1. New urban analysis By Lewis Dijkstra Deputy Head of Unit
Analysis Unit, DG Regional Policy 1

2. Overview Population 1960-2011 at LAU level Road network 1955-2011
Urban Atlas update and change detection
Building detection

3. Population data
Aim is to collect total population data for each of the 2011 local administrative units level 2 for the census year 1960, 1970, 1980, 1990, 2000, 2011
Requires adjustments when boundaries have changed
Requires estimates when the reference year is different
All will be closely documented
Final delivery September

4. What will this tell us? Speed of (de-) urbanisation per decade
Type of urbanisation: large city vs medium-sized
Suburbanisation: city vs commuting zone
Changes in rural areas, remote areas…
Data can also be aggregated to: NUTS 2 and 3 regions, which allows an analysis by type of NUTS 3 region (urban-rural …), labour market areas,

5. Road network since 1955
Based on a series of maps produced by one company
Maps the changes in the highways, main roads and secondary roads
This network can estimate changes in accessibility for each city over each decade

6. Urban Atlas update (SIRS)
A shared project between ESA, DG ENTR (GMES), EEA and DG REGIO.
Provide high resolution land cover/land use maps based on a common methodology
All cities and commuting zones in the EU and a selection of cities outside
Imagery reference year: 2011 (+/- 1 year)
Detect changes for areas included in the first urban atlas

7. Main features Thematic classes based on CORINE Land Cover nomenclature
But more specific for built-up areas, and less specific outside urban areas
Geometric resolution of 1:10,000
Minimum mapping unit of 0.25 ha in urban areas, 1 ha in other areas

8. CORINE Land Cover

9. Urban Atlas

10. SPOT / ALOS images

11. Production Mix of automatic classification and photo-interpretation
Various data sources used, depending on thematic classes

12. Dissemination Georeferenced layers are freely available Data download:
Map viewer:

13. Building detection (JRC)
Based on very high resolution satellite imagery
Identifies built up areas (i.e. building blocks, city blocks or individual buildings)
Identifies their footprint (i.e. their size)
May also identify height and thus volume
Can identify lower half of the urban hierarchy
Analyse settlement patterns and improve population grids.

14. Definitions Bx = building footprint in the position x of the space X
Sx = settlement footprint derived as generalization of building footprints Bx
the applied generalization function g: Sx includes all the building footprints Bx plus the space enclosed between buildings that is smaller than a given size (scale) k
Skx = gk (Bx)
Ox = open spaces are the difference between Sx and Bx at a given scale k
Okx = Skx – Bx  Okx = gk (Bx) – Bx
Dx = density of buildings is the relative amount of surface occupied by building footprints in a given spatial domain k
Dkx = Σx Bx ∩ k / Σ x k
24 May, 2019

15. Rhein-Ruhr conurbation EU pop

16. Rhein-Ruhr conurbation EU pop enhanced by GHSL

17. Paris EU pop

18. Paris EU pop enhanced by GHSL

19. PATREVE EU pop

20. PATREVE EU pop enhanced by GHSL

21. Brussels EU pop

22. Brussels EU pop enhanced by GHSL

23. Analysis open spaces/urban green from Urban Atlas
24 May, 2019

24. GHSL buildings and green areas (simulated)
24 May, 2019

25. GHSL Classification of green open spaces by size (simulated)
24 May, 2019

26. New technology Will be used to identify smaller settlements
Settlement patterns: compact vs scattered
Building height measures will be tested
Change detection will investigated
Exciting new opportunity