EU age/sex stratified population: 100 metre grid: Difference between revisions

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*[http://www.sciencedirect.com/science/article/pii/S0034425706005037 Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sensing of Environment.] Briggs D.J., Gulliver J., Fecht D. and Vienneau D.M. 2007.  108(4):451-466.
*[http://www.sciencedirect.com/science/article/pii/S0034425706005037 Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sensing of Environment.] Briggs D.J., Gulliver J., Fecht D. and Vienneau D.M. 2007.  108(4):451-466.
*[http://link.springer.com/article/10.1007%2Fs11111-010-0108-y A population density grid of the European Union, Population and Environment]. Gallego F.J. 2010. 31: 460-473.
*[http://link.springer.com/article/10.1007%2Fs11111-010-0108-y A population density grid of the European Union, Population and Environment]. Gallego F.J. 2010. 31: 460-473.
==See also==
{{IEHIAS}}

Latest revision as of 19:02, 25 September 2014


The text on this page is taken from an equivalent page of the IEHIAS-project.

Even the highest resolution, LAU 2 level population data may be too coarse for some health risk assessments. Spatial modelling, therefore, may be required to disaggregate these totals to a finer grid. This has been done in several projects using land cover data as a basis for disaggregation (Briggs et al. 2007; Gallego 2010). Here the JRC population density grid (Gallego 2010) was enhanced by incorporating the age categories for both males and females from the census.

The modelling approach is illustrated below. Modelling involved proportioning the LAU2 level age/sex stratified data to the 100m cell on the basis of the JRC grid. Up to 28 grids (14 male and 14 female) were produced for each country depending on the structure of the age groups in the source tables.

Warning: These data are presented here at the 100m to offer flexibility in computing small area estimates at different spatial scales, however should not be taken to reflect population counts at the 100m level. Due to the lack of small area population data such as postcodes across Europe, these data were only validated for GB. Correlations (r) for total population were 0.92, 0.91 and 0.86 at the district, ward and 1km level, respectively. No correlation was found at the output area level. Similar modelling with validation found comparable results (Briggs et al. 2007), indicating that the 100m estimates could be reasonably aggregated to the 1km level.

Source / Reference

Three main sources of data were used to create these population grids:

  • Small area census data
  • Small area administrative boundaries (EuroBoundaryMap 1:1,000,000 January 2006 release)
  • JRC population grid (version 4)

See also

Instructions:

Each country zip file contains up to 28 ascii files. To import into ArcGIS use the ASCII TO RASTER tool, or for ArcInfo use ASCIIGRID with the FLOAT option to create floating point grids.

Data are based on ETRS89 Lambert Azimuthal Equal Area projection with parameters: latitude of origin 52° N, longitude of origin 10° E, false northing 3 210 000.0 m, false easting 4 321 000.0 m.

If a country only has a single zip file (e.g. be.zip or dk_0.zip), you can simply unzip it and use. However, if a country is large, it has several zip files, which are zipped twice. First, you have to unzip all files of a country (e.g. at.zip.001.zip and at.zip.002.zip). This will produce several volumes of a larger zip file (e.g. at.zip which has volumes at.zip.001 and at.zip.002). Merge these volumes and then unzip the country file at.zip to produce the actual ascii files.

We recommend that you use 7zip program for unzipping!

See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages
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Data

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
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