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The use of geostatistics for the analysis of Europe’s

regions

Hugo PoelmanEuropean Commission – DG Regional Policy

hugo.poelman@ec.europa.eu

EFGS workshop, Den Haag, 6 October 2009

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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

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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

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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

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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

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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

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Overcome issues related to irregular regions• A few examples:

– 1) Availability and accessibility of passenger flights

– 2) Estimate of potential market size

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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

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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

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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

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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

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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.

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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

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Concentrations of PM10• Results

– Population-weighted regional average concentrations of PM10

– Same approach can also be applied to other territories (e.g. major agglomerations)

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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

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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

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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…

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

  Timeliness Resolution Issues with other data

Accessibility to passenger flights     road network attributes

Remoteness from city centres     city location and definition

Potential market size     regional GDP/head values

Ozone exceedances     raster resolution of remote sensing data

PM10 concentrations     raster resolution of remote sensing data

Proximity of natural areas     CLC resolution and timeliness

Accessibility to universities, hospitals     definition and location of services

Coastal population living at very low elevation      

Population affected by river floods      

Typology of mountain regions / island regions     resolution and selection of islands

Typology of urban and metro regions     UMZ related to CLC data, Urban Audit LUZ compared to NUTS3

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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)

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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

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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?

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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

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References• Green Paper on Territorial Cohesion

http://ec.europa.eu/regional_policy/consultation/terco/paper_terco_annex.pdf

• Regions2020 reporthttp://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)