Nowadays large amounts of movement data is available. This makes it important not only to be aware of how to collect and store this data, but also how to visually represent the information to get insights and “read” the story behind data. When visualising origin-destination data, the traditional flow map is the solution most often selected. A single flow map, however, does not necessarily show all the available attribute variables and also tends too clutter quickly.A more appropriate solution is a dashboard. It provides users with summaries of the represented information. Despite the dashboard suitability to support getting insights, current dashboards have some limitations regarding the flexibility of the layout. To overcome these limitations, we introduce adaptability in dashboards. In our case adaptability ensures that users get insights into the component of interest (space, time, or attribute) on 3 levels of detail. Adaptability is initiated by user tasks to resulting in changes in the visualizations of represented information and dashboard interfaces. We illustrate the concept of an adaptable dashboard with two case studies.
- origin-destination data