Scaling of Variability Measures in Hierarchical Demographic Data

Demographic heterogeneity is often studied through the geographical lens. Therefore it is considered at a predetermined spatial resolution, which is a suitable choice to understand scale-full phenomena. Spatial autocorrelation indices are well established for this purpose. Yet complex systems are often scale-free, and thus studying the scaling behavior of demographic heterogeneity may provide valuable insights. Furthermore, migration processes are not necessarily influenced by the physical landscape, which is accounted for by the spatial autocorrelation indices. The migration process may be more influenced by the socio-economic landscape, which is better reflected by the hierarchical demographic data. Here we explore the scaling behavior of variability measures in the United Kingdom 2011 census data set. As expected, all of the considered variability measures decrease as the hierarchical scale becomes coarser. Though the non–monotonicity is observed, it can be explained by accounting for the imperfect hierarchical relationships. We show that the scaling behavior of variability measures can be qualitatively understood in terms of Schelling’s segregation model and Kawasaki–Ising model.