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

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 2002 - August 2003 October 24, 2003 The Tandem Consortium, M T-L

The Tandem Consortium, M T-L The first report: http://europa.eu.int/comm/eurostat/Public/ 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

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

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

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

= 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

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

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

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

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

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

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

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

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

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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

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

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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

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

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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

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

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

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

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 11913-11915) - 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

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

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

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