Integrated GMES Project on Landcover and Vegetation geoland Grid based Spatial Disaggregation of Population Data Klaus Steinnocher – ARC systems research

Integrated GMES Project on Landcover and Vegetation

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Grid based Spatial Disaggregation of Population Data

Klaus Steinnocher – ARC systems research – Geostatistics Kongsvinger – 13.09.2006

Co-funded by the European Commission within the GMES initiative in FP-6

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Background

Integrated project geoland

Observatory Spatial PlanningKlaus Steinnocher

ARC systems research13.09.2006

funded within the 6th framework programme of the EC

represents a network of more than 50 research organisations, public administrations and service providers

aims to provide and establish geo-information products and services to support the European Global Monitoring for Environment and Security (GMES) programme

utilises available Earth Observation resources, and integrates them with existing models into pre-operational end-user applications

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Background

Observatory Spatial Planning


ARC systems research

Introducing innovative EO based services and products into the domain of spatial planning

Building upon a representative user group representing European, national and regional policy requirements

DG Regio, ESPON

Environmental agencies

Regional governments

Serving user needs by integrating EO-derived land cover and use information with existing socio-economic data

Spatial Planning on European level focuses to a very high degree on socio-economic data alone

Land cover and use information can substantially improve statistical data

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Linking people and pixels

Spatial disaggregation



Population density per artificial surface areas

NUTS3 population density Share of artificial surface areas

+ =

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Spatial disaggregation

Population density



Parameters Total population – global parameter on administrative units level

Housing density – local parameter from Earth observation

Spatial Disaggregation Distributes population according to housing densities

Results in local distribution of population

Assumptions Population density is proportional to housing density,

no population occurs outside housing areas, and

relationship between population and housing density is constant within a region, but might differ between regions.

Question Is the assumption valid?

How accurate are the resulting data?

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European case study

Transnational test site



Region

Central Europe

7 countries

Data base

Residential areas from CLC (class 1.1 urban fabric)

Population per NUTS 3 area (Eurostat)

Reference data (Austria)

Population grid (250m) –Statistik Austria

Population per district / municipality (census)

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European case study

CORINE land cover 2000



Artificial surfaces

Agricultural areas

Forest and semi-natural areas

Wetlands

Waterbodies

CORINE land cover level 1

Source:CLC 2000

Wien

Praha

München

Stuttgart

Nürnberg

ChemnitzErfurt

Kassel

MilanoVenezia

Bratislava

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European case study

Proportion of urban fabric 2000 per 3x3km grid cell



no urban fabric

below 2.5

2.5 to 5

5 to 10

10 to 20

20 to 25

25 to 30

30 to 40

40 to 50

50 to 60

60 to 75

75 to 100

Proportion of urban fabric [%]

Source:CLC 2000

13.09.2006

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European case study

Population density 2000 per NUTS 3 area



Source:Eurostat Regio Data Base

below 50

50 to 100

100 to 150

150 to 200

200 to 300

300 to 500

500 to 1.000

1.000 to 2.000

above 2.000

Population density [pop/km2]

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European case study

Population density 2000 on urban fabric per 3x3km grid cell



no population

below 10

10 to 100

100 to 250

250 to 500

500 to 1.000

1.000 to 2.000

2.000 to 5.000

above 5.000

Population density [pop/km2]

Source:CLC 2000Eurostat Regio Data Base

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European case study

Population density 2000 on urban fabric per 3x3km grid cell



Source:CLC 2000Eurostat

no population below 100

100 to 500

500 to 1.000

1.000 to 1.500

1.500 to 2.500

2.500 to 10.000

10.000 to 20.000

above 20.000

Population density [pop/grid cell]

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European case study

Population density 2000 from census per 3x3km grid cell



Source:Statistik Austria

no population below 100

100 to 500

500 to 1.000

1.000 to 1.500

1.500 to 2.500

2.500 to 10.000

10.000 to 20.000

20.000 to 75.000

above 75.000

Population density [pop/grid cell]

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European case study

Comparison of census and disaggregation results



Total cells 9.774

Populated cells: Statistik Austria census grid 7.739 NUTS 3 / CLC 1.1 disaggregation 4.009

Disaggregation covers only 51,8 % of cells, BUT 91,3 % of total population

District level all districts urban districts excluded

average error 21,4% (14.505) 13,8% (8.278)

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European case study

Comparison on district level



Source:CLC 2000Eurostat / Statistik Austria

below -70-70 to -40-40 to -20 -20 to 20 20 to 40 above 40

Relative population difference [%]

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Regional case study

Vorarlberg



Region

State of Vorarlberg

5 districts, 93 municipalities

Data sets

Housing density 1:25.000 – 3 density classes (250m grid)

Residential areas 1:100.000 – 1 class (from CLC)

Population – total for entire region

Validation data

Population density raster (250m) – from Statistik Austria

Population totals for districts / municipalities

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Regional case study

Comparison of housing and population density (250m grid)



0

50

100

150

200

250

300

350

400

0 20 40 60 80 100

housing density (in %)

Po

pu

atio

n d

ensi

ty(p

op

per

gri

dce

ll)

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Satellite image

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Regional case study

Comparison of population densities (250m grid)



Reference data (census)Disaggregation results

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Regional case study

Comparison of population densities (250m grid)



Reference data (census)Disaggregation results Difference to reference

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Regional case study

Comparison of population on district level



0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Bludenz Bregenzer Wald Bregenz Dornbirn Feldkirch

Po

pu

lati

on reference

housing densities

residential

proportional

housing densities residential proportional

relative average error 8,4% (5.904) 12,7% (8.874) 94,7% (66.325)

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Regional case study

Comparison of population on district level



Absolute deviations from reference per municipality

0

1000

2000

3000

4000

5000

6000

7000

municipalities

de

lta

po

pu

lati

on

delta housing density delta residential

housing densities residential proportional

relative average error 12,8% (467) 26,9% (981) 113,3% (4.136)

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Conclusions



Spatial disaggregation of population leads to reliable results

Accuracies

improving with level of detail of land cover data (housing densities)

depending on spatial reference unit (districts, municipalities, etc.)

smaller units lead to lower relative but higher absolute accuracies

urban centers are underestimated, due to missing information on building heights

sparsely populated areas are underestimated, due to minimum mapping unit

For disaggregation of population housing density classes are required

Large cities should be treated separately

13.09.2006

Integrated GMES Project on Landcover and Vegetation

geoland

Thank you for your attention!

Co-funded by the European Commission within the GMES initiative in FP-6

www.gmes-geoland.info

Contact:

ARC systems research GmbH

Klaus Steinnocher

+43–(0)50550–3876

[email protected]

geoland coordinators:

Medias-France

Infoterra GmbH

Documents

Integrated GMES Project on Landcover and Vegetation geoland Grid based Spatial Disaggregation of Population Data Klaus Steinnocher – ARC systems research