Analysing spatio-temporal migration structures with gridded population data in Copenhagen, Denmark

Accurate and consistent regularised datasets play a fundamental role when analysing and explaining temporal variations in the spatial patterns of ethnic diversity and socio-spatial segregation in urban centres. These patterns are mainly identified at the local level where population composition and densities are explicitly mapped along with uninhabited areas such as parks. In contrast to analyses of aggregated (e.g., administrative) zones where the size and the shape of spatial units are inconsistent, gridded datasets provide more efficient means for comprehensive analysis of population structures over time. This paper investigates the temporal changes of the population distribution by various demographic characteristics (e.g. age, migration background) utilising fine-grained (100m by 100m) gridded data for the capital region of Copenhagen, Denmark. These data enable insight into micro-level variations in the urban fabric associating migration with other demographic and topographic variables. Examining thirty years of change over four time points, we attempt to relate and explain the distribution of migrants in tandem with the educational attainment or income status of the population, the diaspora effect, real estate prices, and the development of urban plans. The study incorporates a wide-ranging set of tools and methods to explore the varying geographies of ethnic diversity in Copenhagen along with patterns of spatial clustering in and out of the Danish ’parallel societies’ (i.e. compositional data techniques, bivariate visualisations, measures of residential diversity and segregation). By concentrating on Copenhagen as a case study, we examine the potentials of gridded data at high resolution for analysing spatial variability in population composition and report on whether the alleged problematic areas show issues of socio-spatial segregation.