1. Institute for Protection and Security of the Citizen
Disaggregated Population Density to CLC, update ETC-TE, Barcelona, July 12, 2006
Javier Gallego

2. Population density downscaling
Starting data: Population per commune and CORINE Land Cover.
Result: Approximate population density with 1 ha resolution (GIS grid) + =

3. A simple model for downscaling
Xm : population in commune m
Scm : area of land cover type c in commune m.
Ycm : density of population for land cover type c in commune m. Inside each commune Ycm is assumed to be proportional to given coefficients Uc for each land cover type:

4. Using LUCAS 2001

5. Using LUCAS 2001
LUCAS data allow to redo several steps on a more objective basis:
Assessing grouping criteria of CLC classes. For example there LUCAS data suggest that there is no significant difference between arable land, permanent crops and pastures.
Geographical tuning of coefficients: the density of scattered buildings in non-urban areas is not homogeneous in the EU and does not only depend on global population density.
The residential area is used as proxy of the population density in CLC non-urban areas.

6. % of LUCAS residential points for different CLC2000 classes

7. Using LUCAS data for population density downscaling
coefficients for the 2001 disaggregation suggested by the overlay LUCAS-CLC2000

8. Application of logit regression
Assumption: the probability that a random point has residential land use depends on the CLC class and on the average population density of the commune
The logit model assumes more specifically:
Where Jc is an 0-1 indicator of the CLC class c

9. Residuals of the logit regression (2001)
The residuals of the logit regression can be used for the geographical tuning of the coefficients

10. For the next future (hopefully)
Using the photo-interpretation of 1,000,000 points (grid of 2 km in EU 25) ongoing for the stratification of LUCAS 2006.
Exploiting Tele-Atlas.
Smoothing steps between communes (but much less between land cover types).

11. Update of 1991 disaggregation.
Assumption: The 2001 coefficients derived with the help of LUCAS are approximately valid for the 1991 disaggregation.
Problems with the commune codes
First version of disaggregation run in LAEA coordinates.

12. 1991 disaggregation: Commune coefficients.
High (low) coefficients: high (low) density for a given CLC class

13. 1991 disaggregation in LAEA 52-10 co-ordinates.
UK missing:
Corrections needed in the version of the commune layer

14. 2001-1991 straight difference of layers.
UK missing:
Corrections eeded in the version of the commune layer

15. 2001-1991 straight difference of layers.
UK missing:
Corrections needed in the version of the commune layer

16. 2001-1991 straight difference of layers: Barcelona
Some phenomena caught (moving from city to peripherical areas), but fine scale mapping wrong (different behaviours inside the city)

17. 2001-1991 straight difference of layers: Roma
Probably wrong because of the large size of the commune of Rome.

18. 2001-1991 straight difference : Ruhr Gebiet
The agglomeration behaves like a single city.

19. 2001-1991 straight difference of layers: Brussels

20. An possible way of defining urban/rural areas (1)
Example of definition: An urban area is a patch with a population > 50,000 inh. such that a 1km-diameter circle around any point in it the population density is > 500 inh/km2
Intermediate step: urban/rural areas are defined on the basis of geographical structure, regardless of administrative limits
In a later step, they can be defined as a set of communes.
Communes can be classified depending on their link with urban agglomerations, e.g:
Purely urban
Containing an urban agglomeration and significant rural zones
Mainly peri-urban
Etc.

21. Defining urban areas with GIS operations

22. Defining urban areas with GIS operations(6)

23. Urban areas (>50000 inhab) in densely populated regions

24. Example of scheme for a typology of communes
Urban and semi-urban
Fully urban communes: > 99% in an urban agglomeration (>50,000 inhab.?)
Mainly urban communes with moderate rural area: 50-99% in an urban nucleus.
Urban communes with a large rural area: Dominant commune of an urban nucleus (Medium-small urban centres of rural areas). A commune can be considered dominant in the nucleus if it has > 50% of the population of the nucleus,
Suburban: intersects an urban nucleus and is not in the previous categories.

25. Possible subdivisions
Subdivisions based on:
size of nucleus
land cover profile (predominantly arable, forest, grass…),
by topographic roughness (mountain, hill, plain)
Quality of soil, specific climatic handicaps, etc.