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

Ekkehard Petri GISCO Eurostat 1

Update on EUROSTAT activities

A second hand experience

07 October 2010 EFGS Meeting 2010 Den Haag 2

LUCASCensus 2010SDMX


LUCAS data collection process

1 100 000 points

LAND COVER classes1 ARABLE LAND2 PERMANENT CROPS 3 GRASSLAND4 WOODED AREAS AND SHRUBLAND5 BARE LAND, RARE VEGET.6 ARTIFICIAL LAND7 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


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

Sampling strategy: Second phase sampling design


A10 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

Land Cover nomenclature LUCAS 2009

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


U110 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

Land Use nomenclature LUCAS 2009


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


MS covered 2006 2007 2008 2009 MS covered 2006 2007 2008 2009

Country 2006 2007 2008 2009 Country 2006 2007 2008 2009

Austria X Italy X X X

Belgium X X X Latvia X X

Bulgaria X Lithuania X X

Czech Republic X X X Luxembourg X X X

Cyprus Malta X

Denmark X Netherlands X X X

Estonia X Poland X X X

Finland X Portugal X

France X X X Romania X

Germany X X X Slovak Republic X X X

Greece X Slovenia X

Hungary X X X Spain X X X

Ireland X Sw eden X

Data availability per country/year


Census


EU census goals

• 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


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!


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

What data for what geographical area?


Population topics

SexAgeLegal marital status

Country/place of birthCountry of citizenship

Place of usual residence one year prior to the census(Size of the) Locality

Household statusType of private householdSize of private household

Family statusType of family nucleusSize of family nucleus

Total populationPlace of usual residenceRelationships between household members

What data for what geographical area ?

LAU 2

Housing topics

Occupancy status of conventional dwellings

Number of occupantsUseful floor space and/or Number of roomsDensity standard

Dwellings by type of buildingDwellings by period of construction

Type of living quartersLocation of living quarters


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.


• 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.

What we can do for GISCO ?


Census Hub project: architecture

WS

database

WS

database

WS

database


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


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


SDMX


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)


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


SDMX is not just a data transmission format…

Similarities with INSPIRE are substantial

SDMX components

Information model for data and metadata

Syntax for automatic exchange of data and metadata

Information model for data and metadata

Syntax for automatic exchange of data and metadata

Guidelines to Harmonise ContentsGuidelines to Harmonise Contents

IT Architectures for data exchangeIT Architectures for data exchange

IT tools to support implementation and to disseminate SDMX dataIT tools to support implementation and to disseminate SDMX data


SDMX Components: Information Model

Statistical data Metadata

– Structural– Conceptual– Quality– Methodology

Data exchange process


SDMX Information Model

Dataset

Structure

Definition

DSD

Dataset

Structure

Definition

DSD

DataData

Structural

Metadata

Structural

Metadata

Dimensions

(ex: country, variable/topic,

year)

Dimensions

(ex: country, variable/topic,

year)

Attributes

(ex: unit of measure)

Attributes

(ex: unit of measure)

Code listsCode lists

Metadata about an individual value, a time series or a group of time series

Metadata about an individual value, a time series or a group of time series

Provides a way of modelling statistical data, metadata and data exchange processes.

Describe


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 http://

epp.eurostat.ec.europa.eu/portal/page?_pageid=2733,61942355,2733_61942368&_dad=portal&_schema=PORTAL

http://epp.eurostat.ec.europa.eu/portal/page?_pageid=2733,61942355,2733_61942368&_dad=portal&_schema=PORTAL






SDMX Registry

Repository

Structural metadata

CodeLists

ConceptSchemes

DSDs

Provision of information

Dataflows

Provision agreements

Accessible via a Web Service accepting SDMX-ML

messages

Accessible via a Web Service accepting SDMX-ML

messages

Graphical User Interface (GUI) for user interaction over

the Web

Graphical User Interface (GUI) for user interaction over

the Web

DSW – “standalone” Java GUIDSW – “standalone” Java GUI

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

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