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.
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Research Areas:
Visual Computing and Human-Centered Technology: 100%
