1. The Tandem Consortium, M T-L
Tandem II, Towards a common geographical base for statistics across Europe
Meeting of the Working Party “ Geographical Information System for Statistics” Luxembourg, 24 October 2003
Marja Tammilehto-Luode, Statistics Finland
October 24, 2003
The Tandem Consortium, M T-L

2. The Tandem Consortium, M T-L
Lars Backer, Statistics Sweden Martin Ralphs, Office for National Statistics, UK Marja Tammilehto-Luode, Statistics Finland
Work funded by a grant of European Commission (85%) and the participating NSI’s (15%)
Joint work programme with 7 work packages
Grant agreement August 2002
Operation June August 2003
October 24, 2003
The Tandem Consortium, M T-L

3. The Tandem Consortium, M T-L
The first report:
datashop/print-catalogue/EN?catalogue =Eurostat&collection=12-Working% 20papers%20and%20studies [Theme: General Statistics] (Issue Date: 18/09/2002)
PDF -file 154 pages
with pictures 200 pages
October 24, 2003
The Tandem Consortium, M T-L

4. Structure of the presentation
How to build an SSAS (System of Small Area Statistics) for Europe
Development of delineation methods
Comparison of grid-based and polygon-based approaches in zoning/delineation
Guidelines for dissemination of small area statistics
Conclusions
October 24, 2003
The Tandem Consortium, M T-L

5. How to build an SSAS Vision
Framework of spatial data (of NSIs), both descriptive and analytical information
For data capture and spatial analysis
- To improve comparable territorial data - To enable proper spatial analysis - To minimise effects of MAUP and ecological fallacy
Scalability to enable a hierarchy of comparable territories
Harmonised with ESDI and INSPIRE with emphasis on analytical information
October 24, 2003
The Tandem Consortium, M T-L

6. How to build an SSAS Vision
Based on user needs - requirements of H-ESDI, E-ESDI and I-ESDI
Basic methods for processing and analysing spatial data
Harmonised dissemination - better access to data
A framework for territorial sampling
October 24, 2003
The Tandem Consortium, M T-L

7. = SSAS Comparable territorial areas Comparable territorial statistics
Building Blocks or Methods
Data
Comparable Statistics
Grids and Blobs
October 24, 2003
The Tandem Consortium, M T-L

8. How to build an SSAS Development
General view - macro level - Requirements of spatial data at different levels of spatial development planning, at H-ESDI, E-ESDI, I-ESDI - Macro processes - to provide qualified information Top-down co-ordination
Case studies - micro level - Reference data, examples of best practices - Urban delineation and inner differentiation - Sub processes - techniques, benchmarking environment Bottom-up implementation
October 24, 2003
The Tandem Consortium, M T-L

9. How to build an SSAS Development
Data (statistics), Features (geographies) and Methods (conversions, delineation) standardised as far as possible
Seed areas - Building blocks - Hierarchies of comparable areas of different scale
Building blocks reflect different data requirements of the three topic areas (H-ESDI, E-ESDI, I-ESDI)
Features presented by their borders (irregular or regular tessellation) or centre points with links to data
October 24, 2003
The Tandem Consortium, M T-L

10. How to build an SSAS Development
Standard links between statistics and map data
Standards to compile point-based and area -based statistics (blobs and grids)
Standards for the system of grids; coding, hierarchy, projection, co-ordinate system, grouping
Basic methods for processing and analysing
October 24, 2003
The Tandem Consortium, M T-L

11. How to build an SSAS Implementation
Iterative process of continuous development - Design phase - Prototype phase - Implementation phase
By restructuring and refinement of current systems
By studying crucial questions of Data, Features and Methods
Focus on user needs - Requirements of H-ESDI, E-ESDI, I-ESDI - Requirements of spatial data at different levels of spatial planning - Case studies - concrete projects
October 24, 2003
The Tandem Consortium, M T-L

12. How to build an SSAS Implementation
Establishment of a network for co-operation
Grouping of countries according the traditions of data capture (census data) to promote development
Integration into INSPIRE and SDI (co-operation, initiatives)
Top-down co-ordination - Bottom-up implementation - Standards - building national SSASs
October 24, 2003
The Tandem Consortium, M T-L

13. Development of delineation methods Case studies
WP3: Delineation by Automated Zoning Method (AZM)
WP4: Delineation by Interpolation (Kriging)
WP5: Delineation by AZM and Kriging
October 24, 2003
The Tandem Consortium, M T-L

14. WP3: Delineation by Automated Zoning Method
AZM methodology (ZDES) by two approaches (blobs and grids)
Delineation of areas of social deprivation (multivariable definition)
Area-based optimisation method - can be used for optimisation of seed areas to ”Building Blocks”
Produces better between-area variation than the standard geography (NUTS5)
Problems - Definition of number of output areas - Computationally intensive - Islands - Consistent across replication of the algorithm for the same source of BB?
October 24, 2003
The Tandem Consortium, M T-L

