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The use of geostatistics for the analysis of Europe’s regions

The use of geostatistics for the analysis of Europe’s regions. Hugo Poelman European Commission – DG Regional Policy hugo.poelman@ec.europa.eu EFGS workshop, Den Haag, 6 October 2009. The geographical framework. Policy-supporting analysis of the European regions

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The use of geostatistics for the analysis of Europe’s regions

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  1. The use of geostatistics for the analysis of Europe’s regions Hugo Poelman European Commission – DG Regional Policy hugo.poelman@ec.europa.eu EFGS workshop, Den Haag, 6 October 2009

  2. The geographical framework • Policy-supporting analysis of the European regions • NUTS regions: the backbone of the analytical framework • Role in implementing cohesion policy interventions • Legal background (NUTS Regulation) • NUTS2 and NUTS3 levels allowing for different levels of detail • Statistical system providing extensive datasets

  3. Issues with NUTS regions • Spatially heterogeneous • Changes over time • Most regions are more administrative than functional • Thematic data availability varies • Regional breakdown is more appropriate for some themes, less for others • Border effects between countries and regions

  4. Extended thematic needs • Challenges with regional / territorial dimension, e.g. • Climate change effects • Infrastructure and (public) services • Accessibility and connectivity • Environment and quality of life • Energy • Statistical systems often have difficulties to tackle the regional dimension of these areas

  5. Use cases of geostatistics • Exploration of the use of geostatistics • Helping to overcome some of the issues related to (administrative) regions • Allowing for better analysis of various georeferenced data, e.g. • Remote sensing • Network related data • Improvement of the development of regional typologies

  6. Key data source in geostatistics • Many analysis cases rely upon the use of a regular and sufficiently detailed population distribution • 1 km² registered population data for available countries • Population disaggregation layer (JRC – Javier Gallego) for the other countries, aggregated at 1 km² cell size

  7. Overcome issues related to irregular regions • A few examples: • 1) Availability and accessibility of passenger flights • 2) Estimate of potential market size

  8. 1) Accessibility to passenger flights • Various statistical data can be (almost directly) exploited at regional level • Departures per airport • Air traffic data at NUTS2 level • Results are hardly meaningful when only considering the region where the airport is located • Accessibility analysis tries to overcome the border effects of the regions

  9. 1) Accessibility to passenger flights • Basic data • Airport locations + data on departing passenger flights • Road transport network • Population distribution • Results • Areas where a certain number of flights is easily accessible • Population-weighted regional average figures on available and accessible flights

  10. 2) Potential market size • Estimate the amount of GDP or population available within a pre-defined neighbourhood • Within the neighbourhood, distance is taken into account • Analysis is carried out using rasters with 1 km² and 10 x 10 km cells • NUTS2 regions used as reporting units

  11. 2) Potential market size • Basic data • Regional GDP/head figures in PPS • Population distribution grid • NUTS polygons and derived NUTS grid • Results • Focal sum of GDP (or population) • Regional averages of the obtained focal sums • Results expressed as index of EU average for easier interpretation

  12. Enhanced analysis of various georeferenced sources • Geostatistics help in establishing links between data from remote sensing and regional or territorial reporting units • Especially relevant in the area of the environment, climate change effects, hazards, etc.

  13. Concentrations of PM10 • Basic data • Grid layers from GMES PROMOTE project with yearly summary data on concentrations of PM10 (similar data also for ozone) • Population distribution

  14. Concentrations of PM10 • Results • Population-weighted regional average concentrations of PM10 • Same approach can also be applied to other territories (e.g. major agglomerations)

  15. Development of typologies • Regional and territorial typologies often need to refer to data / variables which make sense at lower levels than the level at which the typology is to be applied • Choice between regular grids and local administrative units ?? • Regular grids often have an advantage when using data on geophysical or natural phenomena and help to overcome the MAUP

  16. Typologies using a regular population distribution • Mountain regions, based a comparison of the population grid and a geophysical definition of mountain areas • Typology of rural areas according to their remoteness • Evaluation of the accessibility of city centres via the road network • Share of regional population living at more than 45 minutes from city centres

  17. Findings on use cases • Use of geostatistics for regional policy started in rather experimental way • But resulted in various indicator sets, used in further analysis and reports • Green paper on territorial cohesion • Regions2020 report • Cohesion reports • More use and more “visibility” of the results also means more requirements…

  18. Overview of current use cases of a population grid with 1 km² resolution

  19. Further developments (1) • Topics related to a common European definition of rasters • Rules and methods for conversion of national grids into European ones • Issue of cell size for European-wide analysis of regions and territories • Trade-off between precision, analysis effort and confidentiality issues • De-facto standard used at EU level (JRC / EEA)

  20. Further developments (2) • Timeliness and frequency of essential geostatistical datasets • Weakness of current analysis: 2000-2001 time stamp of major population grid • New thematic datasets with emerging time series require the use of corresponding population distribution • Timely land cover datasets • Disaggregation may help to overcome incompleteness of data

  21. Further developments (3) • Enlarge the thematic scope • Various analyses would benefit from the use of other raster datasets than merely total population • Key demographic breakdowns (age classes) • Employment (day/night time population) • Location of services? • Issues of harmonisation and definition • Confidentiality?

  22. Outlook • Continued and enhanced use of currently available geostatistical sources • Help awareness raising by demonstrating and documenting use cases • Promote (via Eurostat) further development of European-wide geostatistical datasets and methodologies

  23. References • Green Paper on Territorial Cohesion http://ec.europa.eu/regional_policy/consultation/terco/paper_terco_annex.pdf • Regions2020 report http://ec.europa.eu/regional_policy/sources/docoffic/working/regions2020/index_en.htm • Regional Focus • 01/2008: Remote rural regions • 01/2009: Metropolitan regions in the EU http://ec.europa.eu/regional_policy/sources/docgener/studies/study_en.htm • 5th Cohesion Report (due in Autumn 2010)

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