As it has been frequently stated, it is becoming increasingly hard to grasp and understand the urban structures of today through the lens of conventional spatial and temporal scales. Due to dynamic interactions and complex interdependencies in between each of their spatial, social, infrastructural and economic dimensions, cities are expanding way beyond traditional - such as municipal, regional or even national – barriers and boundaries. Nevertheless, comprehensive methods and innovative tools that are able to tackle and manage the complex nature of contemporary urban challenges are still relatively few. Amongst others, these must provide assistance in setting priorities for urban design and planning actions and in optimizing and foreseeing their possible effects on an extensive timescale. Against this background, the author proposes a comprehensive seven-step methodology for multiscale urban analysis and modeling, with a special focus on their effective integration: she believes that for accuracy, credibility and real-world-applicability purposes, spatial models must rest on a thorough investigation of existing space-time patterns and processes. The analysis part first identifies the most important regional trends and their shaping factors for the case study, the Ruhr area in Germany. Secondly, it investigates the region’s prevailing demographic, socioeconomic and economic profiles and derives their characteristic scales. Moving forward, the thesis demonstrates how - with a machine-learning mechanism called self-organizing maps - one may be able to tremendously reduce the complexity of latter empirical observations and therefore effectively infiltrate them into modeling attempts. Subsequently, the work presents the first prototype of a multiscale urban model, which simultaneously unfolds the subsystems of popula- tion, residential migration, employment migration, land price, land use and accessibility. In more detail, the residential and economic dynamics are captured by the well-known master equation approach from statistical physics [G. Haag and W. Weidlich, Geographical Analysis, 16: 331-357, (1984)] whereas land price is modeled by a novel method whose emphasis rests on the analogy between spatial rent price fluctuations and velocity fluctuations in turbulent flows. Latter approach may be extremely fruitful for two reasons: First, turbulent flows, similarly to urban systems, are open systems far from equilibrium and they inherently involve “transport processes in scale” thus ensuring scale-to-scale interactions. Secondly, the method includes and puts a strong emphasis on the so-called non-self-similar nature of (urban and turbulent) environments, which implies an increased probability for the occurrence of very strong fluctuations (e.g. that of land use or land price) at small scales. Finally, with the help of the story-and-simulation (SAS) approach, the author runs simulations under three scenarios: “Healthy City”, “Smart City” and “Deurbanization” and reviews how the joint results of analysis and modeling steps may form the recommendation-basis for both spatial (e.g. urban design and planning measures) and non-spatial (e.g. governance) strategies. The main aim thereby is to inform and instigate meaningful cooperation and synchronized action between urban stakeholders and decision-makers on different scales, which is perhaps even more pressing and challenging for large-scale polycentric regions such as the Ruhr Area in Germany.