Migration studies with a Compositional Data approach: a case study of population structure in the Capital Region of Denmark

TL;DR

This study applies Compositional Data analysis to population data in Denmark's Capital Region, revealing spatial segregation patterns and their relation to housing prices, demonstrating the robustness of CoDa methods over standard techniques.

Contribution

It introduces the use of Compositional Data techniques in Population Geography, highlighting their advantages over traditional methods for analyzing constrained population data.

Findings

Identified spatial population segregation in the Capital Region of Denmark.

Revealed patterns linking population composition to housing prices.

Demonstrated the robustness of CoDa methods in population analysis.

Abstract

Data normalization for removing the influence of population density in Population Geography is a common procedure that may come with an unperceived risk. In this regard, data are constrained to a constant sum and they are therefore not independent observations, a fundamental requirement for applying standard multivariate statistical tools. Compositional Data (CoDa) techniques were developed to solve the issues that the standard statistical tools have with close data (i.e., spurious correlations, predictions outside the range, and sub-compositional incoherence) but they are still not commonly used in the field. Hence, we present in this article a case study where we analyse at parish level the spatial distribution of Danes, Western migrants and non-Western migrants in the Capital region of Denmark. By applying CoDa techniques, we have been able to identify the spatial population segregation in the area and we have recognized some patterns that can be used for interpreting housing prices variations. Our exercise is a basic example of the potential of CoDa techniques, which generate more robust and reliable results than standard statistical procedures, but it can be generalized to other population datasets with more complex structures.