ESPON 2013 DATABASE Malmö Seminar, 2-3 December 2009
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Transcript of ESPON 2013 DATABASE Malmö Seminar, 2-3 December 2009
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
Ref. Grid 1km
Unemployment2001 (Eurostat)
Disaggregation
Population Grid2001 (JRC)
Weighted by
Population
OLAP CUBE
Integration
IntegrationCLC 2000 (EEA) Aggregation
Dimensions
Online Query
6973795Artificial surfacesAT111
Unemployment TotalHaCorine Class Level 1Nuts3 Code
Measures
Multiple
Applications
Maps Graphics & Statistics
Methodology overview
Disaggregation options
1. Maximum area criteria:
2. Proportional calculation
3. Proportional and weighted calculation215%
185%
Cell value = ∑ (Vi * Sharei)
Vi = Value of unit i
Sharei = Share of unit i within the cell
V1 * 0.85 + V2 * 0.15
Cell value = Wc ∑ (Vi * Sharei)
Vi = Value of unit i
Sharei = Share of unit i within the cell
Wc = weight assigned to cell c
215%
185%
Wc Wc(V1 * 0.85 + V2 * 0.15)
Automatic toolsExample: Proportional and weighted calculation
3
1
1
2
1
Grid shp Indicator shp
INPUT
INPUT & INTERMEDIATE
OUTPUT
1 1
21
3
Results (1)
1. Maximum area criteria2. Proportional calculation3. Proportional & weighted
Urban Dominance.Data Source: Urban Morphological Zones 2000 (EEA)
Unemployment rate total in % 2001.Data source: Employment and Labour Market NUTS 3 (v. 1999) (Eurostat)
GDP in € 2002 weighted by Population 2001Data sources: Wealth and Production NUTS 3 (v.2003) (Eurostat), Population density 2001 (JRC)
Results (2)
Nuts Hierarchial
Data:
•GDP total in Euros
•Urban Presence
Corine Land Cover 2000
• 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:
Share of forest and semi-natural areas 2000 in the NUTS 2/3 region
Results (3)
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: [email protected]@uab.cat
Thank you for your attention !