15. The Tandem Consortium, M T-L
Optimisation using a similarity criterion
October 24, 2003
The Tandem Consortium, M T-L

16. The Tandem Consortium, M T-L
October 24, 2003
The Tandem Consortium, M T-L

17. WP4: Delineation by Interpolation (Kriging)
Interpolation method with point-based data of different resolutions
Kriging by Surfer (ArcGIS in WP5) - “fixed Kriging” when data have total coverage (e.g. register data by real estate)
Outer delineation and inner differentiation of urban areas - by isolines of certain (common) values
Fast, simple, effective method
Results highly dependent on quality (coverage) of input data
Scale of results needs to be considered
May be suited to any definition based on densities
Results not “exact” - for administrative purposes
October 24, 2003
The Tandem Consortium, M T-L

18. The Tandem Consortium, M T-L
Kriging
Interpolation based on a statistical model that includes autocorrelation.
1) Construction of a variogramme from the scatter point set to be interpolated.
2) Computation of weights used in kriging
where n is the number of scatter points in the set, fi are the values of the scatter points, and wi are the weights assigned to each scatter point
October 24, 2003
The Tandem Consortium, M T-L

19. The Tandem Consortium, M T-L
October 24, 2003
The Tandem Consortium, M T-L

20. WP5: Delineation by AZM and Kriging
Outer delineation of urban areas with three different methods - Classification, AZM, Kriging
LFS criteria of densely populated area (min 500inh/km2, min total pop. 50,000)
Area-based data: NUTS5, postal code area, 1km2 grid Point-based data: as above but by their centre points
Results are highly dependent on Data (variables, statistics), Features (building blocks and their configuration) and Methods (e.g. AZM, interpolation…)
October 24, 2003
The Tandem Consortium, M T-L

21. The Tandem Consortium, M T-L
October 24, 2003
The Tandem Consortium, M T-L

22. The Tandem Consortium, M T-L
October 24, 2003
The Tandem Consortium, M T-L

23. Comparison of AZM and Kriging
AZM (ZDES, University of Newcastle, UK)
Area-based input data
Area optimisation by certain criteria
Creates comparable data by chosen constraints
Problems - Computing time - Islands - Definition of number of output areas
Kriging (Surfer, ArcGIS…)
Point-based input data
Creates continuous surface by predicting cell values
Isolines of certain values for delineation
Problems - Several interpolation methods available - may give different results - Dependent on coverage of data
October 24, 2003
The Tandem Consortium, M T-L

24. Comparison of grid-based and polygon-based approaches
Grid-based approach
Best with accurate data (total coverage), otherwise estimation needed
Spatial, comparable building blocks
AZM, Kriging and classification applicable
Applicable to different scales and resolutions
Polygon-based data
Small area statistics (smaller than NUTS5) - otherwise spatial analysis not valuable
AZM - when boundaries known (low resolution)
Kriging when boundaries unknown (low to high resolution)
Building blocks could be optimised first by AZM
October 24, 2003
The Tandem Consortium, M T-L

25. Guidelines for dissemination of small area statistics
WP 6 : Dissemination of grid-based data - Descriptions of the different practices in 4 different countries - A case study with Nordic grid data on population by 1 km x 1 km grid cell; problems and possibilities - Discussion on a possible framework of actions for other European countries
Building reference data by 1 km2 grid in the Nordic countries
A survey about a common Nordic grid-based database (dissemination formats, terms of releasing the data)
October 24, 2003
The Tandem Consortium, M T-L

26. Guidelines for dissemination of small area statistics
Harmonising data - Need for reference data - Focus on metadata - Standard data model, storage format, output format - Common grid net
Collaborative agreement - Formal project with proper funds - Focus on harmonised data specifications and common standards - Co-operation with mapping authorities may be fruitful - Third party may be needed to facilitate and optimise sharing, trading and extensive use of multinational data
October 24, 2003
The Tandem Consortium, M T-L

27. Guidelines for dissemination of small area statistics
Quality and disclosure control - Different practice in each country - Essential part of good statistics - Emerging international standards (ISO ) - Requires further studies/research both nationally and internationally - High expectations on INSPIRE
National and/or international guidelines - International guidelines can never compensate national standards - National guidelines should adopt international concepts, definitions, classifications, metadata descriptions….
October 24, 2003
The Tandem Consortium, M T-L

28. Guidelines for dissemination of small area statistics
International - standardised grid net necessary - Common origo - Proper projections for different scales - For joining data from different areas (horizontally) - For joining data from different data sources (vertically) - For data capture, spatial analysis
International guidelines to enable better access to data
October 24, 2003
The Tandem Consortium, M T-L

29. The Tandem Consortium, M T-L
Conclusions
SSAS - Framework of spatial data of NSIs needed
Harmonisation with ESDI and INSPIRE with emphasis on analytical information
Top-down co-ordination - Bottom-up implementation
Restructuring and refinement of current systems
Focus on user needs and case studies
Standards for Data, Features and Methods
Scalability, comparability, delineation, confidentiality, quality, easy-to-use procedures...
October 24, 2003
The Tandem Consortium, M T-L

30. The Tandem Consortium, M T-L
Thanks for listening!
October 24, 2003
The Tandem Consortium, M T-L