Urbanization and Infrastructures

In most of our research projects in the context of urbanization, we use data generated by Geographic Information Systems (GIS). These data contain information such as the location of various structures (urban areas, deprived urban areas, buildings, …) as well as of infrastructures (streets, supply pipes, …) in internationally recognized standards, including georeferencing. Generally, this data is acquired either locally (e.g. in a census or a survey) or remotely (e.g. by satellites, drones). Local data acquisition is usually very cost and time intensive, while the remote data acquisition methods can deliver large quantities of data with high resolution both in the temporal and spatial dimensions.

Another central aspect of our research is data analysis. We study the probability distributions and statistical moments of various parameters, such as the mean value or the standard deviation of objects (sizes of deprived areas in a specific city). Moreover, we use machine learning methods to develop metrics based on cluster analysis. This helps us to identify correlations in the data (such as which slums can be considered part of a single cluster).

In order to determine optimal design or adaptation of the water supply infrastructure, we use methods of mathematical optimization. Herein, a methodology has been specially developed for engineering problems: Technical Operations Research (TOR) .

The resilience assessment and optimization of urban infrastructures is a crucial aspect of the projects within the research area Urbanization and Infrastructures. We focus on the resilience of water supply infrastructure and the interdependencies with other infrastructures, such as the power network.

In case of a disruption such as component failure, a resilient system still fulfils a minimum required functionality and has the ability to recover. Resilience of technical systems and infrastructures can be assessed and optimized using metrics based on graph theory. These methods use approaches from network theory, analyzing the infrastructure system represented as a mathematical graph.

Other practical examples have been developed within the project of theCollaborative Research Center CRC 805 (Sonderforschungsbereich SFB 805) and are related to sustainable system synthesis and operation . They were concerned primarily with resilient topology design of water supply systems in high-rise buildings. Another important research topic within CRC 805 is the resilient design of dynamic systems that react to disruptions autonomously by adjusting their topology or control strategy.

The main question with regard to urbanization is: how do urban structures develop and why? Urban studies often approach this question by developing ever more complex and demanding models. We have chosen a different approach: we study whether it is possible to reduce the urban development processed on their core mechanisms (such as migration) and describe these analytically with simple mathematical models.

According to the United Nations, the current trends in urbanization and simultaneous global population growth will result in an additional 2.5 billion people living in urban areas, equivalent to 68% of the entire global population. Water supply system is an important component of the infrastructure that does not only provide water to citizens, but also to industry, agriculture and electricity generation systems.

Within a research project funded by the KSB Foundation, machine learning methods are to be developed in order to anticipate water demand in urban areas subject to rapid urbanization. The existing water supply infrastructure will be adjusted in accordance with the predicted demand by mathematical optimization. This adjustment will be performed under the consideration of the cost-benefit analysis and water network resilience metrics. To achieve this, graph-theoretical resilience metrics will be used and extended according to new findings. Moreover, an analysis of various urban structures and their influence on the resilience of their water supply infrastructure will be studied. This should lead to recommendations for guiding urban development of future cities.

The main focus of the LOEWE center emergenCITY are Emergency Responsive Digital Cities and their autonomous operation in the case of an emergency. Resilience of various urban infrastructures is studied in 3 phases of the emergency cycle: reaction, recovery and preparation/prevention from an engineering and socio-political perspective.

At FST, we study resilience of water supply infrastructure and its interdependencies with other infrastructures, such as the power network. Decentral data acquisition through (soft) sensor networks as well as their analysis and distribution by ICT systems is another important aspect of this project.

Around one billion people worldwide live in slums, often without urban infrastructure such as electricity or water. To change this, the number of inhabitants and their development must first be known. To do this, slums in several cities are recorded at different times using satellite data. Then it will be investigated whether their development can be described with simple mathematical models such as those otherwise used to describe physical processes. Such new data-driven models could be used to understand how slums, and especially the needs of the people who live in them, will develop in the future.

The project is supervised by Dr.-Ing. John Friesen and Nicolas Kraff .

The function of technical infrastructure is dependent on human demand behavior. Especially in times of uncertainty and crisis, it is important to know and understand the state and behavior of this socio-technical system. We research how digital twins can be utilized for this challenge. We use physical, data driven and agent-based models for the behavior and interaction of humans and infrastructure.

The project is supervised by Jonathan Sattler. He is a research associate at the Institute for the Protection of Terrestrial Infrastructures of the German Aerospace Center (DLR) and an external doctoral student at FST.