Derivation of urban mass concentrations from TanDEM-X data for the analysis of polycentric urban structures in the USA and Germany

Kurzfassung

The urban areas around the world are growing and constantly changing. One of the developments that can be observed alongside, especially in the Global North, is the formation of several densification patterns, so-called (sub-)centers, in addition to the traditional center. This phenomenon, known under the scientific term polycentricity, has so far mainly been analysed based on socioeconomic data on employees, population or human activity data, to name a few. These approaches, however, are reaching their limits. Due to data heterogeneity, limited data availability and diverse methodological strategies to identify center structures, comparisons between cities or regions are difficult. Since remote sensing enables to compute the urban morphology on large scale, it has the potential to support the social sciences. Against this background, within this thesis I used remotely sensed data to calculate urban mass concentrations, defined as volume per processing unit, in four German cities and four cities in the USA. The outcome is a morphological 3D characterization of the urban structure. In the scope of this thesis the approach used to generate these urban mass concentrations is applied for the first time to cities in the USA. Originating from the results, in a next step, high urban mass concentrations interpreted as (sub-)centers are identified by applying a threshold method. Based on the identified (sub-)centers a polycentricity analysis is finally carried out to compare the distribution of (sub-)centers within Germany and the USA as well as between Germany and the USA. Concluding, the thesis investigates the phenomenon of polycentricity from a morphological perspective in order to contribute to the understanding of the changes happening in today’s urban environments of the Global North. At the same time, it is a contribution to the further development of polycentricity analyses.