Population and Environment

, Volume 31, Issue 6, pp 460–473 | Cite as

A population density grid of the European Union

  • Francisco Javier GallegoEmail author
Brief Report


This paper describes four methods used to produce dasymetric population density grids combining population data per commune with CORINE Land Cover, a map available for all countries of the European Union. An accuracy assessment has been carried out for five countries for which a very reliable 1-km population density grid exists; the improvement, compared with the choropleth map per commune, ranges between 20% for the weakest result in Finland and 62% for the best result in the Netherlands. The best results are obtained with a method using logit regression to integrate information from the point survey LUCAS (Land Use/Cover Area frame Survey); however, performance differences between methods are moderate. The dasymetric grid is distributed free of charge by the European Environment Agency, for non-commercial use.


Population density Dasymetric mapping Downscaling CORINE Land Cover (CLC) Land Use/Cover Area frame Survey (LUCAS) 



Eurostat provided most of the data for the study. We are particularly grateful to César de Diego, Daniele Rizzi, Albrecht Wirthmann, Daniel Rase, Torbiorn Carlquist and Edwin Schaaf. The European forum for Geostatistics provided reference data for validation thanks to Ingrid Kaminger, Rina Tammisto, Niek van Leeuwen, Erik Sommer and Lars Backer. Roger Milego, Oscar Gómez, Stefan Kleeshulte and Tomas Soukup, from the European Topic Centre of Terrestrial Environment (ETC/TE) made useful suggestion. Mette Lund managed the distribution of the results through the EEA dataservice. Brooke Tapsall and four anonymous reviewers made useful suggestions to improve the paper.


