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NOTES 1. PLACE, DATE AND EVENT NAME 1.1. Access the slide-set place, date and event name text box beneath the JRC logo from the Slide Master. 1.2. Do not change the size nor the position of that text box. 1.3. Replace the mock-up texts for the place (“Place”), the date (“dd Month YYYY”) and the event name (“Event Name”) with your own texts. 1.4. Set it in MetaPlus Book Roman, if you own the typeface. Otherwise, keep the original typeface – Arial. 1.5. Keep the original flush-left justification. 1.6. Keep the original font colour (white). 1.7. Keep the original font body size (7 pt) and the text on one single line. 2. SLIDE NUMBER 2.1. The slide number on the banner’s lower right-hand side is automatically generated. 3. SLIDES 3.1. Duplicate the first slide as needed. 3.2. Do not change the size nor the position of the slide’s text box. 3.3. Try not to place more text on each slide than will fit in the given text box. 3.4. Replace the mock-up heading text (“Joint Research Centre (JRC)”) with your own text heading. 3.5. Set it in Eurostile Bold Extended Two or in Helvetica Rounded Bold Condensed, if you own one of these typefaces. Otherwise, keep the original typeface – Arial. 3.6. Keep the original flush-left justification. 3.7. Keep the original font colour (100c 80m 0y 0k). 3.8. Keep the original font body size (28 pt) and the heading on one single line whenever possible. Reduce the font body size if needed. 3.9. Replace the mock-up text (“The European Commission’s Research-Based Policy Support Organisation)”) with your own text. 3.10. Set it in MetaPlus Book Roman, if you own the typeface. Otherwise, keep the original typeface – Arial. 3.11. Keep the original flush-left justification. 3.12. Keep the original font colour (100c 80m 0y 0k). Use black if you need a second colour. 3.13. Keep the original font body size (22 pt) or reduce it if unavoidable. 3.14. Replace the EU-27 map mock-up illustration with your own illustration(s). 3.13. Try to keep your illustration(s) right- and top- or bottom-aligned with the main text box whenever possible. A Downscaled Population Density Map of the EU from Commune Data and Land Cover Information Javier.firstname.lastname@example.org
In most countries, population data are available for public use only per administrative unit (commune) In many cases this may be insufficient for geographical analysis. It depends on the size of the geographic units and the scale of the event under assessment. Population hit by a flood Population in the 65 decibel contour of airports Population at a distance > 2 km of the closest primary school. In some countries, population data exist for 1 km grids Bottom-up approach (much better..) Rationale
Population density downscaling • Starting data: Population per commune and CORINE Land Cover. • Result: Approximate population density with 1 ha resolution (GIS grid) + =
Land cover map from photo-interpreted Landsat-TM images 44 classes Urban dense Urban discontinuous …… Minimum mapping unit: 25 ha. Smaller patches swallowed by dominant class heterogeneous classes if no one is dominant (~10% of the total area) For this exercise, simplified nomenclature of 9 classes CORINE Land Cover
A simple model for downscaling • Xm : population in commune m • Scm: area of land cover type c in commune m. • Ycm : density of population for land cover type c in commune m. Inside each commune Ycm is assumed to be proportional to given coefficients Uc for each land cover type: • If we know Uc , Wm are computed to respect the total population of the commune • Problem: estimating reasonable coefficients Uc
Version 1 of the downscaling Estimating Uc with an iterative algorithm: • Pretend for a moment that population is known only per region (not per commune) • Downscale with a provisional set of coefficients • Compute the population that would be attributed to each commune X*m • Compare eachX*m with the known population Xm and compute a disagreement index • Modify Uc to reduce the disagreement (ask paper for details) • Turn to step 2 or stop if modification very small
LUCAS 2001/2003 (Land Use/Cover Area-frame Survey) Managed by Eurostat (Common specifications for EU15 ) nomenclature Land cover (57 classes) * Land use (14 classes) The land Use “residential” gives information useful to assess the density of buildings in CLC non-urban classes. The residential area is used as proxy of the population density in CLC non-urban areas. Introducing LUCAS data
Coefficients suggested by the % of residential areaVersion 3 of the disaggregated grid
Application of logit regression • Assumption: the probability that a random point has residential land use depends on the CLC class and on the average population density of the commune • The logit model assumes more specifically: • Where Jc is an 0-1 indicator of the CLC class c
Residuals of the logit regression (2001) The residuals of the logit regression can be used for the geographical tuning of the coefficients (not yet done…)
Iterative algorithm (Expectation – Maximum likelihood): Assumption: the population Xmc in land cover class c for the commune m follows a Poisson distribution with parameterUc x Scm M step computes a maximum lilelihood estimator of Uc E step makes an adjustment to ensure that the population attributed to the commune’s territory equals Xm(known) EM Algorithm
Validation in 5 countries • A reliable reference grid available for 5 countries with 1 km2 cells • To be extended to other countries • Disagreement index for map m: Reference map Disaggregated map cell disagreement of different disaggregated maps with reference data
Some conclusions • Disaggregated population density maps with the help of CORINE Land Cover reduces the disagreement with a reference map • Improvement between 20% and 60% • But still far from perfect • The logit model seems to give the best results among the approaches tested, but the differences are very small (except for NL) • In communes that contain large urban and non-urban areas, all the disaggregated maps tested seem to over-estimate the density in non-urban areas.
Further developments • Reference maps available might be used also for calibrating models, not only for validation • Downscaling the reference maps to 1 ha resolution? • New layers of geographic data should be tried, e.g.: • night time light • Tele-Atlas • Adding Switzerland and Norway • Producing a first version with 2006 data • Still some countries missing for population data • CLC2006 not yet distributed • Layer of population density changes • Introducing more detailed data for urban areas (Urban Atlas)