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. In this study, information extracted from the first global urban fluvial flood risk data set (Aqueduct) is investigated and visualized to explore current and projected city-level flood impacts driven by urbanization and climate change. We use a novel adaption of the self-organizing map (SOM) method, an artificial neural network proficient at clustering, pattern extraction, and visualization of large, multi-dimensional data sets. Prevalent patterns of current relationships and anticipated changes over time in the nonlinearly-related environmental and social variables are presented, relating urban river flood impacts to socioeconomic development and changing hydrologic conditions. Comparisons are provided between 98 individual cities. Output visualizations compare baseline and changing trends of city-specific exposures of population and property to river flooding, revealing relationships between the cities based on their relative map placements. Cities experiencing high (or low) baseline flood impacts on population and/or property that are expected to improve (or worsen), as a result of anticipated climate change and development, are identified and compared. This paper condenses and conveys large amounts of information through visual communication to accelerate the understanding of relationships between local urban conditions and global processes.

Patterns and comparisons of human-induced changes in river flood impacts in cities