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: roger.milego@uab.catmariajose.ramos@uab.cat

Thank you for your attention !