Visual Analytics Using Heterogeneous Urban Data - Bonadia, Sandro; Gama, Rogério; Oliveira, Daniel de; Miranda, Fabio; Lage, Marcos (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI), 2023)
Several cities contain informal settlements in high-risk areas, particularly mountainous regions. With growing concerns around climate change, these areas are more susceptible to environmental disasters, such as floods and landslides, where topography plays a key role. Fortunately, the availability of data describing complex 2D and 3D aspects of urban regions opens new opportunities to tackle these challenges. This paper aims to integrate these data sets to support human-centered solutions. Based on a collaboration with urban planning professionals, we first contribute a data integration framework that takes data from multiple sources with different mathematical descriptions, dimensions, and resolutions and creates a data-enriched triangle mesh that can be used not only for visualization purposes but also in geometry processing and numerical simulation applications. Second, we contribute a visual analytics system that uses the enriched triangulation to enable stakeholders and domain experts to jointly explore a complex and heterogeneous collection of data sets to identify landfall and flood risk areas. Finally, the usefulness of our approach is highlighted through a case study in collaboration with an urban planner interested in risk areas.