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

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

Population data 1960 - 2011 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

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,

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

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

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

CORINE Land Cover

Urban Atlas

SPOT / ALOS images

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

Dissemination Georeferenced layers are freely available Data download: http://www.eea.europa.eu/data-and-maps/data/urban-atlas Map viewer: http://www.eea.europa.eu/data-and-maps/figures/urban-atlas

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.

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

Rhein-Ruhr conurbation EU pop

Rhein-Ruhr conurbation EU pop enhanced by GHSL

Paris EU pop

Paris EU pop enhanced by GHSL

PATREVE EU pop

PATREVE EU pop enhanced by GHSL

Brussels EU pop

Brussels EU pop enhanced by GHSL

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

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

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

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