Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities.
Adaptation strategies to climate change need information about present and future climatic conditions. However, next to scenarios about the future climate, scenarios about future vulnerability are essential, since also changing societal conditions fundamentally determine adaptation needs. At the international and national level, first initiatives for developing vulnerability scenarios and so-called shared socioeconomic pathways (SSPs) have been undertaken. Most of these scenarios, however, do not provide sufficient information for local scenarios and local climate risk management. There is an urgent need to develop scenarios for vulnerability at the local scale in order to complement climate change scenarios. Heat stress is seen as a key challenge in cities in the context of climate change and further urban growth. Based on the research project ZURES (ZURES 2020 website), the paper presents a new method for human vulnerability scenarios to heat stress at the very local scale for growing medium-sized cities. In contrast to global models that outline future scenarios mostly with a country-level resolution, we show a new method on how to develop spatially specific scenario information for different districts within cities, starting from the planned urban development and expansion. The method provides a new opportunity to explore how different urban development strategies and housing policies influence future human exposure and vulnerability. Opportunities and constraints of the approach are revealed. Finally, we discuss how these scenarios can inform future urban development and risk management strategies and how these could complement more global or national approaches.
PurposeEnhancing the resilience of cities and strengthening risk-informed decision-making are defined as key within the Global Agenda 2030. Implementing risk-informed decision-making also requires the consideration of scenarios of exposure and vulnerability. Therefore, the paper presents selected scenario approaches and illustrates how such vulnerability scenarios can look like for specific indicators and how they can inform decision-making, particularly in the context of urban planning.Design/methodology/approachThe research study uses the example of heat stress in Ludwigsburg, Germany, and adopts participatory and quantitative forecasting methods to develop scenarios for human vulnerability and exposure to heat stress.FindingsThe paper indicates that considering changes in future vulnerability of people is important to provide an appropriate information base for enhancing urban resilience through risk-informed urban planning. This can help cities to define priority areas for future urban development and to consider the socio-economic and demographic composition in their strategies.Originality/valueThe value of the research study lies in implementing new qualitative and quantitative scenario approaches for human exposure and vulnerability to strengthen risk-informed decision-making.
<p><strong>Abstract.</strong> The overarching nature of building resilience across disciplines and its inherent positive mutual understanding due to the association with the immune system also amongst the non-scientific community, makes it an attractive and increasing popular concept which everybody seems able to grasp its necessity. Hence, there is an exponential increase, even limited down to the key words “urban resilience”, in scientific literature over the last decade. Moreover the concept is also taken up by the New Urban Agenda Habitat III, the SDG goals and the IPCC. Hand in hand with this development the definitions and attempts of operationalization are innumerable. Conjoined, there is a lack of validation of resilience measures, including spatio-temporal aspects but also of the single component of it. Moreover, traditional data sources like census or governmental data miss out on certain important facets making empirical validation impossible and lack the spatio-temporal resolution necessary to cover the characteristics of resilience. Hence, this experimental study explores and develops new spatial indicators through machine learning methods applied to OpenStreetMap data to replicate conventional core indicators. In order to cover all spatial attributes indicators for points, lines and areas are deduced and investigated with supervised and unsupervised algorithms.</p>
<p><strong>Abstract.</strong> While climate change is already a real issue in many parts of the world, it is even more threatening the well-being of future generations. The SDG 1.5 explicitly aims to reduce the vulnerability and exposure to climate related hazards by 2030. TheWorld Risk Index (WRI) is one well-respected approach in profiling countries risk to natural hazard. To effectively monitor development and detect decision points on the climate resilience pathway, data of high resolution in space and time about the world’s countries is of urgent importance. The World Risk Index will guide the supervised learning part resulting in an indicator set derived from OpenStreetMap (OSM) tags, establishing on one hand an open risk index and adding deep explanatory power to its components by a qualitative discussion of the OSM themes. The second part explores with unsupervised algorithms the inherent characteristic of country groups classified by the open risk index and deduces common patterns of socio-economic vulnerability. Hence, the inherent challenge of this work is to substitute existing static indicators with new dynamic indicators, not only substituting them but also painting a more detailed picture. Moreover, new data sources still questioned often by their reliability compared to World Bank or census data, and therefore its opportunities are neglected instead of critically exploring the potential. This unique combination is not done yet and bares huge potential moreover united with the open source geo community to contribute a little piece of the puzzle for achieving the SDG 1.5.</p>
<p><strong>Strategies and evaluation criteria for retention areas in reconstruction processes &#8211; new perspectives and open research questions based on the case study Ahr Valley</strong></p> <p>In summer 2021 parts of Western Germany, especially North-Rhine Westphalia and Rhineland Palatinate was hit severely by devastating floods resulting from&#160; the cyclone "Bernd" with almost stationary and high precipitation of 115 mm rain in 72 h (Kreienkamp et al. 2021). The resulting damage of the flood event was the highest since decades in Germany with a high number of damaged buildings and infrastructure and around 189 fatalities (DKKV 2022). The Ahr-Valley in the federal state of Rhineland-Palatinate was the most severely affected region.</p> <p>After one and a half years, the region and affected municipalities are still in the process of reconstruction and facing enormous challenges. Anyway, in order to better cope with future events the responsible planning authorities strive to develop strategies for flood prevention and flood risk reduction. In this regard, the identification of areas for effective water-retention measures and the designation of open corridors for runoff with reduced damage potential plays a crucial role. Possible measures range from adaptations in land use and cultivation patterns or nature based solutions to technical retention systems. Due to this variety of possible measures, which can have different resource requirements and different legal or financial dependencies, a systematic evaluation of potential measures is necessary.</p> <p>Against this background, this study aims at answering the questions on how to systematize and prioritize measures for potential water-retention areas and lowering flood damage potential while considering constraints and challenges during the reconstruction process after a major flood. In addition to common planning criteria, aspects of land allocation need to be taken into account and different levels of feasibility of a certain measure need to be considered. Therefore, we analyze and systematize the multitude of possible retention measures. Based on this research and on expert discussions with relevant actors in the Ahr-Valley we present and discuss a set of criteria for evaluation.</p> <p>The results of this study can help to inform political decision makers and planning authorities for land use and urban planning and support decisions on allocation of areas. Furthermore, the paper discusses which measures and strategies should be prepared long before a flood disaster for successful preparedness and improvement of flood prevention.</p> <p>&#160;</p> <p><strong>References</strong></p> <p>DKKV (Hrsg.,2022): Die Flutkatastrophe im Juli 2021. Ein Jahr danach: Aufarbeitung und erste Lehren f&#252;r die Zukunft.DKKV-Schriftenreihe Nr. 62, Bonn</p> <p>Kreienkamp F, Philip SY, Tradowsky JS, Kew SF, Lorenz P, Arrighi J, Belleflamme A, Bettmann T, Caluwaerts S, Chan SC (2021) Rapid attribution of heavy rainfall events leading to the severe flooding in Western Europe during July 2021</p>
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