<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The transformation of area aggregates between non-hierarchical area systems (administrative areas) is a standard problem in official statistics. For this...

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

Online veröffentlicht: 15. Juni 2020

Seitenbereich: 297 - 314

Eingereicht: 01. März 2019

Akzeptiert: 01. Dez. 2019

DOI: https://doi.org/10.2478/jos-2020-0016

Schlüsselwörter
Choropleth maps, kernel density estimation, statistical reporting, sub-regional estimation, urban development

© 2020 Marcus Groß et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 15. Juni 2020

Seitenbereich: 297 - 314

Eingereicht: 01. März 2019

Akzeptiert: 01. Dez. 2019

DOI: https://doi.org/10.2478/jos-2020-0016

SchlüsselwörterChoropleth maps, kernel density estimation, statistical reporting, sub-regional estimation, urban development

© 2020 Marcus Groß et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Choropleth maps, kernel density estimation, statistical reporting, sub-regional estimation, urban development