NTTS 2009 Brussels February 20 1 A Downscaled Population Density Map of the EU from Commune Data and...

NTTS 2009 Brussels February 20 1

A Downscaled Population Density Map of the EU from Commune Data and Land Cover Information

[email protected]


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 downscalingPopulation density downscalingPopulation density downscalingPopulation density downscaling

•Starting data: Population per commune and CORINE Land Cover. •Result: Approximate population density with 1 ha resolution (GIS grid)

+

=


CORINE Land Cover

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


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

mccm WUY

cccm

mm US

XW

cccm

mccm US

XUY


Version 1 of the downscaling

Estimating Uc with an iterative algorithm:

1. Pretend for a moment that population is known only per region (not per commune)

2. Downscale with a provisional set of coefficients

3. Compute the population that would be attributed to each commune X*

m

4. Compare each X*m with the known population Xm and compute a

disagreement index

5. Modify Uc to reduce the disagreement (ask paper for details)

6. 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

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LUCAS: residential points in non-urban CLC2000 classes

CLC2000 class of the LUCAS point# Other artificial# Agriculture# Heterogeneous# Forest# Natural vegetation


% of LUCAS residential points for different CLC2000 classes


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

mii YLCfLUSp ,"Resid."

mmcc

c YbJbappplogit 1log


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…)


EM Algorithm

Iterative algorithm (Expectation – Maximum likelihood): • Assumption: the population Xmc in land cover class c for the commune m

follows a Poisson distribution with parameter Uc 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)


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:

jrefjmjm YY ,,

cell Disaggregated map

Reference map

disagreement of different disaggregated maps with reference data

Austria

Denmark Finland Sweden

Netherlands

Communes (non disaggregated) 8.96 6.08 6.79 12.48 18.3

CLC-iterative 4.55 4.07 5.44 8.05 7.13

CLC-LUCAS simple 4.39 3.97 5.06 8.09 9.03

CLC-LUCAS logit 4.35 3.95 5.03 8.07 7.08

CLC EM 4.50 3.98 5.12 8.08 9.29

CLC limiting variable 4.83 4.02 5.10 7.78 7.95


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)

NTTS 2009 Brussels February 20 1 A Downscaled Population Density Map of the EU from Commune Data and...

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