Scheidl, A., Almeida Leite, R., & Miksch, S. (2021). VisMiFlow: Visual Analytics to Support Citizen Migration Understanding Over Time and Space. In M. Agus, C. Garth, & A. Kerren (Eds.), EuroVis 2021 - Short Papers (pp. 61–65). The Eurographics Association. https://doi.org/10.2312/evs.20211056
Multivariate networks are complex data structures, which are ubiquitous in many application domains. Driven by a real-world problem, namely the movement behavior of citizens in Vienna, we designed and implemented a Visual Analytics (VA) approach to ease citizen behavior analyses over time and space. We used a dataset of citizens´ movement behavior to, from, or within Vienna from 2007 to 2018, provided by Vienna´s city. To tackle the complexity of time, space, and other moving people´s attributes, we follow a data-user-tasks design approach to support urban developers. We qualitatively evaluated our VA approach with five experts coming from the field of VA and one non-expert. The evaluation illustrated the importance of task-specific visualization and interaction techniques to support users´ decision-making and insights. We elaborate on our findings and suggest potential future works to the field.
Visual Computing and Human-Centered Technology: 100%