  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.
