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Switching Between Different Non-Hierachical Administrative Areas via Simulated Geo-Coordinates: A Case Study for Student Residents in Berlin

Author

Listed:
  • Groß Marcus
  • Kreutzmann Ann-Kristin
  • Rendtel Ulrich
  • Schmid Timo

    (Freie Universität Berlin, Garystraße 21, 14195 Berlin, Germany.)

  • Tzavidis Nikos

    (University of Southampton, Murray Building 58, Highfield Campus, Southampton, UK.)

Abstract

The transformation of area aggregates between non-hierarchical area systems (administrative areas) is a standard problem in official statistics. For this problem, we present a proposal which is based on kernel density estimates. The approach applies a modification of a stochastic expectation maximization algorithm, which was proposed in the literature for the transformation of totals on rectangular areas to kernel density estimates. As a by-product of the routine, one obtains simulated geo-coordinates for each unit. With the help of these geo-coordinates, it is possible to calculate case numbers for any area system of interest. The proposed method is evaluated in a design-based simulation based on a close-to-reality, simulated data set with known exact geo-coordinates. In the empirical part, the method is applied to student resident figures from Berlin, Germany. These are known only at the level of ZIP codes, but they are needed for smaller administrative planning districts. Results for (a) student concentration areas and (b) temporal changes in the student residential areas between 2005 and 2015 are presented and discussed.

Suggested Citation

  • Groß Marcus & Kreutzmann Ann-Kristin & Rendtel Ulrich & Schmid Timo & Tzavidis Nikos, 2020. "Switching Between Different Non-Hierachical Administrative Areas via Simulated Geo-Coordinates: A Case Study for Student Residents in Berlin," Journal of Official Statistics, Sciendo, vol. 36(2), pages 297-314, June.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:2:p:297-314:n:1
    DOI: 10.2478/jos-2020-0016
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    References listed on IDEAS

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    2. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2016. "Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 472-487, April.
    3. Marcus Groß & Ulrich Rendtel & Timo Schmid & Sebastian Schmon & Nikos Tzavidis, 2017. "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," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 161-183, January.
    4. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    5. Ulrich Rendtel & Milo Ruhanen, 2018. "Die Konstruktion von Dienstleistungskarten mit Open Data am Beispiel des lokalen Bedarfs an Kinderbetreuung in Berlin [The construction of service maps with open data: the case of local need for ch," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 271-284, December.
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