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ESPON 2013 DATABASE Malmö Seminar, 2-3 December 2009. Combining data by NUTS With continuous data. Maria José Ramos Roger Milego Agràs. Structure of the presentation. Methodology overview Disaggregation options for discrete data Automatic tools Results Conclusions.
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ESPON 2013 DATABASEMalmö Seminar, 2-3 December 2009 Combining data by NUTS With continuous data Maria José Ramos Roger Milego Agràs
Structure of the presentation • Methodology overview • Disaggregation options for discrete data • Automatic tools • Results • Conclusions
Weighted by Population Unemployment 2001 (Eurostat) Disaggregation Population Grid 2001 (JRC) Integration Ref. Grid 1km Integration Aggregation CLC 2000 (EEA) OLAP CUBE Online Query Dimensions Measures Nuts3 Code Corine Class Level 1 Ha Unemployment Total AT111 Artificial surfaces 3795 697 Multiple Applications Maps Graphics & Statistics Methodology overview
2 15% Cell value = ∑ (Vi * Sharei) Vi = Value of unit i Sharei = Share of unit i within the cell V1 * 0.85 + V2 * 0.15 1 85% Cell value = Wc ∑ (Vi * Sharei) Vi = Value of unit i Sharei = Share of unit i within the cell Wc = weight assigned to cell c 2 15% Wc Wc(V1 * 0.85 + V2 * 0.15) 1 85% Disaggregation options 1. Maximum area criteria: 2. Proportional calculation 3. Proportional and weighted calculation
Grid shp Indicator shp 1 1 1 INPUT 1 INPUT & INTERMEDIATE 2 1 1 2 3 OUTPUT 3 Automatic tools Example: Proportional and weighted calculation
Results (1) 1. Maximum area criteria 2. Proportional calculation 3. Proportional & weighted Unemployment rate total in % 2001. Data source: Employment and Labour Market NUTS 3 (v. 1999) (Eurostat) GDP in € 2002 weighted by Population 2001 Data sources: Wealth and Production NUTS 3 (v.2003) (Eurostat), Population density 2001 (JRC) Urban Dominance. Data Source: Urban Morphological Zones 2000 (EEA)
Nuts Hierarchial Corine Land Cover 2000 • Data: • GDP total in Euros • Urban Presence Results (2)
Share of forest and semi-natural areas 2000 in the NUTS 2/3 region Results (3) • In parallel, we have obtained the statistics of CLC2000 by NUTS3(2006), by classical overlay between both layers. • This allows to calculate many indicators. Example:
Conclusions Disaggregating socioeconomic data by a regular grid is the best solution in order to downscale such information reported by administrative areas. The 1km European Reference Grid is a good option to undertake the disaggregation because: It has an European coverage It follows Inspire specifications It is used for several institutions as the reference grid Its resolution is optimal in order not to lose data precision. The suitable method (maximum area criteria or proportional calculation) depends on the type of variable (uncountable or countable) Whenever it is possible, it is better to weight the final figures using a proportional method, e.g. by population (added value). This methodology allows the integration of socio-economic data into an OLAP cube, which facilitates the comparison and analysis of such data together with land cover data, for example.
Thank you very much for your attention! For further info: roger.milego@uab.cat mariajose.ramos@uab.cat