ALBERT

Description: Rural areas are highly vulnerable to floods due to limited social, economic, and physical resources. Understanding rural vulnerability is vital for developing effective disaster risk reduction strategies. Even though rural areas and cities are intrinsically linked, rural vulnerability was assessed without considering its relation to cities. Numerous theoretical frameworks on systemizing and assessing vulnerability were developed with varying level of scope and depth in terms of scale, dimensions, and components. Nevertheless, these frameworks did not explicitly mention the impact of flood or other hazards on the linkages between spatial units i.e., rural and urban. This study aims to understand and conceptualize the rural vulnerability with respect to the dynamics of rural–urban linkages in the case of flood events. To do so, current literature on rural–urban linkages, vulnerability, as well as factors that influence them were critically reviewed. Taking into account the main elements of rural–urban linkages (flow of people, information, finances, goods and services), components of vulnerability (exposure, susceptibility, and capacity), and factors (social, economic, institutional, infrastructural, spatial, and environmental), a unified framework is proposed. The framework underscores that the role of rural–urban linkages is essential to fully understand rural flood vulnerability. Moreover, the framework highlights the role of spatial factors—city size and proximity to the city—as crucial to comprehend rural vulnerability. This framework can be used as a tool for understanding multifaceted rural vulnerability for climate change adaptation and disaster risk reduction considering spatial development perspective. In this context, empirical investigations can be made to validate the proposed framework and policies can be introduced accordingly. Overall, the proposed framework can help recognize concepts and links of vulnerability, rural–urban dependencies, and rural development dynamics.

Description: 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.

Description: In the face of accelerating climate change, urbanization and the need to adapt to these changes, the concept of resilience as an interdisciplinary and positive approach has gained increasing attention over the last decade. However, measuring resilience and monitoring adaptation efforts have received only limited attention from science and practice so far. Thus, this paper aims to provide an indicator set to measure urban climate resilience and monitor adaptation activities. In order to develop this indicator set, a four-step mixed method approach was implemented: (1) based on a literature review, relevant resilience indicators were selected, (2) researchers, consultants and city representatives were then invited to evaluate those indicators in an online survey before the remaining indicator candidates were validated in a workshop (3) and finally reviewed by sector experts (4). This thorough process resulted in 24 indicators distributed over 24 action fields based on secondary data. The participatory approach allowed the research team to take into account the complexity and interdisciplinarity nature of the topic, as well as place- and context-specific parameters. However, it also showed that in order to conduct a holistic assessment of urban climate resilience, a purely quantitative, indicator-based approach is not sufficient, and additional qualitative information is needed.