  1. Annoni, A., Luzet, C., Gubler, E., & Ihde, J. (2001). Map projections for Europe. Report EUR 20120 EN, JRC-Ispra (Italy), 131 pp.Google Scholar
  2. Barbosa, P., Camia, A., Kucera, J., Liberta, G., Palumbo, I., & San-Miguel-Ayanz, J. (2008). Assessment of forest fire impacts and emissions in the European Union based on the European Forest Fire Information System. Dev Environ Sci, 8, 197–208.CrossRefGoogle Scholar
  3. Bettio, M., Delincé, J., Bruyas, P., Croi, W., & Eiden, G. (2002). Area frame surveys: Aim, principals and operational surveys. Building Agri-environmental indicators, focussing on the European Area frame Survey LUCAS. EC report EUR 20521, pp. 12–27.
  4. Bhaduri, B., Bright, E., Coleman, P. H., & Dobson, J. (2002). LandScan: Locating people is what matters. Geoinformatics, 5(2), 34–37.
  5. Bierkens, M. F. P., Finke, P. A., & de Willigen, P. (2000). Upscaling and downscaling methods for environmental research (p. 190). Dordrecht: Kluwer.Google Scholar
  6. Bracken, I., & Martin, D. (1989). The generation of spatial population distributions from census centroid data. Environment & Planning A, 21(4), 537–543.CrossRefGoogle Scholar
  7. Bracken, I., & Martin, D. (1995). Linkage of the 1981 and 1991 UK censuses using surface modelling concepts. Environment & Planning A, 27(3), 379–390.CrossRefGoogle Scholar
  8. Briggs, D. J., Gulliver, J., Fecht, D., & Vienneau, D. M. (2007). Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote sensing of Environment, 108, 451–466.CrossRefGoogle Scholar
  9. Center for International Earth Science Information Network (CIESIN). (2005). Gridded Population of the World (GPW), Version 3. CIESIN, Columbia University, Palisades, NY.
  10. Chen, K., McAneney, J., Blong, R., Leigh, R., Hunter, L., & Magill, C. (2004). Defining area at risk and its effect in catastrophe loss estimation: A dasymetric mapping approach. Applied Geography, 24, 97–117.CrossRefGoogle Scholar
  11. Delincé, J. (2001). A European approach to area frame survey. Proceedings of the Conference on Agricultural and Environmental Statistical Applications in Rome (CAESAR), June 5–7, vol. 2 pp. 463–472.
  12. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B, 39, 1–38.Google Scholar
  13. Dijkstra, L., & Poelman, H. (2008). Remote rural regions: How proximity to a city influences the performance of rural regions, Regional Focus n.1 (2008) EC-DG REGIO.Google Scholar
  14. Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., & Worley, B. A. (2000). LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 66(7), 849–857.Google Scholar
  15. EEA-ETC/TE. (2002). Corine land cover update, Technical guidelines,
  16. Eicher, C., & Brewer, C. (2001). Dasymetric mapping and areal interpolation: Implementation and evaluation. Cartography and Geographic Information Science, 28, 125–138.CrossRefGoogle Scholar
  17. Flowerdew, R., Green, M., & Kehris, E. (1991). Using areal interpolation methods in GIS; Papers in regional science, 70(3), 303–315.Google Scholar
  18. Gallego, J., & Peedell, S., (2001). Using CORINE Land Cover to map population density. Towards Agri-environmental indicators, Topic report 6/2001 European Environment Agency, Copenhagen, pp. 92–103.
  19. Langford, M. (2007). Rapid facilitation of dasymetric-based population interpolation by means of raster pixel maps. Computers, Environment and Urban Systems, 31(1), 19–32.CrossRefGoogle Scholar
  20. Langford, M., Maguire, D. J., & Unwin, D. J. (1991). The area transform problem: Estimating population using remote sensing in a GIS framework. In I. Masser & M. Blakemore (Eds.), Handling geographical information: Methodology and potential applications (pp. 55–77). London, UK: Longman.Google Scholar
  21. Langford, M., & Unwin, D. J. (1994). Generating and mapping population density surfaces within a geographical information system. Cartographic Journal, 31(1), 21–26.Google Scholar
  22. Martin, D. (1996). An assessment of surface and zonal models of population. International Journal of Geographical Information Systems, 10(8), 973–989.CrossRefGoogle Scholar
  23. Martin, D. (1998). Optimizing census geography: The separation of collection and output geographies. International Journal of Geographical Information Science, 12(7), 673–685.CrossRefGoogle Scholar
  24. Martin, D., Tate, N. J., & Langford, M. (2000). Refining population surface models: Experiments with Northern Ireland Census data. Transactions in GIS, 4(4), 343–360.CrossRefGoogle Scholar
  25. Mennis, J. (2003). Generating surface models of population using dasymetric mapping. Professional Geographer, 55(1), 31–42.Google Scholar
  26. Mrozinski, R., & Cromley, R. (1999). Singly—and doubly—constrained methods of areal interpolation for vector-based GIS. Transactions in GIS, 3, 285–301.CrossRefGoogle Scholar
  27. Reibel, M., & Bufalino, M. (2005). Street-weighted interpolation techniques for demographic count estimation in incompatible zone systems, Environment and Planning A, 37(1), 127–139.Google Scholar
  28. Rosati, L., Marignani, M., & Blasi, C. (2008). A gap analysis comparing Natura 2000 vs National Protected Area network with potential natural vegetation. Community Ecology, 9(2), 147–154.CrossRefGoogle Scholar
  29. Thieken, A., Müller, M., Kleist, L., Seifert, I., Borst, D., & Werner, U. (2006). Regionalisation of asset values for risk analyses. Natural Hazards and Earth System Sciences, 6, 167–178.CrossRefGoogle Scholar
  30. Tobler, W. R. (1979). Smooth pycnophylatic interpolation for geographical regions. Journal of the American Statistical Association, 74, 519–530.CrossRefGoogle Scholar
  31. Tralli, D. M., Blom, R. G., Zlotnicki, V., Donnellan, A., & Evans, D. L. (2005). Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing, 59(4), 185–198.CrossRefGoogle Scholar
  32. Vinkx, K., & Visée, T. (2008). Usefulness of population files for estimation of noise hindrance effects. ICAO Committee on Aviation Environmental Protection. CAEP/8 Modelling and Database Task Force (MODTF). 4th Meeting. Sunnyvale, USA, pp 20–22, February 2008.Google Scholar
  33. Weber, N., & Christophersen, T. (2002). The influence of non-governmental organisations on the creation of Natura 2000 during the European Policy process. Forest Policy and Economics, 4(1), 1–12.CrossRefGoogle Scholar
  34. Wu, CH., & Murray, A. T. (2005). A cokriging method for estimating population density in urban areas, Computers, Environment and Urban Systems, 29(5), 558–579.Google Scholar
  35. Xie, Y. (1995). The overlaid network algorithms for areal interpolation problem. Computers, Environment and Urban Systems, 19(4), 287–306.CrossRefGoogle Scholar
  36. Yuan, Y., Smith, R. M., & Limp, W. F. (1997). Remodeling census population with spatial information from Landsat TM imagery. Computers, Environment and Urban Systems, 21(3–4), 245–258.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.IPSC, JRCIspraItaly

Personalised recommendations