1. Update on EUROSTAT activities
A second hand experience
Ekkehard Petri GISCO Eurostat

2. LUCAS
Census 2010
SDMX
07 October 2010
EFGS Meeting 2010 Den Haag

3. LUCAS data collection process
points
LAND COVER classes
1 ARABLE LAND
2 PERMANENT CROPS
3 GRASSLAND
4 WOODED AREAS AND SHRUBLAND
5 BARE LAND, RARE VEGET.
6 ARTIFICIAL LAND
7 WATER
First phase sample for stratification: orthophoto interpretation
2km grid
Ground survey
Parameters
Land cover
Land use
pictures
etc.
Sample of around 260,000 pts
Second phase sample: in-situ data collection
07 October 2010
EFGS Meeting 2010 Den Haag

4. Second phase sampling design
Sampling strategy:
Second phase sampling design
Definition of sample size by strata
Optimal size by NUTS2 and strata based on fixed precisions for a set of LC classes targeted by country
Points selection
LUCAS 2006 sample points included as much as possible (land cover/use changes can be detected)
Maximisation of the distance between points
Exclusion of remote points and points above 1000m
07 October 2010
EFGS Meeting 2010 Den Haag

5. Land Cover nomenclature LUCAS 2009
Artificial Built-up areas
A20
Artificial non built-up areas
B10
Cereals (+ triticale)
B20
Root crops
B30
Non permanent industrial crops
B40
Dry pulses, vegetables and flowers
B50
Fodder crops
B70
Fruit trees & berries B8
Other Permanent Crops
C10
Broadleaved and evergreen woodland
C20
Coniferous woodland
C30
Mixed woodland
D10
Shrubland with sparse tree cover
D20
Shrubland without tree cover
E10
Grassland with sparse tree/shrub cover
E20
Grassland without tree cover
E30
Spontaneous vegetation
F00
Bare Land
G10
Inland water bodies
G20
Inland running water
G30
Coastal water bodies
G50
Glacier, permanent snow
H10
Inland marshes
H20
Peatbogs
H30
Salt-marshes
H40
Salines
H50
Intertidal flats
A10
Artificial Built-up areas
 A11
Buildings with one to three floors
A12
Buildings with more than three floors
A13
Greenhouses
A20
Artificial non-built up areas
 A21
Non built-up area features
A22
Non built-up linear features
07 October 2010
EFGS Meeting 2010 Den Haag

6. Land Use nomenclature LUCAS 2009
Agriculture ( + Kitchen garden + Fallow land)
U120
Forestry
U130
Fishing
U140
Mining, Quarrying
U150
Hunting
U210
Energy production
U220
Industry & Manufacturing
U310
Transport, communication, …
U320
Water & waste treatment
U330
Construction
U340
Commerce, Finance, Business
U350
Community Services
U360
Recreation, Leisure, Sport
U370
Residential
U400
Unused
07 October 2010
EFGS Meeting 2010 Den Haag

7. Data availability Types of data
Tabular microdata on the first and second phase sample (land cover/use on the specific point, LC/LU change in the specific point etc.)
Pictures (four cardinal directions)
Aggregated estimates (NUTS1/NUTS2 depending on LC classes)
Years
2006
2008/2009 (from march 2010 on)
Terms of use
An agreement has to be signed between DG-ESTAT and users about:
Confidentiality: only aggregated data can be disseminated
07 October 2010
EFGS Meeting 2010 Den Haag

8. Data availability per country/year
07 October 2010
EFGS Meeting 2010 Den Haag

9. Census
07 October 2010
EFGS Meeting 2010 Den Haag

10. EU census goals Same reference year (first time: 2011)
Comparability of census data on the EU level
Same reference year (first time: 2011)
Same ‘topics’ (variables)
Use of harmonized definitions and technical specifications
Use of identical breakdowns of the topics
Unified dissemination programme (hypercubes)
=>Common Baseline across countries
Transparent quality of census results
Quality reports
Detailed tables on quality of the data
Metadata
07 October 2010
EFGS Meeting 2010 Den Haag

