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Switching between different area systems via simulated geocoordinates: A case study for student residents in Berlin

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  • Groß, Marcus
  • Rendtel, Ulrich
  • Schmid, Timo
  • Tzavidis, Nikos

Abstract

The transformation of area aggregates between non-hierarchical area systems is a standard problem of official statistics. We introduce a new method which is based on kernel density estimates. It is a modification of the SEM algorithm proposed by Gross et al. (2016), which was used for the transformation of totals on rectangular areas to kernel densities 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 a new area system. The method is applied to student resident figures from Berlin. These are known only at the level of ZIP codes but they are needed for administrative planning districts. Our method is evaluated on a similar, simulated data set with known exact geo-coordinates. In the empirical part results for changes in the student residential areas between 2005 and 2015 are presented. It is demonstrated that the transformation via kernel density estimates offers additional useful features to display concentration areas.

Suggested Citation

  • Groß, Marcus & Rendtel, Ulrich & Schmid, Timo & Tzavidis, Nikos, 2018. "Switching between different area systems via simulated geocoordinates: A case study for student residents in Berlin," Discussion Papers 2018/2, Free University Berlin, School of Business & Economics.
    • Handle: RePEc:zbw:fubsbe:20182
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    References listed on IDEAS

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    1. Bester, Helmut & Ouyang, Yaofu, 2018. "Optimal procurement of a credence good under limited liability," International Journal of Industrial Organization, Elsevier, vol. 61(C), pages 96-129.
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    Cited by:

    1. 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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    More about this item

    Keywords

    Choropleths; Grid Maps; Kernel Density Estimation; Geo-Coordinates;
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