Time Evolution of City Distributions in Germany: Group-Theoretic Spectrum Analysis.

This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. [ABSTRACT FROM AUTHOR]