Exploring networks over time and space utilizing visual analytics

The digitization of our world provides us with a vast amount of data. This data allows us to construct accurate models of real world situations which are explored and analyzed to get a deeper understanding and eventually draw conclusions for our further actions. Multivariate networks are a particularly complex construct which are ubiquitous in many different subject areas, like social media, telecommunication, transport, finance, and demographics. These networks often have a spatial context attached to them and usually evolve over time. This fact makes it even harder to efficiently visualize the many aspects of such a network. This thesis aims to define and build a visualization of a multivariate network which changes over time and space. The underlying data network is composed of real-world movement data of citizens of Vienna from 2007 to 2018, provided by the city of Vienna, MA23. This data represents the change of residencies of people moving to, from, or within Vienna. To tackle the complexity of the many dimensions of this data such as time, space, and other attributes, like the country of birth of the moving people, we follow a user-centered design approach proposed by Miksch et al. The implemented prototype of the visualization focuses on two different user groups, which are people from the department for urban development on the one hand and the public on the other hand. Both groups may take interest in the relations between the districts and in understanding the migration flow over the years. In the design process, we focus on strengths and weaknesses of different visualization techniques to amplify the visual expressiveness of the key aspects of the data. Spatial information is encoded in a geographic map on which flows depict movements between areas. The design choices of these flows are essential to sustain readability. The temporal aspects are depicted with different time-series visualizations. Each of them focuses on the data from a different angle. Interactivity and interoperability between these visualizations ensure determined navigation through the various aspects of the migration data. We evaluated the visualization prototype with five experts in the field of Visual Analytics and one non-expert. The evaluation showed that the right combination of different visualization and interaction techniques results in an effective and appropriate visualization from which users can draw the desired insight.