1. Geographical Information Systems for Statistics 15-16 Mar 2007
Comparison of population density grids
Albrecht Wirthmann
Michaela Kotecka
European Commission – Eurostat
Geographical Information Systems for Statistics, Luxembourg, Mar 2007

2. Introduction
The analysis of spatial phenomena requires spatial data of adequate and homogeneous resolution
Administrative boundaries and NUTS are of heterogeneous size concerning surface area
Grids provide a good basis for spatial analysis due to their equal size and possibility of spatial aggregation
Population is key statistics for many studying spatial and statistical phenomena
Available datasets
European disaggregated population grid dataset
National population grid dataset of grid initiative

3. Objective of study Comparison of population grids
European disaggregated population grid
National population grids for selected countries
to learn about differences
to learn about limitations of European dataset
limitations related to usage of European dataset
aim of optimising method for the disaggregation

4. Input datasets European disaggregated population map
Joint initiative of Eurostat and the JRC
Total population by commune
CORINE and cover grid as co-variable
Disaggregation of population based on pre-defined population density figures by land cover class
Initial model aggregated 44 land cover classes to 17 population classes
Recent dataset is based on an aggregation of 9 population classes
Recent optimisation using LUCAS data
Correction factor for adjusting the disaggregated total population by commune to statistical figures

5. Input datasets European disaggregated population map
Regrouping of land cover classes to population classes
Optimisation based on comparison with LUCAS points

6. Input datasets European disaggregated population map
Initial set of population density coefficients

7. Input datasets European disaggregated population map Latest version
CORINE land cover 2000
Population 2001
Resolution 100 m
CRS: ETRS-LAEA

8. Input datasets National population grids
Compilation of population grids by Statistics Finland and the grid club initiative
Population figures by grid cell based on geocoded address and buildings registers
Quality of registers determines quality of grid information

9. Input datasets
National population grids
Datasets from 5 countries

10. Applied methods Comparison of population distribution by grid cell
Calculation of population density by land cover population class
Comparison of average density

11. Data preparation European disaggregated grid National grids
Projection to UTM 32N / 33N
Overlay with country and land mask
Aggregation to 1km resolution
National grids
Creation of grid (NL)
Overlay with country and land masks to fill assign empty grid cells with zero value
Land cover grid
Reclassification from 44 classes to 9 population classes
Projection to UTM32N / 33N

12. Issues
Resampling of population figures after projection
Aggregation of land cover classes to 1km
Results for the Netherlands
Seems to be ok

13. Limited validity of comparison results at 1km resolution
Issues
Confusion matrix for the Netherlands
Overall correspondence: 80.2%
Limited validity of comparison results at 1km resolution

14. Visual comparison Population density in NL 100m resolution
National grid European grid

15. Results Classification of population density into 7 classes
Comparison of total population by class

16. Results Classification of population density into 7 classes
Comparison of total population by class

17. Results Classification of population density into 7 classes
Comparison of total population by class

18. Results Comparison by density classes, European dataset
underrepresents grid cells of low density classes (< 20 inh./km²)
overrepresents grid cells of medium density classes
underrepresents grid cells of high density classes (> 500 inh./km²)
Seems to be a relation between distortion of distribution with average population density of countries
Disaggregation pattern highly influenced by land cover patterns as they are mapped by CORINE land cover

19. Comparison by land cover class
Bias due to aggregation of land cover to 1 km resolution!

20. Comparison by land cover class
Bias due to aggregation of land cover to 1 km resolution

21. Comparison by land cover class

22. Comparison by land cover class
Results by country

23. Conclusions
Results concerning land cover of limited validity due to aggregation and concerning absolute values
Distribution determined by characteristics of land cover map
Tendency is obvious
European population grid tends to average population distribution
Density in built-up areas underestimated, overestimated in other areas
Improvement through optimisation of coefficients to a certain degree possible, but averaging effect cannot be avoided
Land cover data of higher resolution necessary to improve spatial distribution
Lower number of land cover classes necessary