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Lewis Dijkstra, DG Regional Policy


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  • 1. The Urban Atlas Lewis Dijkstra, PhD Deputy Head of the Analysis Unit European Commission – DG Regional Policy [email_address]
  • 2. Urban Atlas project
    • A shared project between ESA, DG ENTR (GMES) and DG REGIO.
    • Providing high resolution land cover/land use maps based on a common methodology
    • Covering 305 major European agglomerations, based on Urban Audit definition of Larger Urban Zones
    • Imagery reference year: 2006 (+/- 1 year)
    • Project duration: 2009-2011
  • 3. 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
  • 4. CORINE Land Cover
  • 5. Urban Atlas
  • 6. SPOT / ALOS images
  • 7. Production
    • Mix of automatic classification and photo-interpretation
    • Various data sources used, depending on thematic classes
  • 8. Thematic classes
    • Residential areas: use of soil sealing layer to distinguish between density classes
  • 9. Thematic classes
    • Non-residential urban areas
    • Detailed transport network layer (COTS)
    • Local maps as auxiliary source of information
  • 10. Thematic classes
    • Other classes
    • Less thematic detail for agricultural, natural and forest areas (and MMU 1 ha)
  • 11. Validation process
    • Internal validation by the contractor
    • Qualitative verification supported by EEA and EIONET – ETC/LUSI
      • Format and metadata
      • Mapping specifications
      • Topology
    • Quantitative validation on a subset of cities by independent experts
  • 12. Dissemination
    • Georeferenced layers are freely available
    • Data download:
    • Map viewer:
  • 13. Derived indicators
    • Can be exploited at the level of Urban Audit units:
      • Larger Urban Zone (LUZ), kernel, city, sub-city districts (SCD)
    • Land cover/use surface breakdown
    • Green urban areas per inhabitant
    • A sprawl analysis (ESPON LUPA project)
  • 14. Compactness of cities
    • Share of selected land cover classes in total land surface of concentric circles defined around the city centroid
      • Residential areas (continuous / discontinuous)
      • Industrial, commercial, public, etc. areas
      • Green urban areas
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  • 19. Modelling
    • Input data for modelling of urban vulnerabilities to natural hazards (JRC)
  • 20. Modelling and indicator applications
    • More EU-wide (planned) use cases:
      • Urban morphology and urban form
      • Land use modelling: drivers for/from urban areas
      • Spatial allocation or distribution of statistical variables
      • Climate change and urban areas
      • Distribution and availability of green urban areas and urban green “background”
  • 21. Population distribution and green urban areas
    • Ongoing analysis on proximity and accessibility of green urban areas
    • Combination of Urban Atlas data, population distribution and urban street network
  • 22. Proximity of green urban areas
    • Creation of accessibility areas around urban atlas polygons, based on 15 minutes of walking distance
    • Determine the surface of the green areas which can be reached within the walking distance
    • Calculate the accessible green surface per inhabitant, at the level of the urban atlas polygon
    • Possible aggregation at city level (population weighted average accessible surface)
  • 23. Copenhagen Height = population density
  • 24. The Hague Height = population density
  • 25. Helsinki Height = population density
  • 26. The future of the Urban Atlas
    • Repeat Urban Atlas in order to create a detailed dynamic view on urban land cover/use
    • Coherence with other data collection projects is important
      • Census 2011
      • Urban Audit
    • Consequences
      • Define imagery acquisition requirements (2011 +/- year)
      • Update methodology, including change detection
      • Enhance the geographical scope (possible extension of main agglomerations)