11. EU census limits What does the regulation not provide?
No access to microdata
No possibility to define geographical areas flexibly
No harmonised confidentiality control
No normative minimum quality requirements (quality thresholds)
No consolidation of census results form different Member States
BUT
Member States are free to do more!
07 October 2010
EFGS Meeting 2010 Den Haag

12. What data for what geographical area?
NUTS2:
Year of arrival in the country
Educational attainment
Location of place of work
Current activity status
Occupation
Industry
Status in employment
Tenure status of households
Housing arrangements
Type of ownership (of dwellings)
Water supply system, Toilet facilities, Bathing facilities, Type of heating
07 October 2010
EFGS Meeting 2010 Den Haag

13. What data for what geographical area ?
LAU 2
Population topics
Sex
Age
Legal marital status
Country/place of birth
Country of citizenship
Place of usual residence one year prior to the census
(Size of the) Locality
Household status
Type of private household
Size of private household
Family status
Type of family nucleus
Size of family nucleus
Total population
Place of usual residence
Relationships between household members
Housing topics
Occupancy status of conventional dwellings
Number of occupants
Useful floor space and/or Number of rooms
Density standard
Dwellings by type of building
Dwellings by period of construction
Type of living quarters
Location of living quarters
07 October 2010
EFGS Meeting 2010 Den Haag

14. What we can NOT do for GISCO ?
The municipalities as smallest geographical area for the census data to be transmitted to Eurostat (LAU 2 level) are fixed. No flexibility to define areas freely.
After long and detailed consultation with the Census experts from the Member States, the foreseen obligatory statistical programme represents a balance between the desirable and the feasible.
Eurostat does not have access to census microdata.
Confidentiality control is done by the NSI.
07 October 2010
EFGS Meeting 2010 Den Haag

15. What we can do for GISCO ?
Usage of common definitions, technical specifications and breakdowns makes census data better comparable at the European level.
Intensive description and quality reporting of the NSI on the data sources and methodology they use to do the population and housing census. This might help to develop small area reporting systems.
Key topics will be required for the LAU 2 level. It is likely that some of the data might also be available for even smaller areas in some Member States.
Eurostat organizes a task force on Census Data Disclosure Control which aims at proposing best methodology and practice to protect census data with minimum damage to disseminated results.
The Census Hub might be used to exchange and disseminate small area data from censuses.
07 October 2010
EFGS Meeting 2010 Den Haag

16. Census Hub project: architectureWS WS WS
database
database
database
07 October 2010
EFGS Meeting 2010 Den Haag

17. The Census Hub project
The Census Hub project aims to build a new IT infrastructure to achieve the data exchange between the National Statistical Institutes (NSI), Eurostat and the users of census data using SDMX standards.
Data sharing architecture
Based on the agreed hyper-cubes with harmonised data
Confidentiality problems handled at national level
A data user browses the hub to define a dataset of interest via structural metadata (dimensions, attributes, measures, code lists, etc). Data are retrieved directly from the interested Member States’ systems
07 October 2010
EFGS Meeting 2010 Den Haag

18. Present and ongoing activities
Pilot project in Germany, Ireland, Italy and Portugal
Guideline explaining how to implement an SDMX MSs architecture in the Census Hub context available
07 October 2010
EFGS Meeting 2010 Den Haag

19. SDMX
07 October 2010
EFGS Meeting 2010 Den Haag

20. What is SDMX “Statistical Data and Metadata Exchange”
SDMX preferred standard for exchange and sharing of data and metadata in the global statistical community
Sponsors include
European Central Bank (ECB)
Eurostat
Organisation for Economic Co-operation and Development (OECD)
United Nations Statistical Division (UNSD)
The initiative, started in 2001, is sponsored by 7 international organisations: BIS, ECB, Eurostat, IMF, OECD, UN, WB
Among other things SDMX Standards are seen as facilitating use of Internet-accessible databases in order to be able to retrieve data as soon as they are released.
07 October 2010
EFGS Meeting 2010 Den Haag

21. Benefits from SDMX standards
Covers potentially all statistical domains
Open to all stakeholders
Are neutral in terms of underlying commercial technologies
Demography and the Census hub already implemented
3) This means that investing in SDMX is a good reason not only for improving the data exchange towards International organizations but also on something useful inside own Institution
07 October 2010
EFGS Meeting 2010 Den Haag

