Cooperation on domain area
Country leaders of each domain

7. Aim of WP7 is to find out how a combination of:
Big Data sources
administrative data
statistical data
may enrich statistical output in domains:

8. WP7 - Future perspectives
Suggest pilots and domains with successful implementation potential for further elaboration
in the second wave of pilots in 2018

9. WP 7 – General tasks Data access (SGA-1) Data feasibility (SGA-1)
Data combination (SGA-2)
Summary plus future perspectives (SGA-2)

11. Milestones and deliverables(SGA-1)
Progress and technical report of internal WP-meeting;
by M4
Milestone 2.
List of availability Big Data sources in the domain(s); by M8
Milestone 3. Recommendation for using two or three Big Data sources in the domain(s); by M12
DELIVERABLE
the partial report for each domain containing basic information on:
The data access (with legal and privacy aspects)
The data quality issues
The methodology (focus also on combining data)
The technical aspects
by M13
We are here now

12. TASK 1 & TASK 2 BRAINSTORMING RESULTS QUESTIONNAIRE RESULTS
INTERNAL MEETING
MILESTONE 7.4
PROGRESS AND TECHNICAL REPORT OF INTERNAL WP-MEETING
MILESTONE 7.5 „LIST OF AVAILABLE BIG DATA SOURCES IN THE DOMAIN(S)”

13. Why did we do the brainstorm?
to create the widest possible range of Big Data sources (a cafeteria); possible sources of data that public statistics could use for new developments or supplement existing ones, so that in the later stages these sources can be verified from different points of view and gradually part of them will be eliminated as the least useful.
to analyze as many as possible use cases of using Big Data sources
to take into account the most popular source
Big Data is a new phenomenon we should take into account that the potential of each source may still change.

14. to the QUESTIONNAIRE
From BRAINSTORMING

15. Why did WP7 carry out the questionnaire?
to find out more about the possibilities of technical, methodological quality, access in different countries
recommending the source to the pilots after 2018 to know the plans for Big Data of different countries
questionnaire was sent to countries outside the FPA (but EU country), because we recommend beyond the period of its duration
recognize the obstacles of using Big Data sources

16. The questionnaire results

17. Questionnaire - results

18. Results

19. Results Population Agriculture Tourism
Respondents were asked i.e. to indicate domain assuming, that the data source is accessible. For each of three domains (Population, Agriculture and Tourism/border crossing) respondents indicated the most promising BD sources:
Mobile sensors (tracking) – Mobile phone location;
Social Networks;
Data produced by Public Agencies;
Internet searches;
Websites;
Population
Mobile sensors (tracking) – Satellite images;
Agriculture
Data produced by business – Credit cards;
Traffic sensors.
Tourism

20. Common WP6 & WP7 face to face meeting took place on 28-30 of June in Warsaw1.
Exchange of information/experience in using BD sources and arrangements for future work WP7 2.
Build the list of potential sources for each domain 3.
Preparation and establish a framework for cooperation to SGA-2

21. Results Access Legal Quality Organization IT Methodology Agriculture
Tourism/
Border crossing
Organization
Agriculture IT
Population
Methodology

22. Results
The results were used to elaborate the next milestone (Milestone 2):
„List of availability Big Data sources in the domain(s)”; by M8

23. Use cases for SGA-2 Domain
List of available Big Data sources in the domain(s)
Domain
Population
Agriculture
Tourism/Border Crossing
Name of the use case
Everyday citizen satisfaction
Estimation of Agricultural statistics – pilot case study on crop types based on satellite data
Border movement
Big Data source
Social media/blogs/Internet portals
Satellite images
Traffic sensors
Responsibility
UK – coordinator (SGA-1)
RSO Poznań/Bydgoszcz
Department of Agriculture, RSO Olsztyn + IE
RSO Rzeszów, Department of Social Survey + NL
Brief overview of the methodology
Webscraping Data/Text/Web mining Machine learning
combining data – data fusion on radar and optical remote sensing data; data comparison with traditional surveys e.g. FSS; combining data – administrative data sources with satellite data.
Intertemporal disaggregation and interpolation, Latent variable models,
Cross entropy econometrics.

24. Use case for POPULATION
„Everyday citizen satisfaction „
Responsibility: UK – coordinator, supported by PL, PT
Data sources: Social media/Blogs/Internet portals
Methodology: Webscraping, Data/Text/Web mining, Machine learning
The goal of the case study:
to examine the level of daily satisfaction by analyzing the content of messages for the presence of defined expressions describing emotional states, e.g., happiness, joy, sadness, fear, anger;
to present the moods of people associated with various public events;
to observe morbidity areas, e.g., flu.
Plan of Combining Datasets: Combine in one repository the selected data from all Big Data sources, Comparison with the results of social studies to add more detailed information, Supplement of information gained in social studies.
Main benefits and value added for official statistics: Support traditional European Social Survey, supplement of the research methodology of some phenomena that are difficult to measure through traditional polls.
Everyday citizen satisfaction

25. Use case for POPULATION
„Everyday citizen satisfaction„
Responsibility: UK – coordinator, supported by PL, PT
Data sources: Social media/Blogs/Internet portals
Methodology: Webscraping, Data/Text/Web mining, Machine learning
The goal of the case study: to examine the level of daily satisfaction by analyzing the content of messages for the presence of defined expressions describing emotional states, e.g., happiness, joy, sadness, fear, anger;
to present the moods of people associated with various public events;
to observe morbidity areas, e.g., flu.
Plan of Combining Datasets: Combine in one repository the selected data from all Big Data sources, Comparison with the results of social studies to add more detailed information, Supplement of information gained in social studies.
Main benefits and value added for official statistics: Support traditional European Social Survey, supplement of the research methodology of some phenomena that are difficult to measure through traditional polls.

26. Use case for TOURISM/ BORDER CROSSING
Border movement

27. Use cases for TOURISM/ BORDER CROSSING
„Border movement”
Responsibility: PL – coordinator, supported by NL and PT.
Data sources: Traffic sensors.
Methodology:
intertemporal disaggregation and interpolation;
latent variable models;
cross entropy econometrics.
The goal of the case study: to estimate border traffic through internal border of EU (Polish-German, Polish-Slovakian, Polish-Czech and Polish-Lithuanian border) also regarding to some mirror statistics. Partial estimation of domestic traffic may be an extra result.
Plan of Combining Datasets:
Intertemporal disaggregation of data if it is the case (data frequency issue);
Latent variable model for data imputation for roads without traffic sensors;
Data smoothing if needed;
Preparing comparable data sets (common set of variables);
Combining traffic data from different sources with cross-entropy econometrics method.
Main benefits and value added for official statistics: Decreased burden of interviewers, more detailed results than from the survey solely, data consistent with mirror statistics.

28. Use case for AGRICULTURE
Estimation of Agricultural statistics
– pilot case study on crop types based on satellite data

29. Use case for AGRICULTURE
Estimation of Agricultural statistics
– pilot case study on crop types based on satellite data
Responsibility: PL – coordinator, supported by IE.
Data sources: Satellite images, administrative data, in situ surveys.
Methodology:
combining data – data fusion on radar and optical remote sensing data;
data comparison with traditional surveys e.g. FSS;
combining data – administrative data source s with satellite data. 
The goal of the case study: Crop type: look at the types of crops being grown and see if we can tell this accurately from the imagery; analysis of possibilities of using satellite images.
Plan of Combining Datasets: Data fusion – combining data sources by spatial reference.
Main benefits and value added for official statistics: Increase the quality of the agricultural surveys; Decrease of respondents burden; More detailed data published by official statistics; Potential decrease of the cost of conducting surveys.