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, 15-16 Mar 2007

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

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

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

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

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

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

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

Input datasets National population grids Datasets from 5 countries

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

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

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

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

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

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

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

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

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

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

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

Comparison by land cover class

Comparison by land cover class Results by country

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