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Groß, Marcus
Rendtel, Ulrich
Schmid, Timo
Schmon, Sebastian
Tzavidis, Nikos
Year of Publication: 
Series/Report no.: 
Discussion Paper, School of Business & Economics: Economics 2015/7
Modern systems of official statistics require the timely estimation of area-specific densities of sub-populations. Ideally estimates should be based on precise geo-coded information, which is not available due to confidentiality constraints. One approach for ensuring confidentiality is by rounding the geo-coordinates. We propose multivariate non-parametric kernel density estimation that reverses the rounding process by using a Bayesian measurement error model. The methodology is applied to the Berlin register of residents for deriving density estimates of ethnic minorities and aged people. Estimates are used for identifying areas with a need for new advisory centres for migrants and infrastructure for older people.
binned data
ethnic segregation
non-parametric estimation
official statistics
Document Type: 
Working Paper

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