I am a post-doc in geography with a heavy focus on nature based solutions for climate change adaptation particularly in urban areas, climate change impact assessment, multi-criteria risk assessment, natural hazard research—flooding in particular—and quantitative and semi-qualitative analysis of global patterns and processes of urbanization. My methodological background is strongly influenced by data science—use of advanced statistical methods to uncover present relationships and the application of machine learning to tackle methodical challenges.
Over the 20th century, urbanization has substantially shaped the surface of Earth. With population rapidly shifting from rural locations towards the cities, urban areas have dramatically expanded on a global scale and represent crystallization points of social, cultural and economic assets and activities. This trend is estimated to persist for the next decades, and particularly the developing countries are expected to face rapid urban growth. The management of this growth will require good governance strategies and planning. By threatening the livelihoods, assets and health as foundations of human activities, another major global change contributor, climate change, became an equally important concern of stakeholders. Based on the climate trends observed over the 20th century, and a spatially explicit model of urbanization, this paper investigates the impacts of climate change in relation to different stages of development of urban areas, thus evolving a more integrated perspective on both processes. As a result, an integrative measure of climate change trends and impacts is proposed and estimated for urban areas worldwide. We show that those areas facing major urban growth are to a large extent also hotspots of climate change. Since most of these hotspots are located in the Global South, we emphasize the need for stakeholders to co-manage both drivers of global change. The presented integrative perspective is seen as a starting point to foster such co-management, and furthermore as a means to facilitate communication and knowledge exchange on climate change impacts.
A number of concepts exist regarding how urbanization can be described as a process. Understanding this process that affects billions of people and its future development in a spatial manner is imperative to address related issues such as human quality of life. In the focus of spatially explicit studies on urbanization is typically a city, a particular urban region, an agglomeration. However, gaps remain in spatially explicit global models. This paper addresses that issue by examining the spatial dynamics of urban areas over time, for a full coverage of the world. The presented model identifies past, present and potential future hotspots of urbanization as a function of an urban area's spatial variation and age, whose relation could be depicted both as a proxy and as a path of urban development.
Residential choice behaviour is a complex process underpinned by both housing market restrictions and individual preferences, which are partly conscious and partly tacit knowledge. Due to several limitations, common survey methods cannot sufficiently tap into such tacit knowledge. Thus, this paper introduces an advanced knowledge elicitation process called SilverKnETs and combines it with data mining using random forests to elicit and operationalize this type of knowledge. For the application case of the city of Leipzig, Germany, our findings indicate that rent, location and type of housing form the three predictors strongly influencing the decision making in residential choices. Other explanatory variables appear to have a much lower influence. Random forests have proven to be a promising tool for the prediction of residential choices, although the design and scope of the study govern the explanatory power of these models.
Flood risk management must rely on a proper and encompassing flood risk assessment, which possibly reflects the individual characteristics of all elements at risk of being flooded. In addition to prevalent expert knowledge, such an approach must also rely on local knowledge. In this context, stakeholder preferences for risk assessment indicators and assessment deliverables hold great importance but are often neglected. This paper proposes to put this body of information into operation in form of a knowledge base, thereby making it accessible and reusable in multi-criteria risk assessment. Selected use cases discuss the advantages of such a semantically enhanced assessment approach.
In this paper, we present an approach to modelling multicriteria flood vulnerability which integrates the economic, social and ecological dimension of risk and coping capacity. We start with an existing multicriteria risk mapping approach. The term risk is used here in a way that could be called a starting point view, looking at vulnerability without considering coping capacities. We extend this approach by a multicriteria modelling of coping capacities towards an end point view of vulnerability. In doing so, we explore a way to differentiate coping capacity from flood risk in each of the dimensions of vulnerability. The approach is tested in an urban case study, the city of Leipzig, Germany. Our results show that it is possible to map multicriteria risks as well as coping capacities and relate them in a simple way. However, a detailed calculation of end point vulnerability would require more detailed knowledge on the causal relationships between risk and coping capacity criteria and their relative importance.