22. SDMX components Information model for data and metadata
Syntax for automatic exchange of data and metadata
Guidelines to Harmonise Contents
IT Architectures for data exchange
IT tools to support implementation and to disseminate SDMX data
SDMX is not just a data transmission format…
Similarities with INSPIRE are substantial
SDMX is not just a data transmission format.
It provides a complete solution to model data and metadata to be transmitted together,
and a syntax to make data and metadata exchanges automatic.
Besides, adopting the SDMX standard gives the chance to harmonise objects commonly used in the statistical world,
like code lists, concept descriptors etc. This is the aim of the content oriented guidelines.
To facilitate data exchanges, SMDX envisages three different IT architectures (push, pull and hub).
Finally, SDMX implementation and dissemination are supported by a set a free IT tools available on the SDMX web site.
Note that these tools are not part of the SDMX standards.
07 October 2010
EFGS Meeting 2010 Den Haag 22 2

23. SDMX Components: Information Model
Statistical data
Metadata
Structural
Conceptual
Quality
Methodology
Data exchange process
07 October 2010
EFGS Meeting 2010 Den Haag

24. SDMX Information Model
Provides a way of modelling statistical data, metadata and data exchange processes.
Dimensions
(ex: country, variable/topic, year)
Dataset
Structure
Definition
DSD
Code lists
Structural
Metadata
Attributes
(ex: unit of measure)
Describe
SDMX provides a way of modelling statistical data, metadata and data exchange processes.
The data (and related metadata) for a particular statistical domain are structured according to a "Data Structure Definition" (DSD, previously known as a "key family").
Structural metadata are those metadata acting as identifiers and descriptors of the data.
If we consider a statistical multi-dimensional table, identifiers and descriptors are generally the names of variables or dimensions.
Data must be associated to some structural metadata, otherwise it becomes impossible to properly identify, retrieve and browse the data.
Descriptors can be dimensions when they identify the data or attributes when they only describe the data.
Attributes are metadata about an individual value, a time series or a group of time series.
Code lists are defined when dimensions or attributes are coded values.
The DSD describes the structure of a particular statistical data flow through a list of dimensions (for example: country, variable/topic, year),
a list of "attributes" (for example, unit of measure) and their associated code lists.
A "data flow", in this context, is an abstract concept of the data sets, i.e. a
structure without any data. While a data structure definition defines
dimensions, attributes, measures and associated representation that
comprise the valid structure of data and related metadata contained in a
data set, the dataflow definition associates a data structure definition with
one or more category. This gives a system the ability to state which data
sets are to be reported for a given category and which data sets can be
reported using the data structure definition.
Metadata about an individual value, a time series or a group of time series
Data
07 October 2010
EFGS Meeting 2010 Den Haag 24

25. SDMX Components: IT Tools
SDMX Registry
Tools to create data definitions and metadata
Tools to convert and validate data and metadata
Tools to visualise data and metadata
Training available from Eurostat
07 October 2010
EFGS Meeting 2010 Den Haag

26. SDMX Registry Repository Structural metadata Provision of information
Graphical User Interface (GUI) for user interaction over the Web
Structural metadata
Provision of information
DSW – “standalone” Java GUI
CodeLists
Dataflows
ConceptSchemes
Provision agreements
DSDs
Accessible via a Web Service accepting SDMX-ML messages
Overall the SDMX Registry is an application which is accessible
to other programs over the Internet (or an Intranet or Extranet),
to provide information needed to facilitate the reporting, collection,
and dissemination of statistics. In the same way that a human being
might use a browser to go to Google and search for some information,
the SDMX Registry can be accessed by computer programs to locate and access statistics.
All communications are performed using SDMX-ML XML messages.
The SDMX Registry is not a single, centralized resource for the entire world to use.
The SDMX registry can be accessed by:
User, with an Internet connection and using their CIRCA user ID.
Applications like the Dataset Structure Wizard.
Any other web applications using web service accepting SDMX-ML messages.
07 October 2010
EFGS Meeting 2010 Den Haag 26