Das CLEARING HOUSE-Projekt forscht zu urbanen Wäldern als Form naturbasierter Lösungen, um europäische und chinesische Städte resilienter zu machen.
Green City Lab Hue
Das Projekt Green City Lab Hue hat zum Ziel, eine Vision zur Implementierung naturbasierter Lösungen für die in Zentralvietnam gelegene Stadt gemeinschaftlich zu entwickelt.
The most common approach to assessing natural hazard risk is investigating the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem, i.e. through residential-choice modelling. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the (re-)location of urban dwellers. By modelling residential-choice behaviour in the city of Leipzig, Germany, we seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning, and explore how the proposed methodology may contribute to predicting future trends in exposure, vulnerability, and risk through this analytical focus. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.
Nature-based solutions (NBS) for mitigating climate change are gaining popularity. The number of NBS is increasing, but research gaps still exist at the governance level. The objectives of this paper are (i) to give an overview of the implemented NBS for flood risk management and mitigation in Germany, (ii) to identify governance models that are applied, and (iii) to explore the differences between these models. The results of a hierarchical clustering procedure and a qualitative analysis show that while no one-size-fits-all governance model exists, polycentricism is an important commonality between the projects. The study concludes by highlighting the need for further research on traditional governance model reconversion and paradigm changes. We expect the findings to identify what has worked in the past, as well as what is important for the implementation of NBS for flood risk management in future projects.
Improving Green Space Accessibility (GSA) in public spaces in cities and communities reduces disparities among people and fosters sustainable development. However, traditional mapping approaches in cities neglects green spaces in the hinterland and treats the geographical distance as a fixed quantity. This limits conclusions about spatial inequalities in Green Space Accessibility and influences the evaluation of current policies which seek to ensure a high local recreation quality for all residents irrespective of any administrative boundaries.
This paper aims to detect spatial inequalities in Green Space Accessibility for urban green (UG) and non-urban green (NUG) across Europe, and reveals the role of the rural-urban interface (RUI). The approach taken here calculates Green Space Accessibility across administrative boundaries, which enables distance to be treated as a flexible variable. The results highlight major inequalities between and within regions and countries. However, unequal Green Space Accessibility for urban green is compensated in most countries by more equal one for non-urban green, which is of particular relevance in the rural-urban interface.
The combined perspective on both relative and absolute Green Space Accessibility suggests a new perspective on the rural-urban interface that is critical for equitable green infrastructure planning. This paper concludes that, in order to bridge the urban-rural-divide, monitoring and planning tools that examine the arbitrary use of thresholds and existing administrative boundaries are needed.
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.
MARIO - Manipulation of Spatial and Related Metadata in Online Databases
Das SilverKnETs-Tool SilverKnETs dient der Erhebung von Wissen in Form eines modellbasierten “Spiels”. Solche Wissenserhebungs-Spiele sind geeignete Mittel, um Informationen und Wissen relevanter Akteure in systematischer Form zu erheben und für eine weitere Verarbeitung bereitzustellen.
Risikoanalyse neu gedacht Das Orca-Tool dient der Berechnung, Analyse und Visualisierung des Risikos bezüglich Naturgefahren. Das Tool arbeitet wissensbasiert, d.h., wesentliche für die Berechnung relevante Informationen werden in Form einer adaptierbaren Ontologie bereitgestellt.
Sebastian ist PostDoc in Geographie mit den fachlichen Schwerpunkten Naturbasierte Lösungen im Kontext von Klimawandeladaption und Gesundheit, Naturgefahrenforschung, und mit einem methodischen Background in Data Science. Nebenbei fasziniert ihn die Photographie, und er ist interessiert in Astronomie und Astrophotographie. Und Flugsimulatoren.