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Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error
Author(s) -
Groß Marcus,
Rendtel Ulrich,
Schmid Timo,
Schmon Sebastian,
Tzavidis Nikos
Publication year - 2017
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12179
Subject(s) - kernel density estimation , multivariate statistics , rounding , estimation , confidentiality , statistics , geocoding , ethnic group , computer science , econometrics , multivariate kernel density estimation , georeference , density estimation , geography , mathematics , variable kernel density estimation , kernel method , sociology , engineering , cartography , support vector machine , artificial intelligence , computer security , estimator , physical geography , anthropology , systems engineering , operating system
Summary Modern systems of official statistics require the timely estimation of area‐specific densities of subpopulations. Ideally estimates should be based on precise geocoded information, which is not available because of confidentiality constraints. One approach for ensuring confidentiality is by rounding the geoco‐ordinates. We propose multivariate non‐parametric kernel density estimation that reverses the rounding process by using a 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.